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Li N, Yang J, Li X, Shi Y, Wang K. Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis. PLoS One 2024; 19:e0303421. [PMID: 38743709 PMCID: PMC11093381 DOI: 10.1371/journal.pone.0303421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND AND AIMS Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance. METHODS The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. RESULTS Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). CONCLUSIONS AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.
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Affiliation(s)
- Na Li
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Jian Yang
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Xiaodong Li
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Kunhong Wang
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
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Iacucci M, Santacroce G, Zammarchi I, Maeda Y, Del Amor R, Meseguer P, Kolawole BB, Chaudhari U, Di Sabatino A, Danese S, Mori Y, Grisan E, Naranjo V, Ghosh S. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease. Lancet Gastroenterol Hepatol 2024:S2468-1253(24)00053-0. [PMID: 38759661 DOI: 10.1016/s2468-1253(24)00053-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/16/2024] [Accepted: 02/23/2024] [Indexed: 05/19/2024]
Abstract
Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials.
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Affiliation(s)
- Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland.
| | - Giovanni Santacroce
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Irene Zammarchi
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Yasuharu Maeda
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Rocío Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Pablo Meseguer
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain; Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
| | | | | | - Antonio Di Sabatino
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy; First Department of Internal Medicine, San Matteo Hospital Foundation, Pavia, Italy
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS Ospedale San Raffaele and University Vita-Salute San Raffaele, Milan, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Enrico Grisan
- School of Engineering, London South Bank University, London, UK
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, HUMAN-tech, Universitat Politècnica de València, València, Spain
| | - Subrata Ghosh
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
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Uchikov P, Khalid U, Vankov N, Kraeva M, Kraev K, Hristov B, Sandeva M, Dragusheva S, Chakarov D, Petrov P, Dobreva-Yatseva B, Novakov I. The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis. Diagnostics (Basel) 2024; 14:1004. [PMID: 38786302 PMCID: PMC11119852 DOI: 10.3390/diagnostics14101004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/05/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND AND OBJECTIVES This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. METHOD A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. RESULTS Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. CONCLUSIONS When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.
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Affiliation(s)
- Petar Uchikov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (P.U.); (I.N.)
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Nikola Vankov
- University Multiprofile Hospital for Active Treatment “Saint George”, 4000 Plovdiv, Bulgaria;
| | - Maria Kraeva
- Department of Otorhynolaryngology, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Krasimir Kraev
- Department of Propedeutics of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Bozhidar Hristov
- Section “Gastroenterology”, Second Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Milena Sandeva
- Department of Midwifery, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Snezhanka Dragusheva
- Department of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
- Department of Anesthesiology, Emergency and Intensive Care Medicine, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Dzhevdet Chakarov
- Department of Propaedeutics of Surgical Diseases, Section of General Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria;
| | - Petko Petrov
- Department of Maxillofacial Surgery, Faculty of Dental Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Bistra Dobreva-Yatseva
- Section “Cardiology”, First Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Ivan Novakov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria; (P.U.); (I.N.)
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Ayana G, Barki H, Choe SW. Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer. Cancers (Basel) 2024; 16:1441. [PMID: 38611117 PMCID: PMC11010958 DOI: 10.3390/cancers16071441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
Endoscopic pathological findings of the gastrointestinal tract are crucial for the early diagnosis of colorectal cancer (CRC). Previous deep learning works, aimed at improving CRC detection performance and reducing subjective analysis errors, are limited to polyp segmentation. Pathological findings were not considered and only convolutional neural networks (CNNs), which are not able to handle global image feature information, were utilized. This work introduces a novel vision transformer (ViT)-based approach for early CRC detection. The core components of the proposed approach are ViTCol, a boosted vision transformer for classifying endoscopic pathological findings, and PUTS, a vision transformer-based model for polyp segmentation. Results demonstrate the superiority of this vision transformer-based CRC detection method over existing CNN and vision transformer models. ViTCol exhibited an outstanding performance in classifying pathological findings, with an area under the receiver operating curve (AUC) value of 0.9999 ± 0.001 on the Kvasir dataset. PUTS provided outstanding results in segmenting polyp images, with mean intersection over union (mIoU) of 0.8673 and 0.9092 on the Kvasir-SEG and CVC-Clinic datasets, respectively. This work underscores the value of spatial transformers in localizing input images, which can seamlessly integrate into the main vision transformer network, enhancing the automated identification of critical image features for early CRC detection.
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Affiliation(s)
- Gelan Ayana
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea;
- School of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia
| | - Hika Barki
- Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea;
| | - Se-woon Choe
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea;
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32608, USA
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Li B, Chen H, Duan H. Artificial intelligence-driven prognostic system for conception prediction and management in intrauterine adhesions following hysteroscopic adhesiolysis: a diagnostic study using hysteroscopic images. Front Bioeng Biotechnol 2024; 12:1327207. [PMID: 38638324 PMCID: PMC11024240 DOI: 10.3389/fbioe.2024.1327207] [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: 10/24/2023] [Accepted: 03/04/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction Intrauterine adhesions (IUAs) caused by endometrial injury, commonly occurring in developing countries, can lead to subfertility. This study aimed to develop and evaluate a DeepSurv architecture-based artificial intelligence (AI) system for predicting fertility outcomes after hysteroscopic adhesiolysis. Methods This diagnostic study included 555 intrauterine adhesions (IUAs) treated with hysteroscopic adhesiolysis with 4,922 second-look hysteroscopic images from a prospective clinical database (IUADB, NCT05381376) with a minimum of 2 years of follow-up. These patients were randomly divided into training, validation, and test groups for model development, tuning, and external validation. Four transfer learning models were built using the DeepSurv architecture and a code-free AI application for pregnancy prediction was also developed. The primary outcome was the model's ability to predict pregnancy within a year after adhesiolysis. Secondary outcomes were model performance which evaluated using time-dependent area under the curves (AUCs) and C-index, and ART benefits evaluated by hazard ratio (HR) among different risk groups. Results External validation revealed that using the DeepSurv architecture, InceptionV3+ DeepSurv, InceptionResNetV2+ DeepSurv, and ResNet50+ DeepSurv achieved AUCs of 0.94, 0.95, and 0.93, respectively, for one-year pregnancy prediction, outperforming other models and clinical score systems. A code-free AI application was developed to identify candidates for ART. Patients with lower natural conception probability indicated by the application had a higher ART benefit hazard ratio (HR) of 3.13 (95% CI: 1.22-8.02, p = 0.017). Conclusion InceptionV3+ DeepSurv, InceptionResNetV2+ DeepSurv, and ResNet50+ DeepSurv show potential in predicting the fertility outcomes of IUAs after hysteroscopic adhesiolysis. The code-free AI application based on the DeepSurv architecture facilitates personalized therapy following hysteroscopic adhesiolysis.
