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Egger J, Gsaxner C, Luijten G, Chen J, Chen X, Bian J, Kleesiek J, Puladi B. Is the Apple Vision Pro the Ultimate Display? A First Perspective and Survey on Entering the Wonderland of Precision Medicine. JMIR Serious Games 2024; 12:e52785. [PMID: 39292499 DOI: 10.2196/52785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/26/2024] [Accepted: 07/02/2024] [Indexed: 09/19/2024] Open
Abstract
At the Worldwide Developers Conference in June 2023, Apple introduced the Vision Pro. The Apple Vision Pro (AVP) is a mixed reality headset; more specifically, it is a virtual reality device with an additional video see-through capability. The video see-through capability turns the AVP into an augmented reality (AR) device. The AR feature is enabled by streaming the real world via cameras on the (virtual reality) screens in front of the user's eyes. This is, of course, not unique and is similar to other devices, such as the Varjo XR-3 (Varjo Technologies Oy). Nevertheless, the AVP has some interesting features, such as an inside-out screen that can show the headset wearer's eyes to "outsiders," and a button on the top, called the "digital crown," that allows a seamless blend of digital content with the user's physical space by turning it. In addition, it is untethered, except for the cable to the battery, which makes the headset more agile, compared to the Varjo XR-3. This could actually come closer to "The Ultimate Display," which Ivan Sutherland had already sketched in 1965. After a great response from the media and social networks to the release, we were able to test and review the new AVP ourselves in March 2024. Including an expert survey with 13 of our colleagues after testing the AVP in our institute, this Viewpoint explores whether the AVP can overcome clinical challenges that AR especially still faces in the medical domain; we also go beyond this and discuss whether the AVP could support clinicians in essential tasks to allow them to spend more time with their patients.
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Affiliation(s)
- Jan Egger
- Institute for Artificial Intelligence in Medicine, Essen University Hospital (AöR), Essen, Germany
- Center for Virtual and Extended Reality in Medicine (ZvRM), Essen University Hospital (AöR), Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Essen, Germany
| | - Christina Gsaxner
- Institute for Artificial Intelligence in Medicine, Essen University Hospital (AöR), Essen, Germany
- Department of Oral and Maxillofacial Surgery & Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany
- Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria
| | - Gijs Luijten
- Institute for Artificial Intelligence in Medicine, Essen University Hospital (AöR), Essen, Germany
- Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria
| | - Jianxu Chen
- Leibniz-Institut für Analytische Wissenschaften (ISAS), Dortmund, Germany
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotic, Shanghai Jiao Tong University, Shanghai, China
| | - Jiang Bian
- Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine, Essen University Hospital (AöR), Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), University Medicine Essen (AöR), Essen, Germany
- German Cancer Consortium (DKTK), Partner Site Essen, Essen, Germany
- Department of Physics, TU Dortmund University, Dortmund, Germany
| | - Behrus Puladi
- Department of Oral and Maxillofacial Surgery & Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany
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Kral J, Hradis M, Buzga M, Kunovsky L. Exploring the benefits and challenges of AI-driven large language models in gastroenterology: Think out of the box. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2024. [PMID: 39234774 DOI: 10.5507/bp.2024.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024] Open
Abstract
Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and decision-making support. The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.
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Affiliation(s)
- Jan Kral
- Department of Internal Medicine, University Hospital Motol and Second Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Hepatogastroenterology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Michal Hradis
- MAIA LABS s.r.o., Brno, Czech Republic
- Faculty of Information Technology, University of Technology, Brno, Czech Republic
| | - Marek Buzga
- Department of Physiology and Pathophysiology, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
- Institute of Laboratory Medicine, University Hospital Ostrava, Ostrava, Czech Republic
| | - Lumir Kunovsky
- 2nd Department of Internal Medicine - Gastroenterology and Geriatrics, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
- Department of Surgery, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Gastroenterology and Digestive Endoscopy, Masaryk Memorial Cancer Institute, Brno, Czech Republic
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Tüdös Z, Veverková L, Baxa J, Hartmann I, Čtvrtlík F. The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma. Best Pract Res Clin Endocrinol Metab 2024:101923. [PMID: 39227277 DOI: 10.1016/j.beem.2024.101923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.
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Affiliation(s)
- Zbyněk Tüdös
- Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Lucia Veverková
- Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Jan Baxa
- Department of Imaging Methods, Faculty Hospital Pilsen and Faculty of Medicine in Pilsen, Charles University, Czech Republic
| | - Igor Hartmann
- Department of Urology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Filip Čtvrtlík
- Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic.
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Zhao J, Liu J, Wang S, Zhang P, Yu W, Yang C, Zhang Y, Chen Y. PIAA: Pre-imaging all-round assistant for digital radiography. Technol Health Care 2024:THC240639. [PMID: 39240596 DOI: 10.3233/thc-240639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
BACKGROUND In radiography procedures, radiographers' suboptimal positioning and exposure parameter settings may necessitate image retakes, subjecting patients to unnecessary ionizing radiation exposure. Reducing retakes is crucial to minimize patient X-ray exposure and conserve medical resources. OBJECTIVE We propose a Digital Radiography (DR) Pre-imaging All-round Assistant (PIAA) that leverages Artificial Intelligence (AI) technology to enhance traditional DR. METHODS PIAA consists of an RGB-Depth (RGB-D) multi-camera array, an embedded computing platform, and multiple software components. It features an Adaptive RGB-D Image Acquisition (ARDIA) module that automatically selects the appropriate RGB camera based on the distance between the cameras and patients. It includes a 2.5D Selective Skeletal Keypoints Estimation (2.5D-SSKE) module that fuses depth information with 2D keypoints to estimate the pose of target body parts. Thirdly, it also uses a Domain expertise (DE) embedded Full-body Exposure Parameter Estimation (DFEPE) module that combines 2.5D-SSKE and DE to accurately estimate parameters for full-body DR views. RESULTS Optimizes DR workflow, significantly enhancing operational efficiency. The average time required for positioning patients and preparing exposure parameters was reduced from 73 seconds to 8 seconds. CONCLUSIONS PIAA shows significant promise for extension to full-body examinations.
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Affiliation(s)
- Jie Zhao
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
- Careray Digital Medical Technology Co., Ltd., Suzhou, China
| | - Jianqiang Liu
- Careray Digital Medical Technology Co., Ltd., Suzhou, China
| | - Shijie Wang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Pinzheng Zhang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Wenxue Yu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Chunfeng Yang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Yudong Zhang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Yang Chen
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
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Tran L, Kandel H, Sari D, Chiu CH, Watson SL. Artificial Intelligence and Ophthalmic Clinical Registries. Am J Ophthalmol 2024; 268:263-274. [PMID: 39111520 DOI: 10.1016/j.ajo.2024.07.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 09/03/2024]
Abstract
PURPOSE The recent advances in artificial intelligence (AI) represent a promising solution to increasing clinical demand and ever limited health resources. Whilst powerful, AI models require vast amounts of representative training data to output meaningful predictions in the clinical environment. Clinical registries represent a promising source of large volume real-world data which could be used to train more accurate and widely applicable AI models. This review aims to provide an overview of the current applications of AI to ophthalmic clinical registry data. DESIGN AND METHODS A systematic search of EMBASE, Medline, PubMed, Scopus and Web of Science for primary research articles that applied AI to ophthalmic clinical registry data was conducted in July 2024. RESULTS Twenty-three primary research articles applying AI to ophthalmic clinic registries (n = 14) were found. Registries were primarily defined by the condition captured and the most common conditions where AI was applied were glaucoma (n = 3) and neovascular age-related macular degeneration (n = 3). Tabular clinical data was the most common form of input into AI algorithms and outputs were primarily classifiers (n = 8, 40%) and risk quantifier models (n = 7, 35%). The AI algorithms applied were almost exclusively supervised conventional machine learning models (n = 39, 85%) such as decision tree classifiers and logistic regression, with only 7 applications of deep learning or natural language processing algorithms. Significant heterogeneity was found with regards to model validation methodology and measures of performance. CONCLUSIONS Limited applications of deep learning algorithms to clinical registry data have been reported. The lack of standardized validation methodology and heterogeneity of performance outcome reporting suggests that the application of AI to clinical registries is still in its infancy constrained by the poor accessibility of registry data and reflecting the need for a standardization of methodology and greater involvement of domain experts in the future development of clinically deployable AI.
