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Adachi M, Taki T, Kojima M, Sakamoto N, Matsuura K, Hayashi R, Tabuchi K, Ishikawa S, Ishii G, Sakashita S. Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists. J Pathol Clin Res 2024; 10:e12392. [PMID: 39159053 PMCID: PMC11332396 DOI: 10.1002/2056-4538.12392] [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: 04/02/2024] [Revised: 07/07/2024] [Accepted: 07/16/2024] [Indexed: 08/21/2024]
Abstract
Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. We aimed to develop a highly accurate lymph node recurrence prediction model for clinical stage T1-2, N0 (cT1-2N0) tongue SCC by integrating histopathological artificial intelligence (AI) with clinicopathological information. A dataset from 148 patients with cT1-2N0 tongue SCC was divided into training and test sets. The prediction models were constructed using AI-extracted information from whole slide images (WSIs), human-assessed clinicopathological information, and both combined. Weakly supervised learning and machine learning algorithms were used for WSIs and clinicopathological information, respectively. The combination model utilised both algorithms. Highly predictive patches from the model were analysed for histopathological features. In the test set, the areas under the receiver operating characteristic (ROC) curve for the model using WSI, clinicopathological information, and both combined were 0.826, 0.835, and 0.991, respectively. The highest area under the ROC curve was achieved with the model combining WSI and clinicopathological factors. Histopathological feature analysis showed that highly predicted patches extracted from recurrence cases exhibited significantly more tumour cells, inflammatory cells, and muscle content compared with non-recurrence cases. Moreover, patches with mixed inflammatory cells, tumour cells, and muscle were significantly more prevalent in recurrence versus non-recurrence cases. The model integrating AI-extracted histopathological and human-assessed clinicopathological information demonstrated high accuracy in predicting lymph node recurrence in patients with cT1-2N0 tongue SCC.
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Affiliation(s)
- Masahiro Adachi
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Department of Otolaryngology, Head and Neck SurgeryUniversity of TsukubaTsukubaJapan
| | - Tetsuro Taki
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
| | - Motohiro Kojima
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Naoya Sakamoto
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Kazuto Matsuura
- Department of Head and Neck SurgeryNational Cancer Center Hospital EastKashiwaJapan
| | - Ryuichi Hayashi
- Department of Head and Neck SurgeryNational Cancer Center Hospital EastKashiwaJapan
| | - Keiji Tabuchi
- Department of Otolaryngology, Head and Neck SurgeryUniversity of TsukubaTsukubaJapan
| | - Shumpei Ishikawa
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
- Department of Preventive Medicine, Graduate School of MedicineThe University of TokyoTokyoJapan
| | - Genichiro Ishii
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of Innovative Pathology and Laboratory MedicineNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
| | - Shingo Sakashita
- Department of Pathology and Clinical LaboratoriesNational Cancer Center Hospital EastKashiwaJapan
- Division of PathologyNational Cancer Center Exploratory Oncology Research & Clinical Trial CenterKashiwaJapan
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Ram M, Afrash MR, Moulaei K, Parvin M, Esmaeeli E, Karbasi Z, Heydari S, Sabahi A. Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review. BMC Cancer 2024; 24:1026. [PMID: 39164653 PMCID: PMC11337640 DOI: 10.1186/s12885-024-12764-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 08/05/2024] [Indexed: 08/22/2024] Open
Abstract
BACKGROUND Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes. METHODS An extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study. RESULTS Of the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI's primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images. CONCLUSIONS AI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.
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Affiliation(s)
- Malihe Ram
- Faculty of Medical Sciences, Birjand university of medical sciences, Birjand, Iran
| | - Mohammad Reza Afrash
- Department of Artificial intelligence, Smart University of Medical Sciences, Tehran, Iran
| | - Khadijeh Moulaei
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Mohammad Parvin
- Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, USA
| | - Erfan Esmaeeli
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Karbasi
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Soroush Heydari
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran.
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Kim J, Bae S, Yoon SM, Jeong S. Roadmap for providing and leveraging annotated data by cytologists in the PDAC domain as open data: support for AI-based pathology image analysis development and data utilization strategies. Front Oncol 2024; 14:1346237. [PMID: 39035745 PMCID: PMC11258423 DOI: 10.3389/fonc.2024.1346237] [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: 11/29/2023] [Accepted: 05/31/2024] [Indexed: 07/23/2024] Open
Abstract
Pancreatic cancer is one of the most lethal cancers worldwide, with a 5-year survival rate of less than 5%, the lowest of all cancer types. Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive pancreatic cancer and has been classified as a health emergency in the past few decades. The histopathological diagnosis and prognosis evaluation of PDAC is time-consuming, laborious, and challenging in current clinical practice conditions. Pathological artificial intelligence (AI) research has been actively conducted lately. However, accessing medical data is challenging; the amount of open pathology data is small, and the absence of open-annotation data drawn by medical staff makes it difficult to conduct pathology AI research. Here, we provide easily accessible high-quality annotation data to address the abovementioned obstacles. Data evaluation is performed by supervised learning using a deep convolutional neural network structure to segment 11 annotated PDAC histopathological whole slide images (WSIs) drawn by medical staff directly from an open WSI dataset. We visualized the segmentation results of the histopathological images with a Dice score of 73% on the WSIs, including PDAC areas, thus identifying areas important for PDAC diagnosis and demonstrating high data quality. Additionally, pathologists assisted by AI can significantly increase their work efficiency. The pathological AI guidelines we propose are effective in developing histopathological AI for PDAC and are significant in the clinical field.