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Affiliation(s)
- Bohan Li
- Department of Minimally Invasive Gynecologic Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Healthcare Hospital, Beijing, China
| | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Capital Medical University, Beijing, China
| | - Hua Duan
- Department of Minimally Invasive Gynecologic Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Healthcare Hospital, Beijing, China
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Liu X, Reigle J, Prasath VBS, Dhaliwal J. Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Comput Biol Med 2024; 171:108093. [PMID: 38354499 DOI: 10.1016/j.compbiomed.2024.108093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - James Reigle
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - V B Surya Prasath
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - Jasbir Dhaliwal
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA.
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Grant RK, Jones GR, Plevris N, Lynch RW, Brindle WM, Hutchings HA, Williams JG, Alrubaiy L, Watkins A, Lees CW, Arnott IDR. Validation of the ACE [Albumin, CRP, and Endoscopy] Index in Acute Colitis: Analysis of the CONSTRUCT dataset. J Crohns Colitis 2024; 18:286-290. [PMID: 37615649 DOI: 10.1093/ecco-jcc/jjad148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND AND AIMS In 2020 we reported the ACE Index in acute colitis which used biochemical and endoscopic parameters to predict steroid non-response on admission in patients with acute ulcerative colitis [UC]. We aimed to validate the ACE Index in an independent cohort. METHODS The validation cohort comprised patients screened as eligible for inclusion in the CONSTRUCT study, a prospective, randomized, placebo-controlled trial which compared the effectiveness of treatment with infliximab vs ciclosporin in patients admitted with acute UC. The CONSTRUCT cohort database was reviewed at The Edinburgh IBD Unit and the same biochemical and endoscopic variables and cut-off values as those in the derivation cohort were applied to the validation cohort. RESULTS In total, 800 patients were identified; 62.5% [55/88] of patients with a maximum ACE Index of 3 did not respond to intravenous [IV] steroids (positive predictive value [PPV] 62.5%, negative predictive value [NPV] 79.8%). Furthermore, 79.8% [158/198] of patients with an ACE Index of 0 responded to IV steroids [PPV 79.8%, NPV 62.5%]. Receiver operator characteristic [ROC] curve analysis produced an area under the curve [AUC] of 0.663 [p < 0.001]. CONCLUSIONS We have now reported and externally validated the ACE Index in acute colitis in a combined cohort of over 1000 patients from across the UK. The ACE Index may be used in conjunction with clinical judgement to help identify patients admitted with active UC who are at high risk of not responding to IV steroids. Further studies are required to improve objectivity and accuracy of assessment.
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Affiliation(s)
- Rebecca K Grant
- The Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK
| | | | - Nikolas Plevris
- The Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK
| | - Ruairi W Lynch
- The Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK
- Department of Gastroenterology, Ninewells Hospital, Dundee, UK
| | - William M Brindle
- The Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK
- Department of Gastroenterology, Victoria Hospital, Kirkcaldy, UK
| | - Hayley A Hutchings
- School of Medicine, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - John G Williams
- School of Medicine, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Laith Alrubaiy
- School of Medicine, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Alan Watkins
- School of Medicine, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Charlie W Lees
- The Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK
| | - Ian D R Arnott
- The Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK
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Pal P, Pooja K, Nabi Z, Gupta R, Tandan M, Rao GV, Reddy N. Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review. Indian J Gastroenterol 2024; 43:172-187. [PMID: 38418774 DOI: 10.1007/s12664-024-01531-3] [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: 11/09/2023] [Accepted: 01/08/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND AND OBJECTIVES In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas. METHODS We searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist. RESULTS The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning. CONCLUSION AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.
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Affiliation(s)
- Partha Pal
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
| | - Kanapuram Pooja
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Zaheer Nabi
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Rajesh Gupta
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Manu Tandan
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
| | - Guduru Venkat Rao
- Surgical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500 082, India
| | - Nageshwar Reddy
- Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India
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Chen C, Tang F, Herth FJF, Zuo Y, Ren J, Zhang S, Jian W, Tang C, Li S. Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images. Ther Adv Respir Dis 2024; 18:17534666241253694. [PMID: 38803144 PMCID: PMC11131396 DOI: 10.1177/17534666241253694] [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: 06/14/2023] [Accepted: 04/22/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings. OBJECTIVES To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images. DESIGN We designed the study by comparing the imaging data of patients undergoing bronchoscopy from January 2010 to October 2022 by using EfficientNet. Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation. METHODS Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs). RESULTS We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%. CONCLUSION We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. The model could help young physicians identify this rare airway disease.
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Affiliation(s)
- Chongxiang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Fei Tang
- Department of Interventional Pulmonary and Endoscopic Diagnosis and Treatment Center, Anhui Chest Hospital, Hefei, Anhui Province, China
| | - Felix J. F. Herth
- Department of Pneumology and Critical Care Medicine and Translational Research Unit, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany
| | - Yingnan Zuo
- Guangzhou Tianpeng Computer Technology Co., Ltd. Guangzhou, Guangdong, China
| | - Jiangtao Ren
- School of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shuaiqi Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wenhua Jian
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Chunli Tang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province 510000, China
| | - Shiyue Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province 510000, China
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Jiang X, Luo X, Nan Q, Ye Y, Miao Y, Miao J. Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity. Therap Adv Gastroenterol 2023; 16:17562848231215579. [PMID: 38144424 PMCID: PMC10748675 DOI: 10.1177/17562848231215579] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/03/2023] [Indexed: 12/26/2023] Open
Abstract
Background Achieving endoscopic and histological remission is a critical treatment objective in ulcerative colitis (UC). Nevertheless, interobserver variability can significantly impact overall assessment performance. Objectives We aimed to develop a deep learning algorithm for the real-time and objective evaluation of endoscopic disease activity and prediction of histological remission in UC. Design This is a retrospective diagnostic study. Methods Two convolutional neural network (CNN) models were constructed and trained using 12,257 endoscopic images and biopsy results sourced from 1124 UC patients who underwent colonoscopy at a single center from January 2018 to December 2022. Mayo Endoscopy Subscore (MES) and UC Endoscopic Index of Severity Score (UCEIS) assessments were conducted by two experienced and independent reviewers. Model performance was evaluated in terms of accuracy, sensitivity, and positive predictive value. The output of the CNN models was also compared with the corresponding histological results to assess histological remission prediction performance. Results The MES-CNN model achieved 97.04% accuracy in diagnosing endoscopic remission of UC, while the MES-CNN and UCEIS-CNN models achieved 90.15% and 85.29% accuracy, respectively, in evaluating endoscopic severity of UC. For predicting histological remission, the CNN models achieved accuracy and kappa values of 91.28% and 0.826, respectively, attaining higher accuracy than human endoscopists (87.69%). Conclusion The proposed artificial intelligence model, based on MES and UCEIS evaluations from expert gastroenterologists, offered precise assessment of inflammation in UC endoscopic images and reliably predicted histological remission.