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Affiliation(s)
- Luke Tran
- From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia.
| | - Himal Kandel
- From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia
| | - Daliya Sari
- From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia
| | - Christopher Hy Chiu
- From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia
| | - Stephanie L Watson
- From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia
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Tanaka K, Kato K, Nonaka N, Seita J. Efficient HLA imputation from sequential SNPs data by transformer. J Hum Genet 2024:10.1038/s10038-024-01278-x. [PMID: 39095607 DOI: 10.1038/s10038-024-01278-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024]
Abstract
Human leukocyte antigen (HLA) genes are associated with a variety of diseases, yet the direct typing of HLA alleles is both time-consuming and costly. Consequently, various imputation methods leveraging sequential single nucleotide polymorphisms (SNPs) data have been proposed, employing either statistical or deep learning models, such as the convolutional neural network (CNN)-based model, DEEP*HLA. However, these methods exhibit limited imputation efficiency for infrequent alleles and necessitate a large size of reference dataset. In this context, we have developed a Transformer-based model to HLA allele imputation, named "HLA Reliable IMpuatioN by Transformer (HLARIMNT)" designed to exploit the sequential nature of SNPs data. We evaluated HLARIMNT's performance using two distinct reference panels; Pan-Asian reference panel (n = 530) and Type 1 Diabetes genetics Consortium (T1DGC) reference panel (n = 5225), alongside a combined panel (n = 1060). HLARIMNT demonstrated superior accuracy to DEEP*HLA across several indices, particularly for infrequent alleles. Furthermore, we explored the impact of varying training data sizes on imputation accuracy, finding that HLARIMNT consistently outperformed across all data size. These findings suggest that Transformer-based models can efficiently impute not only HLA types but potentially other gene types from sequential SNPs data.
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Affiliation(s)
- Kaho Tanaka
- Faculty of Engineering, Kyoto University, Kyoto, Japan
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan
| | - Kosuke Kato
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan
| | - Naoki Nonaka
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan
| | - Jun Seita
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, Japan.
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Koyama H. Machine learning application in otology. Auris Nasus Larynx 2024; 51:666-673. [PMID: 38704894 DOI: 10.1016/j.anl.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/13/2024] [Accepted: 04/02/2024] [Indexed: 05/07/2024]
Abstract
This review presents a comprehensive history of Artificial Intelligence (AI) in the context of the revolutionary application of machine learning (ML) to medical research and clinical utilization, particularly for the benefit of researchers interested in the application of ML in otology. To this end, we discuss the key components of ML-input, output, and algorithms. In particular, some representation algorithms commonly used in medical research are discussed. Subsequently, we review ML applications in otology research, including diagnosis, influential identification, and surgical outcome prediction. In the context of surgical outcome prediction, specific surgical treatments, including cochlear implantation, active middle ear implantation, tympanoplasty, and vestibular schwannoma resection, are considered. Finally, we highlight the obstacles and challenges that need to be overcome in future research.
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Affiliation(s)
- Hajime Koyama
- Department of Otorhinolaryngology and Head and Neck Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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Jia PF, Li YR, Wang LY, Lu XR, Guo X. Radiomics in esophagogastric junction cancer: A scoping review of current status and advances. Eur J Radiol 2024; 177:111577. [PMID: 38905802 DOI: 10.1016/j.ejrad.2024.111577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 06/03/2024] [Accepted: 06/14/2024] [Indexed: 06/23/2024]
Abstract
PURPOSE This scoping review aimed to understand the advances in radiomics in esophagogastric junction (EGJ) cancer and assess the current status of radiomics in EGJ cancer. METHODS We conducted systematic searches of PubMed, Embase, and Web of Science databases from January 18, 2012, to January 15, 2023, to identify radiomics articles related to EGJ cancer. Two researchers independently screened the literature, extracted data, and assessed the quality of the studies using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS) tool, respectively. RESULTS A total of 120 articles were retrieved from the three databases, and after screening, only six papers met the inclusion criteria. These studies investigated the role of radiomics in differentiating adenocarcinoma from squamous carcinoma, diagnosing T-stage, evaluating HER2 overexpression, predicting response to neoadjuvant therapy, and prognosis in EGJ cancer. The median score percentage of RQS was 34.7% (range from 22.2% to 38.9%). The median score percentage of METRICS was 71.2% (range from 58.2% to 84.9%). CONCLUSION Although there is a considerable difference between the RQS and METRICS scores of the included literature, we believe that the research value of radiomics in EGJ cancer has been revealed. In the future, while actively exploring more diagnostic, prognostic, and biological correlation studies in EGJ cancer, greater emphasis should be placed on the standardization and clinical application of radiomics.
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Affiliation(s)
- Ping-Fan Jia
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Yu-Ru Li
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Lu-Yao Wang
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Xiao-Rui Lu
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Xing Guo
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China.
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Lee JW, Woo D, Kim KO, Kim ES, Kim SK, Lee HS, Kang B, Lee YJ, Kim J, Jang BI, Kim EY, Jo HH, Chung YJ, Ryu H, Park SK, Park DI, Yu H, Jeong S. Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis. Am J Gastroenterol 2024:00000434-990000000-01273. [PMID: 39051648 DOI: 10.14309/ajg.0000000000002978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
INTRODUCTION Stool characteristics may change depending on the endoscopic activity of ulcerative colitis (UC). We developed a deep learning model using stool photographs of patients with UC (DLSUC) to predict endoscopic mucosal inflammation. METHODS This was a prospective multicenter study conducted in 6 tertiary referral hospitals. Patients scheduled to undergo endoscopy for mucosal inflammation monitoring were asked to take photographs of their stool using smartphones within 1 week before the day of endoscopy. DLSUC was developed using 2,161 stool pictures from 306 patients and tested on 1,047 stool images from 126 patients. The UC endoscopic index of severity was used to define endoscopic activity. The performance of DLSUC in endoscopic activity prediction was compared with that of fecal calprotectin (Fcal). RESULTS The area under the receiver operating characteristic curve (AUC) of DLSUC for predicting endoscopic activity was 0.801 (95% confidence interval [CI] 0.717-0.873), which was not statistically different from the AUC of Fcal (0.837 [95% CI, 0.767-0.899, DeLong P = 0.458]). When rectal-sparing cases (23/126, 18.2%) were excluded, the AUC of DLSUC increased to 0.849 (95% CI, 0.760-0.919). The accuracy, sensitivity, and specificity of DLSUC in predicting endoscopic activity were 0.746, 0.662, and 0.877 in all patients and 0.845, 0.745, and 0.958 in patients without rectal sparing, respectively. Active patients classified by DLSUC were more likely to experience disease relapse during a median 8-month follow-up (log-rank test, P = 0.002). DISCUSSION DLSUC demonstrated a good discriminating power similar to that of Fcal in predicting endoscopic activity with improved accuracy in patients without rectal sparing. This study implies that stool photographs are a useful monitoring tool for typical UC.
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Affiliation(s)
- Jung Won Lee
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Dongwon Woo
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea
| | - Kyeong Ok Kim
- Division of Gastroenterology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Eun Soo Kim
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Sung Kook Kim
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Hyun Seok Lee
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Ben Kang
- Department of Pediatrics, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Yoo Jin Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea
| | - Jeongseok Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Byung Ik Jang
- Division of Gastroenterology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Eun Young Kim
- Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea
| | - Hyeong Ho Jo
- Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea
| | - Yun Jin Chung
- Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Hanjun Ryu
- Department of Internal Medicine, Daegu Fatima Hospital, Daegu, Korea
| | - Soo-Kyung Park
- Division of Gastroenterology, Department of Internal Medicine and Inflammatory Bowel Disease Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul
| | - Dong-Il Park
- Division of Gastroenterology, Department of Internal Medicine and Inflammatory Bowel Disease Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul
| | - Hosang Yu
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea
| | - Sungmoon Jeong
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, Korea
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Korea
- AICU Corp., Daegu, South Korea
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Bellmann L, Wiederhold AJ, Trübe L, Twerenbold R, Ückert F, Gottfried K. Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data. JMIR Med Inform 2024; 12:e49865. [PMID: 39046780 PMCID: PMC11306949 DOI: 10.2196/49865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/11/2023] [Accepted: 05/04/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition, robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method tailored to the needs of physicians must meet all the abovementioned criteria. OBJECTIVE This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge, adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of disease and control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns associated with disease and reveal relations between attributes within the data set. METHODS We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j, Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the System Usability Scale, individual questions, and user tasks. RESULTS We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with a general cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure and dashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants with cardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful task completion of 81.4% were reported. CONCLUSIONS The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a user test, and the validity of the statistical results was confirmed by associations known from literature and standard statistical inference.