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Affiliation(s)
- Jongkwang Kim
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Sumok Bae
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Seong-Mi Yoon
- Department of Pathology at the Medical Center, Medical Research Institute, ORTHOTECH, Daegu, Republic of Korea
| | - Sungmoon Jeong
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
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Zhang S, Yuan Z, Zhou X, Wang H, Chen B, Wang Y. VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108178. [PMID: 38652995 DOI: 10.1016/j.cmpb.2024.108178] [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: 10/31/2023] [Revised: 04/08/2024] [Accepted: 04/13/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis. METHODS To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features. RESULTS The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs. CONCLUSIONS The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.
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Affiliation(s)
- Shuchang Zhang
- Department of Mathematics, National University of Defense Technology, Changsha, China.
| | - Ziyang Yuan
- Academy of Military Sciences of the People's Liberation Army, Beijing, China.
| | - Xianchen Zhou
- Department of Mathematics, National University of Defense Technology, Changsha, China
| | - Hongxia Wang
- Department of Mathematics, National University of Defense Technology, Changsha, China.
| | - Bo Chen
- Suzhou Research Center, Institute of Automation, Chinese Academy of Sciences, Suzhou, China
| | - Yadong Wang
- Department of Laboratory Pathology, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Rahman A, Wadud MAH, Islam MJ, Kundu D, Bhuiyan TMAUH, Muhammad G, Ali Z. Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network. Sci Rep 2024; 14:5297. [PMID: 38438526 PMCID: PMC10912771 DOI: 10.1038/s41598-024-55662-w] [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: 09/18/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.
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Affiliation(s)
- Anichur Rahman
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
- Department of Computer Science and Engineering, Constituent Institute of Dhaka University, National Institute of Textile Engineering and Research (NITER), Savar, Dhaka, 1350, Bangladesh.
| | - Md Anwar Hussen Wadud
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh
| | - Md Jahidul Islam
- Department of Computer Science and Engineering, Green University, Dhaka, Bangladesh
| | - Dipanjali Kundu
- Department of Computer Science and Engineering, Constituent Institute of Dhaka University, National Institute of Textile Engineering and Research (NITER), Savar, Dhaka, 1350, Bangladesh
| | - T M Amir-Ul-Haque Bhuiyan
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
| | - Zulfiqar Ali
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
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Mondello A, Dal Bo M, Toffoli G, Polano M. Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges. Front Pharmacol 2024; 14:1260276. [PMID: 38264526 PMCID: PMC10803549 DOI: 10.3389/fphar.2023.1260276] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/26/2023] [Indexed: 01/25/2024] Open
Abstract
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.
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Affiliation(s)
| | | | | | - Maurizio Polano
- Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy
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Farooq H, Saleem S, Aleem I, Iftikhar A, Sheikh UN, Naveed H. Toward interpretable and generalized mitosis detection in digital pathology using deep learning. Digit Health 2024; 10:20552076241255471. [PMID: 38778869 PMCID: PMC11110526 DOI: 10.1177/20552076241255471] [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: 09/26/2023] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Objective The mitotic activity index is an important prognostic factor in the diagnosis of cancer. The task of mitosis detection is difficult as the nuclei are microscopic in size and partially labeled, and there are many more non-mitotic nuclei compared to mitotic ones. In this paper, we highlight the challenges of current mitosis detection pipelines and propose a method to tackle these challenges. Methods Our proposed methodology is inspired from recent research on deep learning and an extensive analysis on the dataset and training pipeline. We first used the MiDoG'22 dataset for training, validation, and testing. We then tested the methodology without fine-tuning on the TUPAC'16 dataset and on a real-time case from Shaukat Khanum Memorial Cancer Hospital and Research Centre. Results Our methodology has shown promising results both quantitatively and qualitatively. Quantitatively, our methodology achieved an F1-score of 0.87 on the MiDoG'22 dataset and an F1-score of 0.83 on the TUPAC dataset. Qualitatively, our methodology is generalizable and interpretable across various datasets and clinical settings. Conclusion In this paper, we highlight the challenges of current mitosis detection pipelines and propose a method that can accurately predict mitotic nuclei. We illustrate the accuracy, generalizability, and interpretability of our approach across various datasets and clinical settings. Our methodology can speed up the adoption of computer-aided digital pathology in clinical settings.