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Affiliation(s)
- Xinyi Jiang
- Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Yunnan Province Clinical Research Center for Digestive Diseases, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Xudong Luo
- School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, China
| | - Qiong Nan
- Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Yunnan Province Clinical Research Center for Digestive Diseases, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yan Ye
- Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Yunnan Province Clinical Research Center for Digestive Diseases, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yinglei Miao
- Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Yunnan Province Clinical Research Center for Digestive Diseases, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Jiarong Miao
- Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- Yunnan Province Clinical Research Center for Digestive Diseases, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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11
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Rimondi A, Gottlieb K, Despott EJ, Iacucci M, Murino A, Tontini GE. Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Dig Liver Dis 2023:S1590-8658(23)01023-X. [PMID: 38057218 DOI: 10.1016/j.dld.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUNDS AND AIMS Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials. METHODS We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool. RESULTS A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence. CONCLUSIONS AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.
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Affiliation(s)
- Alessandro Rimondi
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom.
| | | | - Edward J Despott
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom
| | - Gian Eugenio Tontini
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Gastroenterology and Endoscopy unit, Milan, Italy
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12
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Uhlig HH, Booth C, Cho J, Dubinsky M, Griffiths AM, Grimbacher B, Hambleton S, Huang Y, Jones K, Kammermeier J, Kanegane H, Koletzko S, Kotlarz D, Klein C, Lenardo MJ, Lo B, McGovern DPB, Özen A, de Ridder L, Ruemmele F, Shouval DS, Snapper SB, Travis SP, Turner D, Wilson DC, Muise AM. Precision medicine in monogenic inflammatory bowel disease: proposed mIBD REPORT standards. Nat Rev Gastroenterol Hepatol 2023; 20:810-828. [PMID: 37789059 DOI: 10.1038/s41575-023-00838-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/31/2023] [Indexed: 10/05/2023]
Abstract
Owing to advances in genomics that enable differentiation of molecular aetiologies, patients with monogenic inflammatory bowel disease (mIBD) potentially have access to genotype-guided precision medicine. In this Expert Recommendation, we review the therapeutic research landscape of mIBD, the reported response to therapies, the medication-related risks and systematic bias in reporting. The mIBD field is characterized by the absence of randomized controlled trials and is dominated by retrospective observational data based on case series and case reports. More than 25 off-label therapeutics (including small-molecule inhibitors and biologics) as well as cellular therapies (including haematopoietic stem cell transplantation and gene therapy) have been reported. Heterogeneous reporting of outcomes impedes the generation of robust therapeutic evidence as the basis for clinical decision making in mIBD. We discuss therapeutic goals in mIBD and recommend standardized reporting (mIBD REPORT (monogenic Inflammatory Bowel Disease Report Extended Phenotype and Outcome of Treatments) standards) to stratify patients according to a genetic diagnosis and phenotype, to assess treatment effects and to record safety signals. Implementation of these pragmatic standards should help clinicians to assess the therapy responses of individual patients in clinical practice and improve comparability between observational retrospective studies and controlled prospective trials, supporting future meta-analysis.
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Affiliation(s)
- Holm H Uhlig
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
- Department of Paediatrics, University of Oxford, Oxford, UK.
- Biomedical Research Centre, University of Oxford, Oxford, UK.
| | - Claire Booth
- UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Paediatric Immunology and Gene Therapy, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Judy Cho
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marla Dubinsky
- Department of Paediatric Gastroenterology, Susan and Leonard Feinstein IBD Clinical Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anne M Griffiths
- SickKids Inflammatory Bowel Disease Centre and Cell Biology Program, Research Institute, Toronto, Canada
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Paediatrics, University of Toronto, Toronto, Canada
| | - Bodo Grimbacher
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency, Medical Center, Faculty of Medicine, Albert Ludwig University of Freiburg, Freiburg, Germany
- Department of Rheumatology and Clinical Immunology, Medical Center, Faculty of Medicine, Albert Ludwig University of Freiburg, Freiburg, Germany
- Institute of Immunology and Transplantation, Royal Free Hospital, University College London, London, UK
| | - Sophie Hambleton
- Primary Immunodeficiency Group, Newcastle University Translational and Clinical Research Institute, Newcastle upon Tyne, UK
| | - Ying Huang
- Department of Gastroenterology, National Children's Medical Center, Children's Hospital of Fudan University, Shanghai, China
| | - Kelsey Jones
- Paediatric Gastroenterology, Great Ormond Street Hospital, London, UK
- Kennedy Institute, University of Oxford, Oxford, UK
| | - Jochen Kammermeier
- Gastroenterology Department, Evelina London Children's Hospital, London, UK
| | - Hirokazu Kanegane
- Department of Child Health and Development, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Sibylle Koletzko
- Dr. von Hauner Children's Hospital, Department of Paediatrics, University Hospital, LMU Munich, Munich, Germany
- Department of Paediatrics, Gastroenterology and Nutrition, School of Medicine Collegium Medicum University of Warmia and Mazury, Olsztyn, Poland
| | - Daniel Kotlarz
- Dr. von Hauner Children's Hospital, Department of Paediatrics, University Hospital, LMU Munich, Munich, Germany
- German Center for Child and Adolescent Health, Munich, Germany
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Christoph Klein
- Dr. von Hauner Children's Hospital, Department of Paediatrics, University Hospital, LMU Munich, Munich, Germany
- German Center for Child and Adolescent Health, Munich, Germany
| | - Michael J Lenardo
- Molecular Development of the Immune System Section, Laboratory of Immune System Biology, and Clinical Genomics Program, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Bernice Lo
- Research Branch, Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Dermot P B McGovern
- F. Widjaja Foundation, Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ahmet Özen
- Marmara University Division of Allergy and Immunology, Istanbul, Turkey
| | - Lissy de Ridder
- Department of Paediatric Gastroenterology, Erasmus University Medical Center Sophia Children's Hospital, Rotterdam, Netherlands
| | - Frank Ruemmele
- Université Paris Cité, APHP, Hôpital Necker Enfants Malades, Service de Gastroentérologie pédiatrique, Paris, France
| | - Dror S Shouval
- Institute of Gastroenterology, Nutrition and Liver Diseases, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Scott B Snapper