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Affiliation(s)
- Louis Bellmann
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Leona Trübe
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Raphael Twerenbold
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK) Partner Site Hamburg-Kiel-Lübeck, Hamburg, Germany
- University Center of Cardiovascular Science, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Frank Ückert
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karl Gottfried
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Allam AH, Eltewacy NK, Alabdallat YJ, Owais TA, Salman S, Ebada MA. Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study. Eur Radiol 2024; 34:1-14. [PMID: 38150076 PMCID: PMC11213794 DOI: 10.1007/s00330-023-10509-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 09/26/2023] [Accepted: 11/02/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES We aimed to assess undergraduate medical students' knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine. METHODS A multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses. RESULTS Of the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies. CONCLUSIONS Arab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum. CLINICAL RELEVANCE STATEMENT This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula. KEY POINTS • Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology. • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea. • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.
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Affiliation(s)
- Ahmed Hafez Allam
- Faculty of Medicine, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
- Eltewacy Arab Research Group, Cairo, Egypt.
| | - Nael Kamel Eltewacy
- Eltewacy Arab Research Group, Cairo, Egypt
- Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt
| | - Yasmeen Jamal Alabdallat
- Eltewacy Arab Research Group, Cairo, Egypt
- Faculty of Medicine, Hashemite University, Zarqa, Jordan
| | - Tarek A Owais
- Eltewacy Arab Research Group, Cairo, Egypt
- Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt
| | - Saif Salman
- Eltewacy Arab Research Group, Cairo, Egypt
- Mayo Clinic College of Medicine, Jacksonville, FL, USA
| | - Mahmoud A Ebada
- Eltewacy Arab Research Group, Cairo, Egypt
- Faculty of Medicine, Zagazig University, Zagazig, El-Sharkia, Egypt
- Egyptian Fellowship of Neurology, Nasr City Hospital for Health Insurance, Nasr City, Cairo, Egypt
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12
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Xing Y, Lin X. Challenges and advances in the management of inflammation in atherosclerosis. J Adv Res 2024:S2090-1232(24)00253-4. [PMID: 38909884 DOI: 10.1016/j.jare.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/14/2024] [Accepted: 06/15/2024] [Indexed: 06/25/2024] Open
Abstract
INTRODUCTION Atherosclerosis, traditionally considered a lipid-related disease, is now understood as a chronic inflammatory condition with significant global health implications. OBJECTIVES This review aims to delve into the complex interactions among immune cells, cytokines, and the inflammatory cascade in atherosclerosis, shedding light on how these elements influence both the initiation and progression of the disease. METHODS This review draws on recent clinical research to elucidate the roles of key immune cells, macrophages, T cells, endothelial cells, and clonal hematopoiesis in atherosclerosis development. It focuses on how these cells and process contribute to disease initiation and progression, particularly through inflammation-driven processes that lead to plaque formation and stabilization. Macrophages ingest oxidized low-density lipoprotein (oxLDL), which partially converts to high-density lipoprotein (HDL) or accumulates as lipid droplets, forming foam cells crucial for plaque stability. Additionally, macrophages exhibit diverse phenotypes within plaques, with pro-inflammatory types predominating and others specializing in debris clearance at rupture sites. The involvement of CD4+ T and CD8+ T cells in these processes promotes inflammatory macrophage states, suppresses vascular smooth muscle cell proliferation, and enhances plaque instability. RESULTS The nuanced roles of macrophages, T cells, and the related immune cells within the atherosclerotic microenvironment are explored, revealing insights into the cellular and molecular pathways that fuel inflammation. This review also addresses recent advancements in imaging and biomarker technology that enhance our understanding of disease progression. Moreover, it points out the limitations of current treatment and highlights the potential of emerging anti-inflammatory strategies, including clinical trials for agents such as p38MAPK, tumor necrosis factor α (TNF-α), and IL-1β, their preliminary outcomes, and the promising effects of canakinumab, colchicine, and IL-6R antagonists. CONCLUSION This review explores cutting-edge anti-inflammatory interventions, their potential efficacy in preventing and alleviating atherosclerosis, and the role of nanotechnology in delivering drugs more effectively and safely.
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Affiliation(s)
- Yiming Xing
- Cardiology Department, The First Affiliated Hospital of Anhui Medical University, Hefei City, Anhui Province, 230022, China
| | - Xianhe Lin
- Cardiology Department, The First Affiliated Hospital of Anhui Medical University, Hefei City, Anhui Province, 230022, China.
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Papachristou P, Söderholm M, Pallon J, Taloyan M, Polesie S, Paoli J, Anderson CD, Falk M. Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. Br J Dermatol 2024; 191:125-133. [PMID: 38234043 DOI: 10.1093/bjd/ljae021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/12/2024] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
BACKGROUND Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics. OBJECTIVES To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. METHODS This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data. RESULTS In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. CONCLUSIONS The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.
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Affiliation(s)
- Panagiotis Papachristou
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - My Söderholm
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Ekholmen Primary Healthcare Centre, Region Östergötland, Linköping, Sweden
| | - Jon Pallon
- Department of Clinical Sciences in Malmö, Family Medicine, Lund University, Malmö, Sweden
- Department of Research and Development, Region Kronoberg, Växjö, Sweden
| | - Marina Taloyan
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Atrium Healthcare Centre, Region Stockholm, Sweden
| | - Sam Polesie
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - John Paoli
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Chris D Anderson
- Department of Biomedical and Clinical Sciences, Division of Dermatology and Venereology, Linköping University, Linköping, Sweden
| | - Magnus Falk
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Region Östergötland, Kärna Primary Healthcare Centre, Linköping, Sweden
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14
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Chen Y, Lin F, Wang K, Chen F, Wang R, Lai M, Chen C, Wang R. Development of a predictive model for 1-year postoperative recovery in patients with lumbar disk herniation based on deep learning and machine learning. Front Neurol 2024; 15:1255780. [PMID: 38919973 PMCID: PMC11197993 DOI: 10.3389/fneur.2024.1255780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 05/23/2024] [Indexed: 06/27/2024] Open
Abstract
Background The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation. Methods The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others. Results The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance. Conclusion Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.
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Affiliation(s)
- Yan Chen
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Fabin Lin
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Kaifeng Wang
- Fujian Medical University, Fuzhou, Fujian, China
| | - Feng Chen
- Fujian Medical University, Fuzhou, Fujian, China
| | - Ruxian Wang
- Fujian Medical University, Fuzhou, Fujian, China
| | - Minyun Lai
- Fujian Medical University, Fuzhou, Fujian, China
| | - Chunmei Chen
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Rui Wang
- Pingtan Comprehensive Experimentation Area Hospital, Pingtan, China
- Fujian Medical University Union Hospital, Fuzhou, Fujian, China
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15
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Cheng CT, Lin HH, Hsu CP, Chen HW, Huang JF, Hsieh CH, Fu CY, Chung IF, Liao CH. Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1113-1123. [PMID: 38366294 PMCID: PMC11169164 DOI: 10.1007/s10278-024-01038-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024]
Abstract
Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on the use of DLMs specifically for trauma image evaluation. In this study, we developed a DLM aimed at detecting solid organ injuries to assist medical professionals in rapidly identifying life-threatening injuries. The study enrolled patients from a single trauma center who received abdominal CT scans between 2008 and 2017. Patients with spleen, liver, or kidney injury were categorized as the solid organ injury group, while others were considered negative cases. Only images acquired from the trauma center were enrolled. A subset of images acquired in the last year was designated as the test set, and the remaining images were utilized to train and validate the detection models. The performance of each model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value based on the best Youden index operating point. The study developed the models using 1302 (87%) scans for training and tested them on 194 (13%) scans. The spleen injury model demonstrated an accuracy of 0.938 and a specificity of 0.952. The accuracy and specificity of the liver injury model were reported as 0.820 and 0.847, respectively. The kidney injury model showed an accuracy of 0.959 and a specificity of 0.989. We developed a DLM that can automate the detection of solid organ injuries by abdominal CT scans with acceptable diagnostic accuracy. It cannot replace the role of clinicians, but we can expect it to be a potential tool to accelerate the process of therapeutic decisions for trauma care.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Hou-Hsien Lin
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Po Hsu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Huan-Wu Chen
- Department of Medical Imaging & Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Jen-Fu Huang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Hsun Hsieh
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Chih-Yuan Fu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan
| | - I-Fang Chung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
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Saha A, Ganie SM, Dutta Pramanik PK, Yadav RK, Mallik S, Zhao Z. Correction: VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images. BMC Med Imaging 2024; 24:128. [PMID: 38822231 PMCID: PMC11140995 DOI: 10.1186/s12880-024-01315-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024] Open
Affiliation(s)
- Anindita Saha
- Department of Computing Science and Engineering, IFTM University, Moradabad, Uttar Pradesh, India
| | - Shahid Mohammad Ganie
- AI Research Centre, Department of Analytics, School of Business, Woxsen University, Hyderabad, Telangana, 502345, India
| | - Pijush Kanti Dutta Pramanik
- School of Computer Applications and Technology, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.
| | - Rakesh Kumar Yadav
- Department of Computer Science & Engineering, MSOET, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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17
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Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.