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Affiliation(s)
- Hasan Farooq
- Computational Biology Research Lab, National University of Computer & Emerging Sciences, Islamabad, Pakistan
| | - Saira Saleem
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Iffat Aleem
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Ayesha Iftikhar
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Umer Nisar Sheikh
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Hammad Naveed
- Computational Biology Research Lab, National University of Computer & Emerging Sciences, Islamabad, Pakistan
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S Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract 2024; 253:154996. [PMID: 38118214 DOI: 10.1016/j.prp.2023.154996] [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: 10/20/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.
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Affiliation(s)
- Mohammed S Alshuhri
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Saudi Arabia
| | | | | | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia, Indonesia; Radiology department of Pantai Indah Kapuk Hospital Jakarta, Jakarta, Indonesia.
| | - Irodakhon Rasulova
- School of Humanities, Natural & Social Sciences, New Uzbekistan University, 54 Mustaqillik Ave., Tashkent 100007, Uzbekistan; Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan
| | - Paul Rodrigues
- Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Abha, Asir, Kingdom of Saudi Arabia
| | | | - Asim Muhammed Alshanberi
- Department of Community Medicine & Pilgrim Healthcare, Umm Alqura University, Makkah 24382, Saudi Arabia; General Medicine Practice Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, the Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
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Badini S, Regondi S, Pugliese R. Unleashing the Power of Artificial Intelligence in Materials Design. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5927. [PMID: 37687620 PMCID: PMC10488647 DOI: 10.3390/ma16175927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.
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10
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Pereira-Prado V, Martins-Silveira F, Sicco E, Hochmann J, Isiordia-Espinoza MA, González RG, Pandiar D, Bologna-Molina R. Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review. Diagnostics (Basel) 2023; 13:2416. [PMID: 37510160 PMCID: PMC10378350 DOI: 10.3390/diagnostics13142416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation.
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Affiliation(s)
- Vanesa Pereira-Prado
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Felipe Martins-Silveira
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Estafanía Sicco
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Jimena Hochmann
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Mario Alberto Isiordia-Espinoza
- Department of Clinics, Los Altos University Center, Institute of Research in Medical Sciences, University of Guadalajara, Guadalajara 44100, Mexico
| | - Rogelio González González
- Research Department, School of Dentistry, Universidad Juárez del Estado de Durango, Durango 34000, Mexico
| | - Deepak Pandiar
- Department of Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Chennai 600077, India
| | - Ronell Bologna-Molina
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
- Research Department, School of Dentistry, Universidad Juárez del Estado de Durango, Durango 34000, Mexico
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11
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Statz GM, Evans AZ, Johnston SL, Adhaduk M, Mudireddy AR, Sonka M, Lee S, Barsotti EJ, Ricci F, Dipaola F, Johansson M, Sheldon RS, Thiruganasambandamoorthy V, Kenny RA, Bullis TC, Pasupula DK, Van Heukelom J, Gebska MA, Olshansky B. Can Artificial Intelligence Enhance Syncope Management?: A JACC: Advances Multidisciplinary Collaborative Statement. JACC. ADVANCES 2023; 2:100323. [PMID: 38939607 PMCID: PMC11198330 DOI: 10.1016/j.jacadv.2023.100323] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/24/2023] [Indexed: 06/29/2024]
Abstract
Syncope, a form of transient loss of consciousness, remains a complex medical condition for which adverse cardiovascular outcomes, including death, are of major concern but rarely occur. Current risk stratification algorithms have not completely delineated which patients benefit from hospitalization and specific interventions. Patients are often admitted unnecessarily and at high cost. Artificial intelligence (AI) and machine learning may help define the transient loss of consciousness event, diagnose the cause, assess short- and long-term risks, predict recurrence, and determine need for hospitalization and therapeutic intervention; however, several challenges remain, including medicolegal and ethical concerns. This collaborative statement, from a multidisciplinary group of clinicians, investigators, and scientists, focuses on the potential role of AI in syncope management with a goal to inspire creation of AI-derived clinical decision support tools that may improve patient outcomes, streamline diagnostics, and reduce health-care costs.