- Division of Gastroenterology and Nutrition, Boston Children's Hospital, Boston, MA, USA
- Department of Paediatrics and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Simon P Travis
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
- Biomedical Research Centre, University of Oxford, Oxford, UK
- Kennedy Institute, University of Oxford, Oxford, UK
| | - Dan Turner
- Shaare Zedek Medical Center, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - David C Wilson
- Child Life and Health, Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
- Department of Paediatric Gastroenterology, The Royal Hospital for Children, and Young People, Edinburgh, UK
| | - Aleixo M Muise
- SickKids Inflammatory Bowel Disease Centre and Cell Biology Program, Research Institute, Toronto, Canada
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Paediatrics, University of Toronto, Toronto, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
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13
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Lv B, Ma L, Shi Y, Tao T, Shi Y. A systematic review and meta-analysis of artificial intelligence-diagnosed endoscopic remission in ulcerative colitis. iScience 2023; 26:108120. [PMID: 37867944 PMCID: PMC10585391 DOI: 10.1016/j.isci.2023.108120] [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: 07/12/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 10/24/2023] Open
Abstract
Endoscopic remission is an important therapeutic goal in ulcerative colitis (UC). The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Mayo Endoscopic Score (MES) are the commonly used endoscopic scoring criteria. This systematic review and meta-analysis aimed to evaluate the accuracy of artificial intelligence (AI) in diagnosing endoscopic remission in UC. We also performed a meta-analysis of each of the four endoscopic remission criteria (UCEIS = 0, MES = 0, UCEIS = <1, MES = <1). Eighteen studies involving 13,687 patients were included. The combined sensitivity and specificity of AI for diagnosing endoscopic remission in UC was 87% (95% confidence interval [CI]:81-92%) and 92% (95% CI: 89-94%), respectively. The area under the curve (AUC) was 0.96 (95% CI: 0.94-0.97). The results showed that the AI model performed well regardless of which criteria were used to define endoscopic remission of UC.
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Affiliation(s)
- Bing Lv
- School of Computer Science and Technology, Shandong University of Technology, NO.266, Xincunxi Road, Zibo, Shandong 255000, China
| | - Lihong Ma
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
| | - Yanping Shi
- Department of Pediatrics, Zhoucun Maternal and Child Health Care Hospital, No.72 Mianhuashi Street, Zibo, Shandong 255000, China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
| | - Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
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14
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Kim JH, Choe AR, Park Y, Song EM, Byun JR, Cho MS, Yoo Y, Lee R, Kim JS, Ahn SH, Jung SA. Using a Deep Learning Model to Address Interobserver Variability in the Evaluation of Ulcerative Colitis (UC) Severity. J Pers Med 2023; 13:1584. [PMID: 38003899 PMCID: PMC10672717 DOI: 10.3390/jpm13111584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
The use of endoscopic images for the accurate assessment of ulcerative colitis (UC) severity is crucial to determining appropriate treatment. However, experts may interpret these images differently, leading to inconsistent diagnoses. This study aims to address the issue by introducing a standardization method based on deep learning. We collected 254 rectal endoscopic images from 115 patients with UC, and five experts in endoscopic image interpretation assigned classification labels based on the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) scoring system. Interobserver variance analysis of the five experts yielded an intraclass correlation coefficient of 0.8431 for UCEIS scores and a kappa coefficient of 0.4916 when the UCEIS scores were transformed into UC severity measures. To establish a consensus, we created a model that considered only the images and labels on which more than half of the experts agreed. This consensus model achieved an accuracy of 0.94 when tested with 50 images. Compared with models trained from individual expert labels, the consensus model demonstrated the most reliable prediction results.
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Affiliation(s)
- Jeong-Heon Kim
- Department of Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.-H.K.)
- Medical Physics and Biomedical Engineering Lab (MPBEL), Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - A Reum Choe
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea; (A.R.C.); (Y.P.)
| | - Yehyun Park
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea; (A.R.C.); (Y.P.)
| | - Eun-Mi Song
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea; (A.R.C.); (Y.P.)
| | - Ju-Ran Byun
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea; (A.R.C.); (Y.P.)
| | - Min-Sun Cho
- Department of Pathology, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea (Y.Y.)
| | - Youngeun Yoo
- Department of Pathology, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea (Y.Y.)
| | - Rena Lee
- Department of Bioengineering, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea
| | - Jin-Sung Kim
- Department of Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (J.-H.K.)
- Medical Physics and Biomedical Engineering Lab (MPBEL), Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - So-Hyun Ahn
- Ewha Medical Research Institute, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea
| | - Sung-Ae Jung
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul 03760, Republic of Korea; (A.R.C.); (Y.P.)
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15
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Polat G, Kani HT, Ergenc I, Ozen Alahdab Y, Temizel A, Atug O. Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning. Inflamm Bowel Dis 2023; 29:1431-1439. [PMID: 36382800 DOI: 10.1093/ibd/izac226] [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: 06/06/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment. METHODS The cohort comprises 11 276 images from 564 patients who underwent colonoscopy for UC. We propose a regression-based deep learning approach for the endoscopic evaluation of UC according to the Mayo endoscopic score (MES). Five state-of-the-art convolutional neural network (CNN) architectures were used for the performance measurements and comparisons. Ten-fold cross-validation was used to train the models and objectively benchmark them. Model performances were assessed using quadratic weighted kappa and macro F1 scores for full Mayo score classification and kappa statistics and F1 score for remission classification. RESULTS Five classification-based CNNs used in the study were in excellent agreement with the expert annotations for all Mayo subscores and remission classification according to the kappa statistics. When the proposed regression-based approach was used, (1) the performance of most of the models statistically significantly increased and (2) the same model trained on different cross-validation folds produced more robust results on the test set in terms of deviation between different folds. CONCLUSIONS Comprehensive experimental evaluations show that commonly used classification-based CNN architectures have successful performance in evaluating endoscopic disease activity of UC. Integration of domain knowledge into these architectures further increases performance and robustness, accelerating their translation into clinical use.