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Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
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18
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Botnari A, Kadar M, Patrascu JM. A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2024; 14:1090. [PMID: 38893617 PMCID: PMC11172202 DOI: 10.3390/diagnostics14111090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVES This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). MATERIALS AND METHODS This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. RESULTS This study's findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears-whether in the anterior or posterior horn-with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. CONCLUSIONS Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool.
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Affiliation(s)
- Alexei Botnari
- Department of Orthopedics, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Manuella Kadar
- Department of Computer Science, Faculty of Informatics and Engineering, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania
| | - Jenel Marian Patrascu
- Department of Orthopedics-Traumatology, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania;
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Saha A, Ganie SM, Pramanik PKD, Yadav RK, Mallik S, Zhao Z. VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images. BMC Med Imaging 2024; 24:120. [PMID: 38789925 PMCID: PMC11127393 DOI: 10.1186/s12880-024-01238-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/05/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. METHODS In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. RESULTS The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. CONCLUSION VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.
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Affiliation(s)
- Anindita Saha
- Department of Computing Science and Engineering, IFTM University, Moradabad, Uttar Pradesh, India
| | - Shahid Mohammad Ganie
- AI Research Centre, Department of Analytics, School of Business, Woxsen University, Hyderabad, Telangana, 502345, India
| | - Pijush Kanti Dutta Pramanik
- School of Computer Applications and Technology, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.
| | - Rakesh Kumar Yadav
- Department of Computer Science & Engineering, MSOET, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Xie P, Wang H, Xiao J, Xu F, Liu J, Chen Z, Zhao W, Hou S, Wu D, Ma Y, Xiao J. Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study. J Med Internet Res 2024; 26:e49848. [PMID: 38728685 PMCID: PMC11127140 DOI: 10.2196/49848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/02/2023] [Accepted: 04/02/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. OBJECTIVE This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. METHODS In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. RESULTS A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. CONCLUSIONS The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.
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Affiliation(s)
- Puguang Xie
- Chongqing Emergency Medical Centre, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Hao Wang
- Chongqing Emergency Medical Centre, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jun Xiao
- Chongqing Emergency Medical Centre, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Fan Xu
- Chongqing Emergency Medical Centre, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jingyang Liu
- Chongqing Emergency Medical Centre, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Zihang Chen
- Bioengineering College, Chongqing University, Chongqing, China
| | - Weijie Zhao
- Bioengineering College, Chongqing University, Chongqing, China
| | - Siyu Hou
- Bio-Med Informatics Research Centre & Clinical Research Centre, Xinqiao Hospital, Army Medical University, Chongqing, China
| | - Dongdong Wu
- Medical Big Data Research Centre, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yu Ma
- Chongqing Emergency Medical Centre, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jingjing Xiao
- Bio-Med Informatics Research Centre & Clinical Research Centre, Xinqiao Hospital, Army Medical University, Chongqing, China
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21
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Devault-Tousignant C, Harvie M, Bissada E, Christopoulos A, Tabet P, Guertin L, Bahig H, Ayad T. The use of artificial intelligence in reconstructive surgery for head and neck cancer: a systematic review. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08663-4. [PMID: 38662215 DOI: 10.1007/s00405-024-08663-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVES The popularity of artificial intelligence (AI) in head and neck cancer (HNC) management is increasing, but postoperative complications remain prevalent and are the main factor that impact prognosis after surgery. Hence, recent studies aim to assess new AI models to evaluate their ability to predict free flap complications more effectively than traditional algorithms. This systematic review aims to summarize current evidence on the utilization of AI models to predict complications following reconstructive surgery for HNC. METHODS A combination of MeSH terms and keywords was used to cover the following three subjects: "HNC," "artificial intelligence," and "free flap or reconstructive surgery." The electronic literature search was performed in three relevant databases: Medline (Ovid), Embase (Ovid), and Cochrane. Quality appraisal of the included study was conducted using the TRIPOD Statement. RESULTS The review included a total of 5 manuscripts (n = 5) for a total of 7524 patients. Across studies, the highest area under the receiver operating characteristic (AUROC) value achieved was 0.824 by the Auto-WEKA model. However, only 20% of reported AUROCs exceeded 0.70. One study concluded that most AI models were comparable or inferior in performance to conventional logistic regression. The highest predictors of complications were flap type, smoking status, tumour location, and age. DISCUSSION Some models showed promising results. Predictors identified across studies were different than those found in existing literature, showing the added value of AI models. However, the algorithms showed inconsistent results, underlying the need for better-powered studies with larger databases before clinical implementation.
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Affiliation(s)
- Cyril Devault-Tousignant
- Faculty of Medicine, McGill University, 3605 de la Montagne Street, Montreal, QC, H3G 2M1, Canada.
| | - Myriam Harvie
- Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Eric Bissada
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Apostolos Christopoulos
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Paul Tabet
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Louis Guertin
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Houda Bahig
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Department of Radiation Oncology, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - Tareck Ayad
- Division of Otolaryngology Head and Neck Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
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22
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Ferreira A, Li J, Pomykala KL, Kleesiek J, Alves V, Egger J. GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy. Med Image Anal 2024; 93:103100. [PMID: 38340545 DOI: 10.1016/j.media.2024.103100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
With the massive proliferation of data-driven algorithms, such as deep learning-based approaches, the availability of high-quality data is of great interest. Volumetric data is very important in medicine, as it ranges from disease diagnoses to therapy monitoring. When the dataset is sufficient, models can be trained to help doctors with these tasks. Unfortunately, there are scenarios where large amounts of data is unavailable. For example, rare diseases and privacy issues can lead to restricted data availability. In non-medical fields, the high cost of obtaining enough high-quality data can also be a concern. A solution to these problems can be the generation of realistic synthetic data using Generative Adversarial Networks (GANs). The existence of these mechanisms is a good asset, especially in healthcare, as the data must be of good quality, realistic, and without privacy issues. Therefore, most of the publications on volumetric GANs are within the medical domain. In this review, we provide a summary of works that generate realistic volumetric synthetic data using GANs. We therefore outline GAN-based methods in these areas with common architectures, loss functions and evaluation metrics, including their advantages and disadvantages. We present a novel taxonomy, evaluations, challenges, and research opportunities to provide a holistic overview of the current state of volumetric GANs.
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Affiliation(s)
- André Ferreira
- Center Algoritmi/LASI, University of Minho, Braga, 4710-057, Portugal; Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH Aachen, 52074 Aachen, Germany.
| | - Jianning Li
- Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany.
| | - Kelsey L Pomykala
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany.
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany; German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, Essen, 45147, Germany; TU Dortmund University, Department of Physics, Otto-Hahn-Straße 4, 44227 Dortmund, Germany.
| | - Victor Alves
- Center Algoritmi/LASI, University of Minho, Braga, 4710-057, Portugal.
| | - Jan Egger
- Computer Algorithms for Medicine Laboratory, Graz, Austria; Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, Essen, 45147, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 801, Austria.
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23
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Yi X, He Y, Gao S, Li M. A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance. Diabetes Metab Syndr 2024; 18:103000. [PMID: 38604060 DOI: 10.1016/j.dsx.2024.103000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 01/23/2024] [Accepted: 03/29/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND AIMS Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research. METHODS An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies. RESULTS Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11). CONCLUSIONS This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).
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Affiliation(s)
- Xinghao Yi
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yangzhige He
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China
| | - Shan Gao
- Department of Endocrinology, Xuan Wu Hospital, Capital Medical University, Beijing 10053, China
| | - Ming Li
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China.
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24
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Park D, Kim Y, Kang H, Lee J, Choi J, Kim T, Lee S, Son S, Kim M, Kim I. PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference. Comput Biol Med 2024; 172:108241. [PMID: 38489987 DOI: 10.1016/j.compbiomed.2024.108241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/30/2024] [Accepted: 02/27/2024] [Indexed: 03/17/2024]
Abstract
Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS). However, it is difficult for the model to accurately segment a bolus region in a VFSS image because VFSS images are translucent, have low contrast and unclear region boundaries, and lack color information. To overcome these challenges, we propose PECI-Net, a network architecture for VFSS image analysis that combines two novel techniques: the preprocessing ensemble network (PEN) and the cascaded inference network (CIN). PEN enhances the sharpness and contrast of the VFSS image by combining multiple preprocessing algorithms in a learnable way. CIN reduces ambiguity in bolus segmentation by using context from other regions through cascaded inference. Moreover, CIN prevents undesirable side effects from unreliably segmented regions by referring to the context in an asymmetric way. In experiments, PECI-Net exhibited higher performance than four recently developed baseline models, outperforming TernausNet, the best among the baseline models, by 4.54% and the widely used UNet by 10.83%. The results of the ablation studies confirm that CIN and PEN are effective in improving bolus segmentation performance.