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Affiliation(s)
- Giselle M. Statz
- Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Aron Z. Evans
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Samuel L. Johnston
- Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Mehul Adhaduk
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Avinash R. Mudireddy
- The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USA
| | - Milan Sonka
- The Iowa Initiative for Artificial Intelligence, University of Iowa, Iowa City, Iowa, USA
| | - Sangil Lee
- Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - E. John Barsotti
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA
| | - Fabrizio Ricci
- Department of Neurosciences, Imaging and Clinical Sciences, Institute for Advanced Biomedical Technologies, University G. d’Annunzio, Chieti, Italy
| | - Franca Dipaola
- Internal Medicine, Syncope Unit, IRCCS Humanitas Research Hospital, Humanitas University, Rozzano, Milan, Italy
| | - Madeleine Johansson
- Department of Cardiology, Skåne University Hospital, Lund University, Malmo, Sweden
| | - Robert S. Sheldon
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | | | - Rose-Anne Kenny
- Department of Medical Gerontology, School of Medicine, Trinity College, Dublin, Ireland
| | - Tyler C. Bullis
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Deepak K. Pasupula
- Division of Cardiovascular Disease, Department of Internal Medicine, MercyOne North Iowa Heart Center, Mason City, Iowa, USA
| | - Jon Van Heukelom
- Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Milena A. Gebska
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| | - Brian Olshansky
- Department of Internal Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
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12
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Singh AV, Chandrasekar V, Paudel N, Laux P, Luch A, Gemmati D, Tissato V, Prabhu KS, Uddin S, Dakua SP. Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother 2023; 163:114784. [PMID: 37121152 DOI: 10.1016/j.biopha.2023.114784] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/15/2023] [Accepted: 04/24/2023] [Indexed: 05/02/2023] Open
Abstract
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
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Affiliation(s)
- Ajay Vikram Singh
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | | | - Namuna Paudel
- Department of Chemistry, Amrit Campus, Institute of Science and Technology, Tribhuvan University, Lainchaur, Kathmandu 44600 Nepal
| | - Peter Laux
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Andreas Luch
- Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), 10589 Berlin, Germany
| | - Donato Gemmati
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Veronica Tissato
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy; Centre Hemostasis & Thrombosis, University of Ferrara, 44121 Ferrara, Italy; Centre for Gender Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Kirti S Prabhu
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Shahab Uddin
- Translational Research Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
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13
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Behling AH, Wilson BC, Ho D, Virta M, O'Sullivan JM, Vatanen T. Addressing antibiotic resistance: computational answers to a biological problem? Curr Opin Microbiol 2023; 74:102305. [PMID: 37031568 DOI: 10.1016/j.mib.2023.102305] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 04/11/2023]
Abstract
The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.
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Affiliation(s)
- Anna H Behling
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Brooke C Wilson
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Daniel Ho
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Marko Virta
- Department of Microbiology, University of Helsinki, Helsinki, Finland
| | - Justin M O'Sullivan
- Liggins Institute, University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Private Bag 92019, Auckland, New Zealand; Australian Parkinsons Mission, Garvan Institute of Medical Research, Sydney, New South Wales, 384 Victoria Street, Darlinghurst, NSW 2010, Australia; MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton SO16 6YD, United Kingdom; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore.
| | - Tommi Vatanen
- Liggins Institute, University of Auckland, Auckland, New Zealand; Department of Microbiology, University of Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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14
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Alhassan S, Soudani A, Almusallam M. Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:2228. [PMID: 36850829 PMCID: PMC9962521 DOI: 10.3390/s23042228] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 06/15/2023]
Abstract
The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.
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Affiliation(s)
- Sarah Alhassan
- Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11362, Saudi Arabia
- Department of Computer Science, College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia
| | - Adel Soudani
- Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11362, Saudi Arabia
| | - Manan Almusallam
- Department of Computer Science, College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia
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15
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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Inteligencia artificial al servicio de la salud del futuro. REVISTA MÉDICA CLÍNICA LAS CONDES 2023. [DOI: 10.1016/j.rmclc.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
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17
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Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer. Cancers (Basel) 2022; 14:cancers14225595. [PMID: 36428686 PMCID: PMC9688370 DOI: 10.3390/cancers14225595] [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: 09/09/2022] [Revised: 10/29/2022] [Accepted: 11/01/2022] [Indexed: 11/16/2022] Open
Abstract
As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.
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Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants. BMC Vet Res 2022; 18:394. [DOI: 10.1186/s12917-022-03486-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk.
Results
Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction.
Conclusion
The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT.