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Affiliation(s)
- Gorkem Polat
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
- Neuroscience and Neurotechnology Center of Excellence, Middle East Technical University, Ankara, Turkey
| | - Haluk Tarik Kani
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Ilkay Ergenc
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Yesim Ozen Alahdab
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Alptekin Temizel
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
- Neuroscience and Neurotechnology Center of Excellence, Middle East Technical University, Ankara, Turkey
| | - Ozlen Atug
- Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey
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16
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Chen C, Herth FJF, Zuo Y, Li H, Liang X, Chen Y, Ren J, Jian W, Zhong C, Li S. Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network. Ther Adv Chronic Dis 2023; 14:20406223231181495. [PMID: 37637372 PMCID: PMC10457519 DOI: 10.1177/20406223231181495] [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: 01/15/2023] [Accepted: 05/24/2023] [Indexed: 08/29/2023] Open
Abstract
Background Artificial intelligence (AI) technology has been used for finding lesions via gastrointestinal endoscopy. However, there were few AI-associated studies that discuss bronchoscopy. Objectives To use convolutional neural network (CNN) to recognize the observed anatomical positions of the airway under bronchoscopy. Design We designed the study by comparing the imaging data of patients undergoing bronchoscopy from March 2022 to October 2022 by using EfficientNet (one of the CNNs) and U-Net. Methods Based on the inclusion and exclusion criteria, 1527 clear images of normal anatomical positions of the airways from 200 patients were used for training, and 475 clear images from 72 patients were utilized for validation. Further, 20 bronchoscopic videos of examination procedures in another 20 patients with normal airway structures were used to extract the bronchoscopic images of normal anatomical positions to evaluate the accuracy for the model. Finally, 21 respiratory doctors were enrolled for the test of recognizing corrected anatomical positions using the validating datasets. Results In all, 1527 bronchoscopic images of 200 patients with nine anatomical positions of the airway, including carina, right main bronchus, right upper lobe bronchus, right intermediate bronchus, right middle lobe bronchus, right lower lobe bronchus, left main bronchus, left upper lobe bronchus, and left lower lobe bronchus, were used for supervised machine learning and training, and 475 clear bronchoscopic images of 72 patients were used for validation. The mean accuracy of recognizing these 9 positions was 91% (carina: 98%, right main bronchus: 98%, right intermediate bronchus: 90%, right upper lobe bronchus: 91%, right middle lobe bronchus 92%, right lower lobe bronchus: 83%, left main bronchus: 89%, left upper bronchus: 91%, left lower bronchus: 76%). The area under the curves for these nine positions were >0.98. In addition, the accuracy of extracting the images via the video by the trained model was 94.7%. We also conducted a deep learning study to segment 10 segment bronchi in right lung, and 8 segment bronchi in Left lung. Because of the problem of radial depth, only segment bronchi distributions below right upper bronchus and right middle bronchus could be correctly recognized. The accuracy of recognizing was 84.33 ± 7.52% by doctors receiving interventional pulmonology education in our hospital over 6 months. Conclusion Our study proved that AI technology can be used to distinguish the normal anatomical positions of the airway, and the model we trained could extract the corrected images via the video to help standardize data collection and control quality.
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Affiliation(s)
- Chongxiang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Felix JF Herth
- Department of Pneumology and Critical Care Medicine and Translational Research Unit, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany
| | - Yingnan Zuo
- Guangzhou Tianpeng Computer Technology Co., Ltd. Guangzhou, China
| | - Hongjia Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xinyuan Liang
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yaqing Chen
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jiangtao Ren
- School of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangzhou, China
| | - Wenhua Jian
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Changhao Zhong
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China
| | - Shiyue Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China
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17
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Wang G, Zhang S, Li J, Zhao K, Ding Q, Tian D, Li R, Zou F, Yu Q. CB-HRNet: A Class-Balanced High-Resolution Network for the evaluation of endoscopic activity in patients with ulcerative colitis. Clin Transl Sci 2023; 16:1421-1430. [PMID: 37154517 PMCID: PMC10432877 DOI: 10.1111/cts.13542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/11/2023] [Accepted: 04/21/2023] [Indexed: 05/10/2023] Open
Abstract
Endoscopic evaluation is the key to the management of ulcerative colitis (UC). However, there is interobserver variability in interpreting endoscopic images among gastroenterologists. Furthermore, it is time-consuming. Convolutional neural networks (CNNs) can help overcome these obstacles and has yielded preliminary positive results. We aimed to develop a new CNN-based algorithm to improve the performance for evaluation tasks of endoscopic images in patients with UC. A total of 12,163 endoscopic images from 308 patients with UC were collected from January 2014 to December 2021. The training set and test set images were randomly divided into 37,515 and 3191 after excluding possible interference and data augmentation. Mayo Endoscopic Subscores (MES) were predicted by different CNN-based models with different loss functions. Their performances were evaluated by several metrics. After comparing the results of different CNN-based models with different loss functions, High-Resolution Network with Class-Balanced Loss achieved the best performances in all MES classification subtasks. It was especially great at determining endoscopic remission in UC, which achieved a high accuracy of 95.07% and good performances in other evaluation metrics with sensitivity 92.87%, specificity 95.41%, kappa coefficient 0.8836, positive predictive value 93.44%, negative predictive value 95.00% and area value under the receiver operating characteristic curve 0.9834, respectively. In conclusion, we proposed a new CNN-based algorithm, Class-Balanced High-Resolution Network (CB-HRNet), to evaluate endoscopic activity of UC with excellent performance. Besides, we made an open-source dataset and it can be a new benchmark in the task of MES classification.