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Affiliation(s)
- Dougho Park
- Pohang Stroke and Spine Hospital, Pohang, Republic of Korea; School of Convergence Science and Technology, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Younghun Kim
- School of CSEE, Handong Global University, Pohang, Republic of Korea
| | - Harim Kang
- School of CSEE, Handong Global University, Pohang, Republic of Korea
| | - Junmyeoung Lee
- School of CSEE, Handong Global University, Pohang, Republic of Korea
| | - Jinyoung Choi
- School of CSEE, Handong Global University, Pohang, Republic of Korea
| | - Taeyeon Kim
- Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Sangeok Lee
- Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Seokil Son
- Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Minsol Kim
- Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Injung Kim
- School of CSEE, Handong Global University, Pohang, Republic of Korea.
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25
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Farrahi V, Collings PJ, Oussalah M. Deep learning of movement behavior profiles and their association with markers of cardiometabolic health. BMC Med Inform Decis Mak 2024; 24:74. [PMID: 38481262 PMCID: PMC10936042 DOI: 10.1186/s12911-024-02474-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders. METHODS Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20-65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003-2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data. RESULTS Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as "Early-morning movers" and the profile characterized as "Highest activity" both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the "Lowest activity" profile. No significant differences were observed for the "Least sedentary movers" profile compared to the "Lowest activity" profile. CONCLUSIONS Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.
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Affiliation(s)
- Vahid Farrahi
- Institute for Sport and Sport Science, TU Dortmund University, Dortmund, Germany.
| | - Paul J Collings
- Physical Activity, Sport and Health Research Group, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Mourad Oussalah
- Centre of Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
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26
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Kanbour S, Harris C, Lalani B, Wolf RM, Fitipaldi H, Gomez MF, Mathioudakis N. Machine Learning Models for Prediction of Diabetic Microvascular Complications. J Diabetes Sci Technol 2024; 18:273-286. [PMID: 38189280 PMCID: PMC10973856 DOI: 10.1177/19322968231223726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
IMPORTANCE AND AIMS Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). METHODS A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics. RESULTS Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance. CONCLUSIONS AND RELEVANCE There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.
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Affiliation(s)
| | - Catharine Harris
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Benjamin Lalani
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Risa M. Wolf
- Division of Pediatric Endocrinology,
Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund
University Diabetes Centre, Lund University, Malmö, Sweden
| | - Maria F. Gomez
- Department of Clinical Sciences, Lund
University Diabetes Centre, Lund University, Malmö, Sweden
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
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27
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Neves J, Hsieh C, Nobre IB, Sousa SC, Ouyang C, Maciel A, Duchowski A, Jorge J, Moreira C. Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning. Eur J Radiol 2024; 172:111341. [PMID: 38340426 DOI: 10.1016/j.ejrad.2024.111341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/04/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance X-ray analysis by mapping expert focus areas, guiding automated anomaly detection, optimizing workflow efficiency, and bolstering training methods for novice radiologists. However, the literature shows contradictory results regarding the usefulness of eye-tracking data in deep-learning architectures for abnormality detection. We argue that these discrepancies between studies in the literature are due to (a) the way eye-tracking data is (or is not) processed, (b) the types of deep learning architectures chosen, and (c) the type of application that these architectures will have. We conducted a systematic literature review using PRISMA to address these contradicting results. We analyzed 60 studies that incorporated eye-tracking data in a deep-learning approach for different application goals in radiology. We performed a comparative analysis to understand if eye gaze data contains feature maps that can be useful under a deep learning approach and whether they can promote more interpretable predictions. To the best of our knowledge, this is the first survey in the area that performs a thorough investigation of eye gaze data processing techniques and their impacts in different deep learning architectures for applications such as error detection, classification, object detection, expertise level analysis, fatigue estimation and human attention prediction in medical imaging data. Our analysis resulted in two main contributions: (1) taxonomy that first divides the literature by task, enabling us to analyze the value eye movement can bring for each case and build guidelines regarding architectures and gaze processing techniques adequate for each application, and (2) an overall analysis of how eye gaze data can promote explainability in radiology.
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Affiliation(s)
- José Neves
- Instituto Superior Técnico / INESC-ID, University of Lisbon, Portugal.
| | - Chihcheng Hsieh
- School of Information Systems, Queensland University of Technology, Australia.
| | | | | | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Australia.
| | - Anderson Maciel
- Instituto Superior Técnico / INESC-ID, University of Lisbon, Portugal.
| | | | - Joaquim Jorge
- Instituto Superior Técnico / INESC-ID, University of Lisbon, Portugal.
| | - Catarina Moreira
- Human Technology Institute, University of Technology Sydney, Australia.
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28
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Heremans ERM, Seedat N, Buyse B, Testelmans D, van der Schaar M, De Vos M. U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging. Comput Biol Med 2024; 171:108205. [PMID: 38401452 DOI: 10.1016/j.compbiomed.2024.108205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/16/2024] [Accepted: 02/20/2024] [Indexed: 02/26/2024]
Abstract
With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high and low confidence and reducing the risk of errors. This study introduces U-PASS, a specialized human-centered machine learning pipeline tailored for clinical applications, which effectively communicates uncertainty to clinical experts and collaborates with them to improve predictions. U-PASS incorporates uncertainty estimation at every stage of the process, including data acquisition, training, and model deployment. Training is divided into a supervised pre-training step and a semi-supervised recording-wise finetuning step. We apply U-PASS to the challenging task of sleep staging and demonstrate that it systematically improves performance at every stage. By optimizing the training dataset, actively seeking feedback from domain experts for informative samples, and deferring the most uncertain samples to experts, U-PASS achieves an impressive expert-level accuracy of 85% on a challenging clinical dataset of elderly sleep apnea patients. This represents a significant improvement over the starting point at 75% accuracy. The largest improvement gain is due to the deferral of uncertain epochs to a sleep expert. U-PASS presents a promising AI approach to incorporating uncertainty estimation in machine learning pipelines, improving their reliability and unlocking their potential in clinical settings.
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Affiliation(s)
- Elisabeth R M Heremans
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
| | | | - Bertien Buyse
- UZ Leuven, Department of Pneumology, Herestraat 49, B-3000 Leuven, Belgium
| | - Dries Testelmans
- UZ Leuven, Department of Pneumology, Herestraat 49, B-3000 Leuven, Belgium
| | | | - Maarten De Vos
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
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29
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Tan Y, Feng LJ, Huang YH, Xue JW, Long LL, Feng ZB. A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features. Transl Oncol 2024; 40:101864. [PMID: 38141376 PMCID: PMC10788295 DOI: 10.1016/j.tranon.2023.101864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/08/2023] [Accepted: 12/12/2023] [Indexed: 12/25/2023] Open
Abstract
OBJECTIVE This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer (pathological staging). METHODS Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram. RESULTS From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness. CONCLUSION This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.
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Affiliation(s)
- Yang Tan
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Li-Juan Feng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Ying-He Huang
- Department of Pathology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, PR China
| | - Jia-Wen Xue
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Li-Ling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, PR China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, PR China.
| | - Zhen-Bo Feng
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
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30
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Yang J, Huang J, Han D, Ma X. Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review. Clin Med Insights Oncol 2024; 18:11795549231220320. [PMID: 38187459 PMCID: PMC10771756 DOI: 10.1177/11795549231220320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024] Open
Abstract
Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.
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Affiliation(s)
- Jiaqing Yang
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Huang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Deqian Han
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
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Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, Badnjevic A. Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics. Technol Health Care 2024; 32:1273-1287. [PMID: 38073356 DOI: 10.3233/thc-231482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
BACKGROUND Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. OBJECTIVE The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. METHODS A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. RESULTS According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. CONCLUSION The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.