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Shanbehzadeh M, Afrash MR, Mirani N, Kazemi-Arpanahi H. Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia. BMC Med Inform Decis Mak 2022; 22:236. [PMID: 36068539 PMCID: PMC9450320 DOI: 10.1186/s12911-022-01980-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/30/2022] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION Chronic myeloid leukemia (CML) is a myeloproliferative disorder resulting from the translocation of chromosomes 19 and 22. CML includes 15-20% of all cases of leukemia. Although bone marrow transplant and, more recently, tyrosine kinase inhibitors (TKIs) as a first-line treatment have significantly prolonged survival in CML patients, accurate prediction using available patient-level factors can be challenging. We intended to predict 5-year survival among CML patients via eight machine learning (ML) algorithms and compare their performance. METHODS The data of 837 CML patients were retrospectively extracted and randomly split into training and test segments (70:30 ratio). The outcome variable was 5-year survival with potential values of alive or deceased. The dataset for the full features and important features selected by minimal redundancy maximal relevance (mRMR) feature selection were fed into eight ML techniques, including eXtreme gradient boosting (XGBoost), multilayer perceptron (MLP), pattern recognition network, k-nearest neighborhood (KNN), probabilistic neural network, support vector machine (SVM) (kernel = linear), SVM (kernel = RBF), and J-48. The scikit-learn library in Python was used to implement the models. Finally, the performance of the developed models was measured using some evaluation criteria with 95% confidence intervals (CI). RESULTS Spleen palpable, age, and unexplained hemorrhage were identified as the top three effective features affecting CML 5-year survival. The performance of ML models using the selected-features was superior to that of the full-features dataset. Among the eight ML algorithms, SVM (kernel = RBF) had the best performance in tenfold cross-validation with an accuracy of 85.7%, specificity of 85%, sensitivity of 86%, F-measure of 87%, kappa statistic of 86.1%, and area under the curve (AUC) of 85% for the selected-features. Using the full-features dataset yielded an accuracy of 69.7%, specificity of 69.1%, sensitivity of 71.3%, F-measure of 72%, kappa statistic of 75.2%, and AUC of 70.1%. CONCLUSIONS Accurate prediction of the survival likelihood of CML patients can inform caregivers to promote patient prognostication and choose the best possible treatment path. While external validation is required, our developed models will offer customized treatment and may guide the prescription of personalized medicine for CML patients.
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Affiliation(s)
- Mostafa Shanbehzadeh
- Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Mohammad Reza Afrash
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nader Mirani
- Department of Treatment, Head of the Medical Truism, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
- Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran
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20
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Liu M, Yang X, Chen G, Ding Y, Shi M, Sun L, Huang Z, Liu J, Liu T, Yan R, Li R. Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China. Front Physiol 2022; 13:896969. [PMID: 36035487 PMCID: PMC9413067 DOI: 10.3389/fphys.2022.896969] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 07/05/2022] [Indexed: 12/03/2022] Open
Abstract
Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE). Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation. Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17). Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.
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Affiliation(s)
- Mengyuan Liu
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xiaofeng Yang
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Guolu Chen
- School of Information and Communication Engineering, Harbin Engineering University, Harbin, China
| | - Yuzhen Ding
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Meiting Shi
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Lu Sun
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhengrui Huang
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jia Liu
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Tong Liu
- School of Information and Communication Engineering, Harbin Engineering University, Harbin, China
- *Correspondence: Tong Liu, ; Ruiling Yan, ; Ruiman Li,
| | - Ruiling Yan
- The First Affiliated Hospital of Jinan University, Guangzhou, China
- *Correspondence: Tong Liu, ; Ruiling Yan, ; Ruiman Li,
| | - Ruiman Li
- The First Affiliated Hospital of Jinan University, Guangzhou, China
- *Correspondence: Tong Liu, ; Ruiling Yan, ; Ruiman Li,
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Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes. Diagnostics (Basel) 2022; 12:diagnostics12071619. [PMID: 35885524 PMCID: PMC9324130 DOI: 10.3390/diagnostics12071619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 11/17/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.
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22
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Huang LY, Chen FY, Jhou MJ, Kuo CH, Wu CZ, Lu CH, Chen YL, Pei D, Cheng YF, Lu CJ. Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study. J Clin Med 2022; 11:3661. [PMID: 35806944 PMCID: PMC9267784 DOI: 10.3390/jcm11133661] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/19/2022] [Accepted: 06/22/2022] [Indexed: 02/07/2023] Open
Abstract
The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.
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Affiliation(s)
- Li-Ying Huang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Fang-Yu Chen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan;
| | - Chun-Heng Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Chung-Ze Wu
- Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City 23561, Taiwan;
- Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Chieh-Hua Lu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, Taipei 11490, Taiwan;
| | - Yen-Lin Chen
- Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan;
| | - Dee Pei
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Department of Medical Education, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 24352, Taiwan; (L.-Y.H.); (F.-Y.C.); (C.-H.K.); (D.P.)
| | - Yu-Fang Cheng
- Department of Endocrinology and Metabolism, Changhua Christian Hospital, Changhua 50051, Taiwan;
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan;
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
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General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology. Diagnostics (Basel) 2022; 12:diagnostics12051272. [PMID: 35626427 PMCID: PMC9141041 DOI: 10.3390/diagnostics12051272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022] Open
Abstract
Integrating Artificial Intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.
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An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8787023. [PMID: 35634063 PMCID: PMC9132629 DOI: 10.1155/2022/8787023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/30/2022] [Accepted: 04/15/2022] [Indexed: 11/30/2022]
Abstract
In the past few years, remote monitoring technologies have grown increasingly important in the delivery of healthcare. According to healthcare professionals, a variety of factors influence the public perception of connected healthcare systems in a variety of ways. First and foremost, wearable technology in healthcare must establish better bonds with the individuals who will be using them. The emotional reactions of patients to obtaining remote healthcare services may be of interest to healthcare practitioners if they are given the opportunity to investigate them. In this study, we develop an artificial intelligence-based classification system that aims to detect the emotions from the input data using metaheuristic feature selection and machine learning classification. The proposed model is made to undergo series of steps involving preprocessing, feature selection, and classification. The simulation is conducted to test the efficacy of the model on various features present in a dataset. The results of simulation show that the proposed model is effective enough to classify the emotions from the input dataset than other existing methods.