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Affiliation(s)
- Ge Wang
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Shujiao Zhang
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
| | - Jie Li
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Kai Zhao
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Qiang Ding
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Dean Tian
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Ruixuan Li
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
| | - Fuhao Zou
- School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
| | - Qin Yu
- Department of GastroenterologyTongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyWuhanHubeiChina
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18
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Jahagirdar V, Bapaye J, Chandan S, Ponnada S, Kochhar GS, Navaneethan U, Mohan BP. Diagnostic accuracy of convolutional neural network-based machine learning algorithms in endoscopic severity prediction of ulcerative colitis: a systematic review and meta-analysis. Gastrointest Endosc 2023; 98:145-154.e8. [PMID: 37094691 DOI: 10.1016/j.gie.2023.04.2074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/06/2023] [Accepted: 04/16/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND AND AIMS Endoscopic assessment of ulcerative colitis (UC) can be performed by using the Mayo Endoscopic Score (MES) or the Ulcerative Colitis Endoscopic Index of Severity (UCEIS). In this meta-analysis, we assessed the pooled diagnostic accuracy parameters of deep machine learning by means of convolutional neural network (CNN) algorithms in predicting UC severity on endoscopic images. METHODS Databases including MEDLINE, Scopus, and Embase were searched in June 2022. Outcomes of interest were the pooled accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Standard meta-analysis methods used the random-effects model, and heterogeneity was assessed using the I2statistics. RESULTS Twelve studies were included in the final analysis. The pooled diagnostic parameters of CNN-based machine learning algorithms in endoscopic severity assessment of UC were as follows: accuracy 91.5% (95% confidence interval [CI], 88.3-93.8; I2 = 84%), sensitivity 82.8% (95% CI, 78.3-86.5; I2 = 89%), specificity 92.4% (95% CI, 89.4-94.6; I2 = 84%), PPV 86.6% (95% CI, 82.3-90; I2 = 89%), and NPV 88.6% (95% CI, 85.7-91; I2 = 78%). Subgroup analysis revealed significantly better sensitivity and PPV with the UCEIS scoring system compared with the MES (93.6% [95% CI, 87.5-96.8; I2 = 77%] vs 82% [95% CI, 75.6-87; I2 = 89%], P = .003, and 93.6% [95% CI, 88.7-96.4; I2 = 68%] vs 83.6% [95% CI, 76.8-88.8; I2 = 77%], P = .007, respectively). CONCLUSIONS CNN-based machine learning algorithms demonstrated excellent pooled diagnostic accuracy parameters in the endoscopic severity assessment of UC. Using UCEIS scores in CNN training might offer better results than the MES. Further studies are warranted to establish these findings in real clinical settings.
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Affiliation(s)
- Vinay Jahagirdar
- Department of Internal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, Missouri, USA
| | - Jay Bapaye
- Department of Internal Medicine, Rochester General Hospital, Rochester, New York, USA
| | - Saurabh Chandan
- Department of Gastroenterology, Creighton University Medical Center, Creighton, Nebraska, USA
| | - Suresh Ponnada
- Internal Medicine, Roanoke Carilion Hospital, Roanoke, Virginia, USA
| | - Gursimran S Kochhar
- Department of Gastroenterology & Hepatology, Allegheny Health Network, Pittsburgh, Pennsylvania, USA
| | | | - Babu P Mohan
- Department of Gastroenterology & Hepatology, University of Utah, Salt Lake City, Utah, USA
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19
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Biamonte P, D’Amico F, Fasulo E, Barà R, Bernardi F, Allocca M, Zilli A, Danese S, Furfaro F. New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients. Biomedicines 2023; 11:2139. [PMID: 37626636 PMCID: PMC10452412 DOI: 10.3390/biomedicines11082139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Ulcerative colitis (UC) is a chronic inflammatory bowel disease primarily affecting the colon and rectum. Endoscopy plays a crucial role in the diagnosis and management of UC. Recent advancements in endoscopic technology, including chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and the use of artificial intelligence, have revolutionized the assessment and treatment of UC patients. These innovative techniques enable early detection of dysplasia and cancer, more precise characterization of disease extent and severity and more targeted biopsies, leading to improved diagnosis and disease monitoring. Furthermore, these advancements have significant implications for therapeutic decision making, empowering clinicians to carefully consider a range of treatment options, including pharmacological therapies, endoscopic interventions and surgical approaches. In this review, we provide an overview of the latest endoscopic technologies and their applications for diagnosing and monitoring UC. We also discuss their impact on treatment decision making, highlighting the potential benefits and limitations of each technique.
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Affiliation(s)
- Paolo Biamonte
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Ferdinando D’Amico
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
| | - Ernesto Fasulo
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Rukaia Barà
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Francesca Bernardi
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Mariangela Allocca
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Alessandra Zilli
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Gastroenterology and Endoscopy, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Federica Furfaro
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
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20
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Zulqarnain F, Rhoads SF, Syed S. Machine and deep learning in inflammatory bowel disease. Curr Opin Gastroenterol 2023; 39:294-300. [PMID: 37144491 PMCID: PMC10256313 DOI: 10.1097/mog.0000000000000945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
PURPOSE OF REVIEW The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients. RECENT FINDINGS Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment. SUMMARY Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.
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Affiliation(s)
- Fatima Zulqarnain
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, Virginia, USA
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21
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Naz J, Sharif MI, Sharif MI, Kadry S, Rauf HT, Ragab AE. A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features. Biomedicines 2023; 11:1723. [PMID: 37371819 DOI: 10.3390/biomedicines11061723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/23/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth-Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM's accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.
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Affiliation(s)
- Javeria Naz
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
| | - Muhammad Imran Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
| | - Muhammad Irfan Sharif
- Department of Computer Science, University of Education Lahore, Jauharabad Campus, Lahore 54770, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
- MEU Research Unit, Middle East University, Amman 11831, Jordan
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
| | - Adham E Ragab
- Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
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22
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Najdawi F, Sucipto K, Mistry P, Hennek S, Jayson CKB, Lin M, Fahy D, Kinsey S, Wapinski I, Beck AH, Resnick MB, Khosla A, Drage MG. Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology. Mod Pathol 2023; 36:100124. [PMID: 36841434 DOI: 10.1016/j.modpat.2023.100124] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/23/2022] [Accepted: 01/28/2023] [Indexed: 02/17/2023]
Abstract
Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.
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Affiliation(s)
| | | | | | | | | | - Mary Lin
- PathAI, Inc, Boston, Massachusetts
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23
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Artificial intelligence evaluation of COVID-19 restrictions and speech therapy effects on the autistic children's behavior. Sci Rep 2023; 13:4312. [PMID: 36922527 PMCID: PMC10016168 DOI: 10.1038/s41598-022-25902-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 12/06/2022] [Indexed: 03/17/2023] Open
Abstract
In the present study, we aimed to quantify the effects of COVID-19 restrictions and speech treatment approaches during lockdowns on autistic children using CBCL and neuro-fuzzy artificial intelligence method. In this regard, a survey including CBCL questionnaire is prepared using online forms. In total, 87 children with diagnosed Autism spectrum disorders (ASD) participated in the survey. The influences of three treatment approaches of in-person, telehealth and public services along with no-treatment condition during lockdown were the main factors of the investigation. The main output factors were internalized and externalized problems in general and their eight subcategory syndromes. We examined the reports by parents/caregivers to find correlation between treatments and CBCL listed problems. Moreover, comparison of the eight syndromes rating scores from pre-lockdown to post-lockdown periods were performed. In addition, artificial intelligence method were engaged to find the influence of speech treatment during restrictions on the level of internalizing and externalizing problems. In this regard, a fully connected adaptive neuro fuzzy inference system is employed with type and duration of treatments as input and T-scores of the syndromes are the output of the network. The results indicate that restrictions alleviate externalizing problems while intensifying internalizing problems. In addition, it is concluded that in-person speech therapy is the most effective and satisfactory approach to deal with ASD children during stay-at-home periods.