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Affiliation(s)
- Edin Medjedovic
- Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
- Department of Gynecology, Obstetrics and Reproductive Medicine, School of Medicine, Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Milan Stanojevic
- Department of Obstetrics and Gynecology, University Hospital "Sveti Duh", Zagreb, Croatia
| | - Sabaheta Jonuzovic-Prosic
- Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Emina Ribic
- Public Institution Department for Health Care of Women and Maternity of Sarajevo Canton, Sarajevo, Bosnia and Herzegovina
| | - Zijo Begic
- Department of Cardiology, Pediatric Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Anis Cerovac
- Department of Gynecology and Obstetrics Tesanj, General Hospital Tesanj, Bosnia and Herzegovina
| | - Almir Badnjevic
- International Burch University, Sarajevo, Bosnia and Herzegovina
- Genetics and Bioengineering Department, Faculty of Engineering and Natural Sciences, Sarajevo, Bosnia and Herzegovina
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Rempe M, Mentzel F, Pomykala KL, Haubold J, Nensa F, Kroeninger K, Egger J, Kleesiek J. k-strip: A novel segmentation algorithm in k-space for the application of skull stripping. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107912. [PMID: 37981454 DOI: 10.1016/j.cmpb.2023.107912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/30/2023] [Accepted: 11/02/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND AND OBJECTIVE We present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich complex valued k-space. METHODS Using four datasets from different institutions with a total of around 200,000 MRI slices, we show that our network can perform skull-stripping on the raw data of MRIs while preserving the phase information which no other skull stripping algorithm is able to work with. For two of the datasets, skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain is used as the ground truth, whereas the third and fourth dataset comes with per-hand annotated brain segmentations. RESULTS All four datasets were very similar to the ground truth (DICE scores of 92 %-99 % and Hausdorff distances of under 5.5 pixel). Results on slices above the eye-region reach DICE scores of up to 99 %, whereas the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-Strip often has smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. CONCLUSION With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Besides preserving valuable information for further diagnostics, this approach makes an immediate anonymization of patient data possible, already before being transformed into the image domain. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.
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Affiliation(s)
- Moritz Rempe
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Florian Mentzel
- Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Kelsey L Pomykala
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Johannes Haubold
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Felix Nensa
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Kevin Kroeninger
- Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Jan Egger
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; The Computer Algorithms for Medicine Laboratory, Graz, Austria; The Institute of Computer Graphics and Vision, Inffeldgasse 16, Graz University of Technology, Graz 8010, Austria; Cancer Research Center Cologne Essen (CCCE), Hufelandstraße 55, University Medicine Essen, Essen 45147, Germany
| | - Jens Kleesiek
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; Cancer Research Center Cologne Essen (CCCE), Hufelandstraße 55, University Medicine Essen, Essen 45147, Germany; Partner Site Essen, Hufelandstraße 55, German Cancer Consortium (DKTK), Essen 45147, Germany.
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Katal N, Gupta S, Verma P, Sharma B. Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation. Diagnostics (Basel) 2023; 13:3605. [PMID: 38132188 PMCID: PMC10742760 DOI: 10.3390/diagnostics13243605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 12/23/2023] Open
Abstract
Heart diseases is the world's principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning approaches have played an important role in automatically identifying complicated patterns from ECG data, which can be further used to identify arrhythmia. In this paper, deep-learning-based methods for arrhythmia identification using ECG signals are thoroughly studied and their performances evaluated on the basis of accuracy, specificity, precision, and F1 score. We propose the development of a small CNN, and its performance is compared against pretrained models like GoogLeNet. The comparative study demonstrates the promising potential of deep-learning-based arrhythmia identification using ECG signals.
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Affiliation(s)
- Nitish Katal
- School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India;
| | - Saurav Gupta
- School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India;
| | - Pankaj Verma
- University Centre for Research and Development, Academic Unit 2, Chandigarh University, Mohali 140413, Punjab, India;
| | - Bhisham Sharma
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Zhang L, Xiao Z, Jiang W, Luo C, Ye M, Yue G, Chen Z, Ouyang S, Liu Y. Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing. Health Inf Sci Syst 2023; 11:51. [PMID: 37954065 PMCID: PMC10632346 DOI: 10.1007/s13755-023-00255-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.
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Affiliation(s)
- Ling Zhang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Zhennan Xiao
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Wenchao Jiang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Chengbin Luo
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Ming Ye
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Guanghui Yue
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060 Guangdong China
| | - Zhiyuan Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Shuman Ouyang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Yupin Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
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36
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Ren W, Zhu Y, Wang Q, Song Y, Fan Z, Bai Y, Lin D. Deep learning prediction model for central lymph node metastasis in papillary thyroid microcarcinoma based on cytology. Cancer Sci 2023; 114:4114-4124. [PMID: 37574759 PMCID: PMC10551586 DOI: 10.1111/cas.15930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/11/2023] [Accepted: 08/01/2023] [Indexed: 08/15/2023] Open
Abstract
Controversy exists regarding whether patients with low-risk papillary thyroid microcarcinoma (PTMC) should undergo surgery or active surveillance; the inaccuracy of the preoperative clinical lymph node status assessment is one of the primary factors contributing to the controversy. It is imperative to accurately predict the lymph node status of PTMC before surgery. We selected 208 preoperative fine-needle aspiration (FNA) liquid-based preparations of PTMC as our research objects; all of these instances underwent lymph node dissection and, aside from lymph node status, were consistent with low-risk PTMC. We separated them into two groups according to whether the postoperative pathology showed central lymph node metastases. The deep learning model was expected to predict, based on the preoperative thyroid FNA liquid-based preparation, whether PTMC was accompanied by central lymph node metastases. Our deep learning model attained a sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and accuracy of 78.9% (15/19), 73.9% (17/23), 71.4% (15/21), 81.0% (17/21), and 76.2% (32/42), respectively. The area under the receiver operating characteristic curve (value was 0.8503. The predictive performance of the deep learning model was superior to that of the traditional clinical evaluation, and further analysis revealed the cell morphologies that played key roles in model prediction. Our study suggests that the deep learning model based on preoperative thyroid FNA liquid-based preparation is a reliable strategy for predicting central lymph node metastases in thyroid micropapillary carcinoma, and its performance surpasses that of traditional clinical examination.
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Affiliation(s)
- Wenhao Ren
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of PathologyPeking University Cancer Hospital and InstituteBeijingChina
| | - Yanli Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of PathologyPeking University Cancer Hospital and InstituteBeijingChina
| | - Qian Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of PathologyPeking University Cancer Hospital and InstituteBeijingChina
| | - Yuntao Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Head and Neck SurgeryPeking University Cancer Hospital and InstituteBeijingChina
| | - Zhihui Fan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of UltrasoundPeking University Cancer Hospital and InstituteBeijingChina
| | - Yanhua Bai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of PathologyPeking University Cancer Hospital and InstituteBeijingChina
| | - Dongmei Lin
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of PathologyPeking University Cancer Hospital and InstituteBeijingChina
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Liu Y, Wu M. Deep learning in precision medicine and focus on glioma. Bioeng Transl Med 2023; 8:e10553. [PMID: 37693051 PMCID: PMC10486341 DOI: 10.1002/btm2.10553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 04/13/2023] [Accepted: 05/08/2023] [Indexed: 09/12/2023] Open
Abstract
Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi-omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi-omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.
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Affiliation(s)
- Yihao Liu
- Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunanChina
- NHC Key Laboratory of Carcinogenesis, Xiangya HospitalCentral South UniversityChangshaHunanChina
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research InstituteCentral South UniversityChangshaHunanChina
| | - Minghua Wu
- Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunanChina
- NHC Key Laboratory of Carcinogenesis, Xiangya HospitalCentral South UniversityChangshaHunanChina
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research InstituteCentral South UniversityChangshaHunanChina
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Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems. Clin Pract 2023; 13:994-1014. [PMID: 37623270 PMCID: PMC10453547 DOI: 10.3390/clinpract13040089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.
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Affiliation(s)
| | - Yaron Ilan
- Hadassah Medical Center, Department of Medicine, Faculty of Medicine, Hebrew University, POB 1200, Jerusalem IL91120, Israel;
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Kufel J, Bargieł-Łączek K, Kocot S, Koźlik M, Bartnikowska W, Janik M, Czogalik Ł, Dudek P, Magiera M, Lis A, Paszkiewicz I, Nawrat Z, Cebula M, Gruszczyńska K. What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine. Diagnostics (Basel) 2023; 13:2582. [PMID: 37568945 PMCID: PMC10417718 DOI: 10.3390/diagnostics13152582] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/19/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023] Open
Abstract
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.
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Affiliation(s)
- Jakub Kufel
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Zabrze, Poland;
| | - Katarzyna Bargieł-Łączek
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
| | - Szymon Kocot
- Bright Coders’ Factory, Technologiczna 2, 45-839 Opole, Poland
| | - Maciej Koźlik
- Division of Cardiology and Structural Heart Disease, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Wiktoria Bartnikowska
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
| | - Michał Janik
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Łukasz Czogalik
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Piotr Dudek
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Mikołaj Magiera
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Anna Lis
- Cardiology Students’ Scientific Association at the III Department of Cardiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Iga Paszkiewicz
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Zbigniew Nawrat
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Zabrze, Poland;
| | - Maciej Cebula
- Individual Specialist Medical Practice Maciej Cebula, 40-754 Katowice, Poland;
| | - Katarzyna Gruszczyńska
- Department of Radiodiagnostics, Invasive Radiology and Nuclear Medicine, Department of Radiology and Nuclear Medicine, School of Medicine in Katowice, Medical University of Silesia, Medyków 14, 40-752 Katowice, Poland;
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Azher Z, Fatemi M, Lu Y, Srinivasan G, Diallo A, Christensen B, Salas L, Kolling F, Perreard L, Palisoul S, Vaickus L, Levy J. Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.30.551187. [PMID: 37577686 PMCID: PMC10418096 DOI: 10.1101/2023.07.30.551187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.