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Wang Y, Miao X, Xiao G, Huang C, Sun J, Wang Y, Li P, You X. Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method. Front Genet 2022; 13:889378. [PMID: 35559036 PMCID: PMC9086166 DOI: 10.3389/fgene.2022.889378] [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: 03/04/2022] [Accepted: 03/15/2022] [Indexed: 11/18/2022] Open
Abstract
Background: Heart failure (HF) is the main cause of mortality in hemodialysis (HD) patients. However, it is still a challenge for the prediction of HF in HD patients. Therefore, we aimed to establish and validate a prediction model to predict HF events in HD patients. Methods: A total of 355 maintenance HD patients from two hospitals were included in this retrospective study. A total of 21 variables, including traditional demographic characteristics, medical history, and blood biochemical indicators, were used. Two classification models were established based on the extreme gradient boosting (XGBoost) algorithm and traditional linear logistic regression. The performance of the two models was evaluated based on calibration curves and area under the receiver operating characteristic curves (AUCs). Feature importance and SHapley Additive exPlanation (SHAP) were used to recognize risk factors from the variables. The Kaplan–Meier curve of each risk factor was constructed and compared with the log-rank test. Results: Compared with the traditional linear logistic regression, the XGBoost model had better performance in accuracy (78.5 vs. 74.8%), sensitivity (79.6 vs. 75.6%), specificity (78.1 vs. 74.4%), and AUC (0.814 vs. 0.722). The feature importance and SHAP value of XGBoost indicated that age, hypertension, platelet count (PLT), C-reactive protein (CRP), and white blood cell count (WBC) were risk factors of HF. These results were further confirmed by Kaplan–Meier curves. Conclusions: The HF prediction model based on XGBoost had a satisfactory performance in predicting HF events, which could prove to be a useful tool for the early prediction of HF in HD.
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Affiliation(s)
- Yanfeng Wang
- The School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Xisha Miao
- The School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Gang Xiao
- Department of Clinical Laboratory, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Chun Huang
- The School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Junwei Sun
- The School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Ying Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Panlong Li
- The School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Xu You
- Department of Clinical Laboratory, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
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Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Curr Oncol 2022; 29:1773-1795. [PMID: 35323346 PMCID: PMC8947571 DOI: 10.3390/curroncol29030146] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 12/29/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
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Affiliation(s)
- Hang Qiu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Correspondence: (H.Q.); (X.W.)
| | - Shuhan Ding
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA;
| | - Jianbo Liu
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Xiaodong Wang
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence: (H.Q.); (X.W.)
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Machine Learning and Smart Devices for Diabetes Management: Systematic Review. SENSORS 2022; 22:s22051843. [PMID: 35270989 PMCID: PMC8915068 DOI: 10.3390/s22051843] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/05/2022] [Accepted: 02/18/2022] [Indexed: 01/27/2023]
Abstract
(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.
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Lee J, Jeong J, Jung S, Moon J, Rho S. Verification of De-Identification Techniques for Personal Information Using Tree-Based Methods with Shapley Values. J Pers Med 2022; 12:jpm12020190. [PMID: 35207676 PMCID: PMC8877642 DOI: 10.3390/jpm12020190] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 02/04/2023] Open
Abstract
With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and usability are insufficient. This paper proposes a verification of de-identification techniques for personal healthcare information by considering data confidentiality and usability. Data are generated and preprocessed by considering the actual statistical data, personal information datasets, and de-identification datasets based on medical data to represent the de-identification technique as a numeric dataset. Five tree-based regression models (i.e., decision tree, random forest, gradient boosting machine, extreme gradient boosting, and light gradient boosting machine) are constructed using the de-identification dataset to effectively discover nonlinear relationships between dependent and independent variables in numerical datasets. Then, the most effective model is selected from personal information data in which pseudonym processing is essential for data utilization. The Shapley additive explanation, an explainable artificial intelligence technique, is applied to the most effective model to establish pseudonym processing policies and machine learning to present a machine-learning process that selects an appropriate de-identification methodology.
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Cui Y, Yang M, Zhu J, Zhang H, Duan Z, Wang S, Liao Z, Liu W. Developments in diagnostic applications of saliva in Human Organ Diseases. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2022. [DOI: 10.1016/j.medntd.2022.100115] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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Osta A, Kokkinaki A, Chedrawi C. Online Health Communities: The Impact of AI Conversational Agents on Users. INFORM SYST 2022. [DOI: 10.1007/978-3-030-95947-0_35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors. Curr Opin Otolaryngol Head Neck Surg 2021; 30:107-113. [PMID: 34907957 DOI: 10.1097/moo.0000000000000782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. RECENT FINDINGS The development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy. SUMMARY All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.