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24
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Augustin J, McLellan PT, Calderaro J. Mise au point de l’utilisation de l’intelligence artificielle dans la prise en charge des maladies inflammatoires chroniques de l’intestin. Ann Pathol 2023:S0242-6498(23)00075-5. [PMID: 36997441 DOI: 10.1016/j.annpat.2023.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 03/30/2023]
Abstract
Complexity of inflammatory bowel diseases (IBD) lies on their management and their biology. Clinics, blood and fecal samples tests, endoscopy and histology are the main tools guiding IBD treatment, but they generate a large amount of data, difficult to analyze by clinicians. Because of its capacity to analyze large number of data, artificial intelligence is currently generating enthusiasm in medicine, and this technology could be used to improve IBD management. In this review, after a short summary on IBD management and artificial intelligence, we will report pragmatic examples of artificial intelligence utilisation in IBD. Lastly, we will discuss the limitations of this technology.
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Affiliation(s)
- Jérémy Augustin
- Département de pathologie, hôpital universitaire Henri-Mondor, assistance publique-hôpitaux de Paris, Créteil, France; Inserm U955 Team 18, université Paris-Est-Créteil, faculté de Médecine, Créteil, France.
| | - Paul Thomas McLellan
- Département de gastroentérologie, hôpital Saint-Antoine, assistance publique-hôpitaux de Paris, Sorbonne université, Paris, France
| | - Julien Calderaro
- Département de pathologie, hôpital universitaire Henri-Mondor, assistance publique-hôpitaux de Paris, Créteil, France; Inserm U955 Team 18, université Paris-Est-Créteil, faculté de Médecine, Créteil, France
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25
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Houwen BBSL, Nass KJ, Vleugels JLA, Fockens P, Hazewinkel Y, Dekker E. Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability. Gastrointest Endosc 2023; 97:184-199.e16. [PMID: 36084720 DOI: 10.1016/j.gie.2022.08.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 01/28/2023]
Abstract
BACKGROUND AND AIMS Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This review aimed to describe the availability, accessibility, and usability of publicly available colonoscopic imaging databases, focusing on polyp detection, polyp characterization, and quality of colonoscopy. METHODS A systematic literature search was performed in MEDLINE and Embase to identify AI studies describing publicly available colonoscopic imaging databases published after 2010. Second, a targeted search using Google's Dataset Search, Google Search, GitHub, and Figshare was done to identify databases directly. Databases were included if they contained data about polyp detection, polyp characterization, or quality of colonoscopy. To assess accessibility of databases, the following categories were defined: open access, open access with barriers, and regulated access. To assess the potential usability of the included databases, essential details of each database were extracted using a checklist derived from the Checklist for Artificial Intelligence in Medical Imaging. RESULTS We identified 22 databases with open access, 3 databases with open access with barriers, and 15 databases with regulated access. The 22 open access databases contained 19,463 images and 952 videos. Nineteen of these databases focused on polyp detection, localization, and/or segmentation; 6 on polyp characterization, and 3 on quality of colonoscopy. Only half of these databases have been used by other researcher to develop, train, or benchmark their AI system. Although technical details were in general well reported, important details such as polyp and patient demographics and the annotation process were under-reported in almost all databases. CONCLUSIONS This review provides greater insight on public availability of colonoscopic imaging databases for AI research. Incomplete reporting of important details limits the ability of researchers to assess the usability of current databases.
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Affiliation(s)
- Britt B S L Houwen
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Karlijn J Nass
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jasper L A Vleugels
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Paul Fockens
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Yark Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud University Nijmegen Medical Center, Radboud University of Nijmegen, Nijmegen, the Netherlands
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centres, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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26
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Guimarães P, Finkler H, Reichert MC, Zimmer V, Grünhage F, Krawczyk M, Lammert F, Keller A, Casper M. Artificial-intelligence-based decision support tools for the differential diagnosis of colitis. Eur J Clin Invest 2023; 53:e13960. [PMID: 36721878 DOI: 10.1111/eci.13960] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 12/19/2022] [Accepted: 01/21/2023] [Indexed: 02/02/2023]
Abstract
BACKGROUND Whereas Artificial Intelligence (AI) based tools have recently been introduced in the field of gastroenterology, application in inflammatory bowel disease (IBD) is in its infancies. We established AI-based algorithms to distinguish IBD from infectious and ischemic colitis using endoscopic images and clinical data. METHODS First, we trained and tested a Convolutional Neural Network (CNN) using 1796 real-world images from 494 patients, presenting with three diseases (IBD [n = 212], ischemic colitis [n = 157], and infectious colitis [n = 125]). Moreover, we evaluated a Gradient Boosted Decision Trees (GBDT) algorithm using five clinical parameters as well as a hybrid approach (CNN + GBDT). Patients and images were randomly split into two completely independent datasets. The proposed approaches were benchmarked against each other and three expert endoscopists on the test set. RESULTS For the image-based CNN, the GBDT algorithm and the hybrid approach global accuracies were .709, .792, and .766, respectively. Positive predictive values were .602, .702, and .657. Global areas under the receiver operating characteristics (ROC) and precision recall (PR) curves were .727/.585, .888/.823, and .838/.733, respectively. Global accuracy did not differ between CNN and endoscopists (.721), but the clinical parameter-based GBDT algorithm outperformed CNN and expert image classification. CONCLUSIONS Decision support systems exclusively based on endoscopic image analysis for the differential diagnosis of colitis, representing a complex clinical challenge, seem not yet to be ready for primetime and more diverse image datasets may be necessary to improve performance in future development. The clinical value of the proposed clinical parameters algorithm should be evaluated in prospective cohorts.