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Zhang K, Fan Y, Long K, Lan Y, Gao P. Research Hotspots and Trends of Deep Learning in Critical Care Medicine: A Bibliometric and Visualized Study. J Multidiscip Healthc 2023; 16:2155-2166. [PMID: 37539364 PMCID: PMC10395519 DOI: 10.2147/jmdh.s420709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/25/2023] [Indexed: 08/05/2023] Open
Abstract
Background Interest in the application of deep learning (DL) in critical care medicine (CCM) is growing rapidly. However, comprehensive bibliometric research that analyze and measure the global literature is still lacking. Objective The present study aimed to systematically evaluate the research hotspots and trends of DL in CCM worldwide based on the output of publications, cooperative relationships of research, citations, and the co-occurrence of keywords. Methods A total of 1708 articles in all were obtained from Web of Science. Bibliometric analysis was performed by Bibliometrix package in R software (4.2.2), Microsoft Excel 2019, VOSviewer (1.6.18), and CiteSpace (5.8.R3). Results The annual publications increased steeply in the past five years, accounting for 95.67% (1634/1708) of all the included literature. China and USA contributed to approximately 71.66% (1244/1708) of all publications. Seven of the top ten most productive organizations rank in the top 100 universities globally. Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Convolutional neural networks, long and short-term memory networks, recurrent neural networks, transformer models, and attention mechanisms were all commonly used DL technologies. Conclusion Hot spots in research on the application of DL in CCM have focused on classifying disease phenotypes, predicting early signs of clinical deterioration, and forecasting disease progression, prognosis, and death. Extensive collaborative research to improve the maturity and robustness of the model remains necessary to make DL-based model applications sufficiently compelling for conventional CCM practice.
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Affiliation(s)
- Kaichen Zhang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Yihua Fan
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Kunlan Long
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Ying Lan
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
| | - Peiyang Gao
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China
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Biamonte P, D’Amico F, Fasulo E, Barà R, Bernardi F, Allocca M, Zilli A, Danese S, Furfaro F. New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients. Biomedicines 2023; 11:2139. [PMID: 37626636 PMCID: PMC10452412 DOI: 10.3390/biomedicines11082139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Ulcerative colitis (UC) is a chronic inflammatory bowel disease primarily affecting the colon and rectum. Endoscopy plays a crucial role in the diagnosis and management of UC. Recent advancements in endoscopic technology, including chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and the use of artificial intelligence, have revolutionized the assessment and treatment of UC patients. These innovative techniques enable early detection of dysplasia and cancer, more precise characterization of disease extent and severity and more targeted biopsies, leading to improved diagnosis and disease monitoring. Furthermore, these advancements have significant implications for therapeutic decision making, empowering clinicians to carefully consider a range of treatment options, including pharmacological therapies, endoscopic interventions and surgical approaches. In this review, we provide an overview of the latest endoscopic technologies and their applications for diagnosing and monitoring UC. We also discuss their impact on treatment decision making, highlighting the potential benefits and limitations of each technique.
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Affiliation(s)
- Paolo Biamonte
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Ferdinando D’Amico
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
| | - Ernesto Fasulo
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Rukaia Barà
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Francesca Bernardi
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Mariangela Allocca
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Alessandra Zilli
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Gastroenterology and Endoscopy, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Federica Furfaro
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
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Zhu S, Zheng W, Pang H. CPAE: Contrastive predictive autoencoder for unsupervised pre-training in health status prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107484. [PMID: 37030137 DOI: 10.1016/j.cmpb.2023.107484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/20/2023] [Accepted: 03/12/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVE Fully-supervised learning approaches have shown promising results in some health status prediction tasks using Electronic Health Records (EHRs). These traditional approaches rely on sufficient labeled data to learn from. However, in practice, acquiring large-scaled labeled medical data for various prediction tasks is often not feasible. Thus, it is of great interest to utilize contrastive pre-training to leverage the unlabeled information. METHODS In this work, we propose a novel data-efficient framework, contrastive predictive autoencoder (CPAE), to first learn without labels from the EHR data in the pre-training process, and then fine-tune on the downstream tasks. Our framework comprises of two parts: (i) a contrastive learning process, inherited from contrastive predictive coding (CPC), which aims to extract global slow-varying features, and (ii) a reconstruction process, which forces the encoder to capture local features. We also introduce the attention mechanism in one variant of our framework to balance the above two processes. RESULTS Experiments on real-world EHR dataset verify the effectiveness of our proposed framework on two downstream tasks (i.e., in-hospital mortality prediction and length-of-stay prediction), compared to their supervised counterparts, the CPC model, and other baseline models. CONCLUSIONS By comprising of both contrastive learning components and reconstruction components, CPAE aims to extract both global slow-varying information and local transient information. The best results on two downstream tasks are all achieved by CPAE. The variant AtCPAE is particularly superior when fine-tuned on very small training data. Further work may incorporate techniques of multi-task learning to optimize the pre-training process of CPAEs. Moreover, this work is based on the benchmark MIMIC-III dataset which only includes 17 variables. Future work may extend to a larger number of variables.
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Affiliation(s)
- Shuying Zhu
- Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China.
| | - Weizhong Zheng
- Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China.
| | - Herbert Pang
- Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, NC, USA.
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Schulze A, Tran D, Daum MTJ, Kisilenko A, Maier-Hein L, Speidel S, Distler M, Weitz J, Müller-Stich BP, Bodenstedt S, Wagner M. Ensuring privacy protection in the era of big laparoscopic video data: development and validation of an inside outside discrimination algorithm (IODA). Surg Endosc 2023:10.1007/s00464-023-10078-x. [PMID: 37145173 PMCID: PMC10338566 DOI: 10.1007/s00464-023-10078-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/10/2023] [Indexed: 05/06/2023]
Abstract
BACKGROUND Laparoscopic videos are increasingly being used for surgical artificial intelligence (AI) and big data analysis. The purpose of this study was to ensure data privacy in video recordings of laparoscopic surgery by censoring extraabdominal parts. An inside-outside-discrimination algorithm (IODA) was developed to ensure privacy protection while maximizing the remaining video data. METHODS IODAs neural network architecture was based on a pretrained AlexNet augmented with a long-short-term-memory. The data set for algorithm training and testing contained a total of 100 laparoscopic surgery videos of 23 different operations with a total video length of 207 h (124 min ± 100 min per video) resulting in 18,507,217 frames (185,965 ± 149,718 frames per video). Each video frame was tagged either as abdominal cavity, trocar, operation site, outside for cleaning, or translucent trocar. For algorithm testing, a stratified fivefold cross-validation was used. RESULTS The distribution of annotated classes were abdominal cavity 81.39%, trocar 1.39%, outside operation site 16.07%, outside for cleaning 1.08%, and translucent trocar 0.07%. Algorithm training on binary or all five classes showed similar excellent results for classifying outside frames with a mean F1-score of 0.96 ± 0.01 and 0.97 ± 0.01, sensitivity of 0.97 ± 0.02 and 0.0.97 ± 0.01, and a false positive rate of 0.99 ± 0.01 and 0.99 ± 0.01, respectively. CONCLUSION IODA is able to discriminate between inside and outside with a high certainty. In particular, only a few outside frames are misclassified as inside and therefore at risk for privacy breach. The anonymized videos can be used for multi-centric development of surgical AI, quality management or educational purposes. In contrast to expensive commercial solutions, IODA is made open source and can be improved by the scientific community.
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Affiliation(s)
- A Schulze
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases, Heidelberg, Germany
| | - D Tran
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases, Heidelberg, Germany
| | - M T J Daum
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases, Heidelberg, Germany
| | - A Kisilenko
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- National Center for Tumor Diseases, Heidelberg, Germany
| | - L Maier-Hein
- Division of Intelligent Medical Systems, German Cancer Research Center (Dkfz), Heidelberg, Germany
| | - S Speidel
- Department for Translational Surgical Oncology, National Center for Tumor Diseases, Partner Site Dresden, Dresden, Germany
- Center for the Tactile Internet With Human in the Loop (CeTI), Technische Universität Dresden, Dresden, Germany
| | - M Distler
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - J Weitz
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - B P Müller-Stich
- Clarunis, University Center for Gastrointestinal and Liver Disease, Basel, Switzerland
| | - S Bodenstedt
- Department for Translational Surgical Oncology, National Center for Tumor Diseases, Partner Site Dresden, Dresden, Germany
- Center for the Tactile Internet With Human in the Loop (CeTI), Technische Universität Dresden, Dresden, Germany
| | - M Wagner
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
- National Center for Tumor Diseases, Heidelberg, Germany.