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Su K, Wu J, Gu D, Yang S, Deng S, Khakimova AK. An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios. Diagnostics (Basel) 2021; 11:2288. [PMID: 34943525 PMCID: PMC8700766 DOI: 10.3390/diagnostics11122288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/04/2021] [Accepted: 12/06/2021] [Indexed: 12/19/2022] Open
Abstract
Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.
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Affiliation(s)
- Kaixiang Su
- School of Management, Hefei University of Technology, Hefei 230009, China; (K.S.); (S.Y.)
| | - Jiao Wu
- School of Business, Northern Illinois University, DeKalb, IL 60115, USA;
| | - Dongxiao Gu
- School of Management, Hefei University of Technology, Hefei 230009, China; (K.S.); (S.Y.)
- Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009, China
| | - Shanlin Yang
- School of Management, Hefei University of Technology, Hefei 230009, China; (K.S.); (S.Y.)
- Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009, China
| | | | - Aida K. Khakimova
- Scientific-Research Center for Physical-Technical Informatics, Russian New University, Radio St., 22, 105005 Moscow, Russia;
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Ulivieri FM, Rinaudo L, Messina C, Piodi LP, Capra D, Lupi B, Meneguzzo C, Sconfienza LM, Sardanelli F, Giustina A, Grossi E. Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study. Eur Radiol Exp 2021; 5:47. [PMID: 34664136 PMCID: PMC8523735 DOI: 10.1186/s41747-021-00242-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 08/23/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND We applied an artificial intelligence-based model to predict fragility fractures in postmenopausal women, using different dual-energy x-ray absorptiometry (DXA) parameters. METHODS One hundred seventy-four postmenopausal women without vertebral fractures (VFs) at baseline (mean age 66.3 ± 9.8) were retrospectively evaluated. Data has been collected from September 2010 to August 2018. All subjects performed a spine x-ray to assess VFs, together with lumbar and femoral DXA for bone mineral density (BMD) and the bone strain index (BSI) evaluation. Follow-up exams were performed after 3.34 ± 1.91 years. Considering the occurrence of new VFs at follow-up, two groups were created: fractured versus not-fractured. We applied an artificial neural network (ANN) analysis with a predictive tool (TWIST system) to select relevant input data from a list of 13 variables including BMD and BSI. A semantic connectivity map was built to analyse the connections among variables within the groups. For group comparisons, an independent-samples t-test was used; variables were expressed as mean ± standard deviation. RESULTS For each patient, we evaluated a total of n = 6 exams. At follow-up, n = 69 (39.6%) women developed a VF. ANNs reached a predictive accuracy of 79.56% within the training testing procedure, with a sensitivity of 80.93% and a specificity of 78.18%. The semantic connectivity map showed that a low BSI at the total femur is connected to the absence of VFs. CONCLUSION We found a high performance of ANN analysis in predicting the occurrence of VFs. Femoral BSI appears as a useful DXA index to identify patients at lower risk for lumbar VFs.
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Affiliation(s)
- Fabio Massimo Ulivieri
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
- Current address: Università Vita-Salute San Raffaele, Via Olgettina, 58 20132, Milan, Italy
| | - Luca Rinaudo
- BSE TECHNOLOGIC S.r.l., Lungo Dora Voghera, 34/36A, 10153, Turin, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milan, Italy
| | - Luca Petruccio Piodi
- Former: Gastroenterology and Digestive Endoscopy Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Davide Capra
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy
| | - Barbara Lupi
- Scuola di Specializzazione in Medicina Fisica e Riabilitativa, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122, Milan, Italy
| | - Camilla Meneguzzo
- Scuola di Specializzazione in Medicina Fisica e Riabilitativa, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milan, Italy.
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy.
| | - Francesco Sardanelli
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy
- IRCCS Policlinico San Donato, Via Rodolfo Morandi, 30, 20097, San Donato Milanese, Milan, Italy
| | - Andrea Giustina
- Institute of Endocrine and Metabolic Sciences (IEMS) San Raffaele Vita-Salute University, IRCCS San Raffaele Hospital, Via Olgettina Milano, 20, 20132, Milan, MI, Italy
| | - Enzo Grossi
- Villa Santa Maria Foundation, Via IV Novembre, 15, 22038, Tavernerio, Como, Italy
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Abstract
INTRODUCTION Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbidity and mortality. This essay explores the forms of artificial intelligence to be implemented the field of burns management to optimise the care we deliver in the National Health Service (NHS) in the UK. METHODS Machine Learning methods which predict or classify are explored. This includes linear and logistic regression, artificial neural networks, deep learning, and decision tree analysis. DISCUSSION Utilizing Machine Learning in burns care holds potential from prevention, burns assessment, predicting mortality and critical care monitoring to healing time. Establishing a regional or national Machine Learning group would be the first step towards the development of these essential technologies. CONCLUSION The implementation of machine learning technologies will require buy-in from the NHS health boards, with significant implications with cost of investment, implementation, employment of machine learning teams and provision of training to medical professionals.