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Affiliation(s)
- Pedro Guimarães
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.,Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Helen Finkler
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | | | - Vincent Zimmer
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany.,Department of Medicine, Knappschaft Hospital Saar, Püttlingen, Germany
| | - Frank Grünhage
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | - Marcin Krawczyk
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
| | - Frank Lammert
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany.,Chair for Health Sciences, Hannover Medical School (MHH), Hannover, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.,Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, California, USA
| | - Markus Casper
- Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
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27
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A Deep-Learning Approach for Identifying and Classifying Digestive Diseases. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
The digestive tract, often known as the gastrointestinal (GI) tract or the gastrointestinal system, is affected by digestive ailments. The stomach, large and small intestines, liver, pancreas and gallbladder are all components of the digestive tract. A digestive disease is any illness that affects the digestive system. Serious to moderate conditions can exist. Heartburn, cancer, irritable bowel syndrome (IBS) and lactose intolerance are only a few of the frequent issues. The digestive system may be treated with many different surgical treatments. Laparoscopy, open surgery and endoscopy are a few examples of these techniques. This paper proposes transfer-learning models with different pre-trained models to identify and classify digestive diseases. The proposed systems showed an increase in metrics, such as the accuracy, precision and recall, when compared with other state-of-the-art methods, and EfficientNetB0 achieved the best performance results of 98.01% accuracy, 98% precision and 98% recall.
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28
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Chaddad A, Peng J, Xu J, Bouridane A. Survey of Explainable AI Techniques in Healthcare. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020634. [PMID: 36679430 PMCID: PMC9862413 DOI: 10.3390/s23020634] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/14/2022] [Accepted: 12/29/2022] [Indexed: 05/27/2023]
Abstract
Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whether a patient is sick before forming a reasonable explanation based on the patient's symptoms and/or an examination. Therefore, to be a viable and accepted tool, AI needs to mimic human judgment and interpretation skills. Specifically, explainable AI (XAI) aims to explain the information behind the black-box model of deep learning that reveals how the decisions are made. This paper provides a survey of the most recent XAI techniques used in healthcare and related medical imaging applications. We summarize and categorize the XAI types, and highlight the algorithms used to increase interpretability in medical imaging topics. In addition, we focus on the challenging XAI problems in medical applications and provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis. Furthermore, this survey provides future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical imaging.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China
- The Laboratory for Imagery Vision and Artificial Intelligence, Ecole de Technologie Superieure, 1100 Rue Notre Dame O, Montreal, QC H3C 1K3, Canada
| | - Jihao Peng
- School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China
| | - Jian Xu
- School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China
| | - Ahmed Bouridane
- Centre for Data Analytics and Cybersecurity, University of Sharjah, Sharjah 27272, United Arab Emirates
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29
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Chang Y, Wang Z, Sun HB, Li YQ, Tang TY. Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Advanced Development and New Horizons. Gastroenterol Res Pract 2023; 2023:3228832. [PMID: 37101782 PMCID: PMC10125749 DOI: 10.1155/2023/3228832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/28/2022] [Accepted: 12/02/2022] [Indexed: 04/28/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a complex chronic immune disease with two subtypes: Crohn's disease and ulcerative colitis. Considering the differences in pathogenesis, etiology, clinical presentation, and response to therapy among patients, gastroenterologists mainly rely on endoscopy to diagnose and treat IBD during clinical practice. However, as exemplified by the increasingly comprehensive ulcerative colitis endoscopic scoring system, the endoscopic diagnosis, evaluation, and treatment of IBD still rely on the subjective manipulation and judgment of endoscopists. In recent years, the use of artificial intelligence (AI) has grown substantially in various medical fields, and an increasing number of studies have investigated the use of this emerging technology in the field of gastroenterology. Clinical applications of AI have focused on IBD pathogenesis, etiology, diagnosis, and patient prognosis. Large-scale datasets offer tremendous utility in the development of novel tools to address the unmet clinical and practice needs for treating patients with IBD. However, significant differences among AI methodologies, datasets, and clinical findings limit the incorporation of AI technology into clinical practice. In this review, we discuss practical AI applications in the diagnosis of IBD via gastroenteroscopy and speculate regarding a future in which AI technology provides value for the diagnosis and treatment of IBD patients.
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Affiliation(s)
- Yu Chang
- Department of Gastroenterology, First Hospital of Jilin University, Changchun, 130000 Jilin, China
| | - Zhi Wang
- Department of Gastroenterology, First Hospital of Jilin University, Changchun, 130000 Jilin, China
| | - Hai-Bo Sun
- Department of Gastroenterology, First Hospital of Jilin University, Changchun, 130000 Jilin, China
| | - Yu-Qin Li
- Department of Gastroenterology, First Hospital of Jilin University, Changchun, 130000 Jilin, China
| | - Tong-Yu Tang
- Department of Gastroenterology, First Hospital of Jilin University, Changchun, 130000 Jilin, China
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Fati SM, Senan EM, Azar AT. Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22114079. [PMID: 35684696 PMCID: PMC9185306 DOI: 10.3390/s22114079] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 05/27/2023]
Abstract
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the survival of patients. Additionally, removing benign tumors in their early stages has more risks than benefits. Video endoscopy technology is essential for imaging the GI tract and identifying disorders such as bleeding, ulcers, polyps, and malignant tumors. Videography generates 5000 frames, which require extensive analysis and take a long time to follow all frames. Thus, artificial intelligence techniques, which have a higher ability to diagnose and assist physicians in making accurate diagnostic decisions, solve these challenges. In this study, many multi-methodologies were developed, where the work was divided into four proposed systems; each system has more than one diagnostic method. The first proposed system utilizes artificial neural networks (ANN) and feed-forward neural networks (FFNN) algorithms based on extracting hybrid features by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and fuzzy color histogram (FCH) algorithms. The second proposed system uses pre-trained CNN models which are the GoogLeNet and AlexNet based on the extraction of deep feature maps and their classification with high accuracy. The third proposed method uses hybrid techniques consisting of two blocks: the first block of CNN models (GoogLeNet and AlexNet) to extract feature maps; the second block is the support vector machine (SVM) algorithm for classifying deep feature maps. The fourth proposed system uses ANN and FFNN based on the hybrid features between CNN models (GoogLeNet and AlexNet) and LBP, GLCM and FCH algorithms. All the proposed systems achieved superior results in diagnosing endoscopic images for the early detection of lower gastrointestinal diseases. All systems produced promising results; the FFNN classifier based on the hybrid features extracted by GoogLeNet, LBP, GLCM and FCH achieved an accuracy of 99.3%, precision of 99.2%, sensitivity of 99%, specificity of 100%, and AUC of 99.87%.
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Affiliation(s)
- Suliman Mohamed Fati
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
| | - Ebrahim Mohammed Senan
- Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India;
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
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Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification. SENSORS 2022; 22:s22072801. [PMID: 35408415 PMCID: PMC9003289 DOI: 10.3390/s22072801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/26/2022] [Accepted: 04/02/2022] [Indexed: 01/10/2023]
Abstract
Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.
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