- Center for the Tactile Internet With Human in the Loop (CeTI), Technische Universität Dresden, Dresden, Germany.
- Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
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Ozcelik N, Ozcelik AE, Guner Zirih NM, Selimoglu I, Gumus A. Deep learning for diagnosis of malign pleural effusion on computed tomography images. Clinics (Sao Paulo) 2023; 78:100210. [PMID: 37149920 DOI: 10.1016/j.clinsp.2023.100210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 04/01/2023] [Accepted: 04/18/2023] [Indexed: 05/09/2023] Open
Abstract
BACKGROUND The pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surface ensures a regular absorption of this fluid. If this balance is disturbed, fluid accumulation occurs in the pleural space called "Pleural Effusion". Today, accurate diagnosis of pleural diseases is becoming more critical, as advances in treatment protocols have contributed positively to prognosis. Our aim is to perform computer-aided numerical analysis of Computed Tomography (CT) images from patients showing pleural effusion images on CT and to examine the prediction of malignant/benign distinction using deep learning by comparing with the cytology results. METHODS The authors classified 408 CT images from 64 patients whose etiology of pleural effusion was investigated using the deep learning method. 378 of the images were used for the training of the system; 15 malignant and 15 benign CT images, which were not included in the training group, were used as the test. RESULTS Among the 30 test images evaluated in the system; 14 of 15 malignant patients and 13 of 15 benign patients were estimated with correct diagnosis (PPD: 93.3%, NPD: 86.67%, Sensitivity: 87.5%, Specificity: 92.86%). CONCLUSION Advances in computer-aided diagnostic analysis of CT images and obtaining a pre-diagnosis of pleural fluid may reduce the need for interventional procedures by guiding physicians about which patients may have malignancies. Thus, it is cost and time-saving in patient management, allowing earlier diagnosis and treatment.
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Affiliation(s)
- Neslihan Ozcelik
- Recep Tayyip Erdogan University, Faculty of Medicine, Training and Research Hospital, Chest Disease, Rize, Turkey.
| | - Ali Erdem Ozcelik
- Recep Tayyip Erdogan University, Engineering and Architecture Faculty, Department of Landscape Architecture (Geomatics Engineer), Rize, Turkey
| | - Nese Merve Guner Zirih
- Recep Tayyip Erdogan University, Faculty of Medicine, Training and Research Hospital, Chest Disease, Rize, Turkey
| | - Inci Selimoglu
- Recep Tayyip Erdogan University, Faculty of Medicine, Training and Research Hospital, Chest Disease, Rize, Turkey
| | - Aziz Gumus
- Recep Tayyip Erdogan University, Faculty of Medicine, Training and Research Hospital, Chest Disease, Rize, Turkey
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Li Z, Zhang N, Gong H, Qiu R, Zhang W. MFA-Net: Multiple Feature Association Network for medical image segmentation. Comput Biol Med 2023; 158:106834. [PMID: 37003067 DOI: 10.1016/j.compbiomed.2023.106834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/01/2023] [Accepted: 03/26/2023] [Indexed: 03/30/2023]
Abstract
Medical image segmentation plays a crucial role in computer-aided diagnosis. However, due to the large variability of medical images, accurate segmentation is a highly challenging task. In this paper, we present a novel medical image segmentation network named the Multiple Feature Association Network (MFA-Net), which is based on deep learning techniques. The MFA-Net utilizes an encoder-decoder architecture with skip connections as its backbone network, and a parallelly dilated convolutions arrangement (PDCA) module is integrated between the encoder and the decoder to capture more representative deep features. Furthermore, a multi-scale feature restructuring module (MFRM) is introduced to restructure and fuse the deep features of the encoder. To enhance global attention perception, the proposed global attention stacking (GAS) modules are cascaded on the decoder. The proposed MFA-Net leverages novel global attention mechanisms to improve the segmentation performance at different feature scales. We evaluated our MFA-Net on four segmentation tasks, including lesions in intestinal polyp, liver tumor, prostate cancer, and skin lesion. Our experimental results and ablation study demonstrate that the proposed MFA-Net outperforms state-of-the-art methods in terms of global positioning and local edge recognition.
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Affiliation(s)
- Zhixun Li
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China
| | - Nan Zhang
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China
| | - Huiling Gong
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China.
| | - Ruiyun Qiu
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China
| | - Wei Zhang
- School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China
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Rafieyan S, Vasheghani-Farahani E, Baheiraei N, Keshavarz H. MLATE: Machine learning for predicting cell behavior on cardiac tissue engineering scaffolds. Comput Biol Med 2023; 158:106804. [PMID: 36989740 DOI: 10.1016/j.compbiomed.2023.106804] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/08/2023] [Accepted: 03/20/2023] [Indexed: 03/29/2023]
Abstract
Cardiovascular disease is one of the leading causes of mortality worldwide and is responsible for millions of deaths annually. One of the most promising approaches to deal with this problem, which has spread recently, is cardiac tissue engineering (CTE). Many researchers have tried developing scaffolds with different materials, cell lines, and fabrication methods to help regenerate heart tissue. Machine learning (ML) is one of the hottest topics in science and technology, revolutionizing many fields and changing our perspective on solving problems. As a result of using ML, some scientific issues have been resolved, including protein-folding, a challenging problem in biology that remained unsolved for 50 years. However, it is not well addressed in tissue engineering. An AI-based software was developed by our group called MLATE (Machine Learning Applications in Tissue Engineering) to tackle tissue engineering challenges, which highly depend on conducting costly and time-consuming experiments. For the first time, to the best of our knowledge, a CTE scaffold dataset was created by collecting specifications from the literature, including different materials, cell lines, and fabrication methods commonly used in CTE scaffold development. These specifications were used as variables in the study. Then, the CTE scaffolds were rated based on cell behaviors such as cell viability, growth, proliferation, and differentiation on the scaffold on a scale of 0-3. These ratings were considered a function of the variables in the gathered dataset. It should be stated that this study was merely based on information available in the literature. Then, twenty-eight ML algorithms were applied to determine the most effective one for predicting cell behavior on CTE scaffolds fabricated by different materials, compositions, and methods. The results indicated the high performance of XGBoost with an accuracy of 87%. Also, by implementing ensemble learning algorithms and using five algorithms with the best performance, an accuracy of 93% with the AdaBoost Classifier and Voting Classifier was achieved. Finally, the open-source software developed in this study was made available for everyone by publishing the best model along with a step-by-step guide to using it online at: https://github.com/saeedrafieyan/MLATE.
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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Abstract
This Viewpoint discusses the benefits and potential harms of using artificial intelligence (AI) algorithms in medicine and proposes the collaborative creation of a Code of Conduct for AI in Health Care.
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Affiliation(s)
- David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland
| | - Laura Adams
- National Academy of Medicine, Washington, DC
| | - Peter Embí
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
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Rezaei SR, Ahmadi A. A hierarchical GAN method with ensemble CNN for accurate nodule detection. Int J Comput Assist Radiol Surg 2023; 18:695-705. [PMID: 36522545 PMCID: PMC9754998 DOI: 10.1007/s11548-022-02807-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE Lung cancer can evolve into one of the deadliest diseases whose early detection is one of the major survival factors. However, early detection is a challenging task due to the unclear structure, shape, and the size of the nodule. Hence, radiologists need automated tools to make accurate decisions. METHODS This paper develops a new approach based on generative adversarial network (GAN) architecture for nodule detection to propose a two-step GAN model containing lung segmentation and nodule localization. The first generator comprises a U-net network, while the second utilizes a mask R-CNN. The task of lung segmentation involves a two-class classification of the pixels in each image, categorizing lung pixels in one class and the rest in the other. The classifier becomes imbalanced due to numerous non-lung pixels, decreasing the model performance. This problem is resolved by using the focal loss function for training the generator. Moreover, a new loss function is developed as the nodule localization generator to enhance the diagnosis quality. Discriminator nets are implemented in GANs as an ensemble of convolutional neural networks (ECNNs), using multiple CNNs and connecting their outputs to make a final decision. RESULTS Several experiments are designed to assess the model on the well-known LUNA dataset. The experiments indicate that the proposed model can reduce the error of the state-of-the-art models on the IoU criterion by about 35 and 16% for lung segmentation and nodule localization, respectively. CONCLUSION Unlike recent studies, the proposed method considers two loss functions for generators, further promoting the goal achievements. Moreover, the network of discriminators is regarded as ECNNs, generating rich features for decisions.
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Affiliation(s)
- Seyed Reza Rezaei
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
| | - Abbas Ahmadi
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.
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