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Affiliation(s)
- Lydia Robb
- Core Surgical Trainee, East of Scotland Deanery, Plastic Surgery Department, NHS Lothian, St John's Hospital at Howden, Livingston
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A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data. Processes (Basel) 2021. [DOI: 10.3390/pr9081466] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.
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Schade F, Schwack W, Demirbas Y, Morlock GE. Open-source all-in-one LabToGo Office Chromatography. Anal Chim Acta 2021; 1174:338702. [PMID: 34247737 DOI: 10.1016/j.aca.2021.338702] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/16/2021] [Accepted: 05/25/2021] [Indexed: 12/14/2022]
Abstract
Print and media technologies were used uncommonly in the field of chromatography and explored in application to create a miniaturized all-in-one LabToGo system. This novel research field termed Office Chromatography (OC) uses additive manufacturing in terms of 3D printing of operational parts as well as open-source hard- and software. The OCLab2 presented here has been considerably extended in its functionalities. For inkjet printing of solutions, a newly designed printhead was manufactured controlled by a self-constructed ink-jet board, allowing to check the nozzles' resistance heating circuit. Plate heating was newly integrated, especially favorable for the demonstrated application of higher volumes of aqueous samples. The UV/Vis/FLD plate images were captured by a Raspberry Pi V2 camera module under illumination by novel light emitting diodes (LEDs) for highly selective RGBW color (Vis), UVC 278-nm (UV) and UVA 366-nm (FLD) detection, installed in a newly created miniature cabinet to protect from extraneous light. The spectral separation of differently colored food dyes was achieved by the fully addressable driver controlled RGBW LEDs. The software was newly written in R to speed-up the processes, supported by the new Raspberry Pi 4B computer with 4 GB RAM. The analysis of Stevia leaves for steviol glycosides yielded results comparable to the status quo. Different water samples were analyzed for bioactive compounds. Thereby, compounds of general cytotoxicity were effect-directed detected by bioluminescent A. fischeri bacteria. It allowed the bioanalytical screening for potential risks in tap water, surface waters, rain water, landfill leachates and biogas slurries.
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Affiliation(s)
- Fred Schade
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Wolfgang Schwack
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Yetkin Demirbas
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany
| | - Gertrud E Morlock
- Chair of Food Science, Institute of Nutritional Science, and Interdisciplinary Research Center (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany.
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Sultan H, Owais M, Park C, Mahmood T, Haider A, Park KR. Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine. J Pers Med 2021; 11:482. [PMID: 34072079 PMCID: PMC8229063 DOI: 10.3390/jpm11060482] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/15/2021] [Accepted: 05/24/2021] [Indexed: 01/10/2023] Open
Abstract
Re-operations and revisions are often performed in patients who have undergone total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RTSA). This necessitates an accurate recognition of the implant model and manufacturer to set the correct apparatus and procedure according to the patient's anatomy as personalized medicine. Owing to unavailability and ambiguity in the medical data of a patient, expert surgeons identify the implants through a visual comparison of X-ray images. False steps cause heedlessness, morbidity, extra monetary weight, and a waste of time. Despite significant advancements in pattern recognition and deep learning in the medical field, extremely limited research has been conducted on classifying shoulder implants. To overcome these problems, we propose a robust deep learning-based framework comprised of an ensemble of convolutional neural networks (CNNs) to classify shoulder implants in X-ray images of different patients. Through our rotational invariant augmentation, the size of the training dataset is increased 36-fold. The modified ResNet and DenseNet are then combined deeply to form a dense residual ensemble-network (DRE-Net). To evaluate DRE-Net, experiments were executed on a 10-fold cross-validation on the openly available shoulder implant X-ray dataset. The experimental results showed that DRE-Net achieved an accuracy, F1-score, precision, and recall of 85.92%, 84.69%, 85.33%, and 84.11%, respectively, which were higher than those of the state-of-the-art methods. Moreover, we confirmed the generalization capability of our network by testing it in an open-world configuration, and the effectiveness of rotational invariant augmentation.
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Affiliation(s)
| | | | | | | | | | - Kang Ryoung Park
- Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea; (H.S.); (M.O.); (C.P.); (T.M.); (A.H.)
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Gonzalez JJ, Houchens N, Gupta A. Quality & safety in the literature: May 2021. BMJ Qual Saf 2021; 30:bmjqs-2021-013322. [PMID: 33727413 DOI: 10.1136/bmjqs-2021-013322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 03/03/2021] [Indexed: 11/04/2022]
Affiliation(s)
- Juan J Gonzalez
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Nathan Houchens
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Medicine Service, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Ashwin Gupta
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Medicine Service, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
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