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Mota J, Almeida MJ, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Cardoso H, Andrade P, Ferreira J, Mascarenhas M, Macedo G. From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy? Diagnostics (Basel) 2024; 14:291. [PMID: 38337807 PMCID: PMC10855436 DOI: 10.3390/diagnostics14030291] [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: 11/13/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
- Digestive Artificial Intelligence Development, R. Alfredo Allen 455-461, 4200-135 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- ManopH Gastroenterology Clinic, R. de Sá da Bandeira 752, 4000-432 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Popa SL, Stancu B, Ismaiel A, Turtoi DC, Brata VD, Duse TA, Bolchis R, Padureanu AM, Dita MO, Bashimov A, Incze V, Pinna E, Grad S, Pop AV, Dumitrascu DI, Munteanu MA, Surdea-Blaga T, Mihaileanu FV. Enteroscopy versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders-A Comparative Analysis of Artificial Intelligence Applications. Biomedicines 2023; 11:2991. [PMID: 38001991 PMCID: PMC10669430 DOI: 10.3390/biomedicines11112991] [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: 10/04/2023] [Revised: 10/26/2023] [Accepted: 11/05/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Small bowel disorders present a diagnostic challenge due to the limited accessibility of the small intestine. Accurate diagnosis is made with the aid of specific procedures, like capsule endoscopy or double-ballon enteroscopy, but they are not usually solicited and not widely accessible. This study aims to assess and compare the diagnostic effectiveness of enteroscopy and video capsule endoscopy (VCE) when combined with artificial intelligence (AI) algorithms for the automatic detection of small bowel diseases. MATERIALS AND METHODS We performed an extensive literature search for relevant studies about AI applications capable of identifying small bowel disorders using enteroscopy and VCE, published between 2012 and 2023, employing PubMed, Cochrane Library, Google Scholar, Embase, Scopus, and ClinicalTrials.gov databases. RESULTS Our investigation discovered a total of 27 publications, out of which 21 studies assessed the application of VCE, while the remaining 6 articles analyzed the enteroscopy procedure. The included studies portrayed that both investigations, enhanced by AI, exhibited a high level of diagnostic accuracy. Enteroscopy demonstrated superior diagnostic capability, providing precise identification of small bowel pathologies with the added advantage of enabling immediate therapeutic intervention. The choice between these modalities should be guided by clinical context, patient preference, and resource availability. Studies with larger sample sizes and prospective designs are warranted to validate these results and optimize the integration of AI in small bowel diagnostics. CONCLUSIONS The current analysis demonstrates that both enteroscopy and VCE with AI augmentation exhibit comparable diagnostic performance for the automatic detection of small bowel disorders.
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Affiliation(s)
- Stefan Lucian Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Bogdan Stancu
- 2nd Surgical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania;
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Daria Claudia Turtoi
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Vlad Dumitru Brata
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Traian Adrian Duse
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Roxana Bolchis
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Alexandru Marius Padureanu
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Miruna Oana Dita
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Atamyrat Bashimov
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Victor Incze
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Edoardo Pinna
- Faculty of Medicine, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.D.B.); (T.A.D.); (R.B.); (A.M.P.); (M.O.D.); (A.B.); (V.I.); (E.P.)
| | - Simona Grad
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Andrei-Vasile Pop
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, “Iuliu Hatieganu“ University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania;
| | - Mihai Alexandru Munteanu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania;
| | - Teodora Surdea-Blaga
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (S.L.P.); (A.I.); (S.G.); (A.-V.P.); (T.S.-B.)
| | - Florin Vasile Mihaileanu
- 2nd Surgical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania;
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Minchenberg SB, Walradt T, Glissen Brown JR. Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy. World J Gastrointest Oncol 2022; 14:989-1001. [PMID: 35646286 PMCID: PMC9124983 DOI: 10.4251/wjgo.v14.i5.989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/21/2021] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.
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Affiliation(s)
- Scott B Minchenberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Trent Walradt
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Jeremy R Glissen Brown
- Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
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Chetcuti Zammit S, Sidhu R. Artificial intelligence within the small bowel: are we lagging behind? Curr Opin Gastroenterol 2022; 38:307-317. [PMID: 35645023 DOI: 10.1097/mog.0000000000000827] [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] [Indexed: 12/10/2022]
Abstract
PURPOSE OF REVIEW The use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date. RECENT FINDINGS The diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician. SUMMARY Artificial intelligence is in built within the Navicam-Ankon capsule endoscopy reading system. This development will no doubt expand to other capsule endoscopy platforms and capsule endoscopies that are used to visualize other parts of the gastrointestinal tract as a standard. This wireless and patient friendly technique combined with rapid reading platforms with the help of artificial intelligence will become an attractive and viable choice to alter how patients are investigated in the future.
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Affiliation(s)
| | - Reena Sidhu
- Academic Department of Gastroenterology, Royal Hallamshire Hospital
- Academic Unit of Gastroenterology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
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Yang Y, Li YX, Yao RQ, Du XH, Ren C. Artificial intelligence in small intestinal diseases: Application and prospects. World J Gastroenterol 2021; 27:3734-3747. [PMID: 34321840 PMCID: PMC8291013 DOI: 10.3748/wjg.v27.i25.3734] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/08/2021] [Indexed: 02/06/2023] Open
Abstract
The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.
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Affiliation(s)
- Yu Yang
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Yu-Xuan Li
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ren-Qi Yao
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
- Department of Burn Surgery, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Xiao-Hui Du
- Department of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Chao Ren
- Trauma Research Center, The Fourth Medical Center and Medical Innovation Research Division of the Chinese People‘s Liberation Army General Hospital, Beijing 100048, China
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Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27:1664-1690. [PMID: 33967550 PMCID: PMC8072192 DOI: 10.3748/wjg.v27.i16.1664] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/11/2021] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.
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Affiliation(s)
- Jia-Sheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Zi-Yi Lu
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Ming-Yu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Sarun Juengpanich
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Jia-Hao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Shi-Jie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Win Topatana
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xue-Yin Zhou
- School of Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - Xu Feng
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Liang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Yu Liu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xiu-Jun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
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Chetcuti Zammit S, Bull LA, Sanders DS, Galvin J, Dervilis N, Sidhu R, Worden K. Towards the Probabilistic Analysis of Small Bowel Capsule Endoscopy Features to Predict Severity of Duodenal Histology in Patients with Villous Atrophy. J Med Syst 2020; 44:195. [PMID: 33005996 PMCID: PMC7529615 DOI: 10.1007/s10916-020-01657-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 09/16/2020] [Indexed: 12/17/2022]
Abstract
Small bowel capsule endoscopy (SBCE) can be complementary to histological assessment of celiac disease (CD) and serology negative villous atrophy (SNVA). Determining the severity of disease on SBCE using statistical machine learning methods can be useful in the follow up of patients. SBCE can play an additional role in differentiating between CD and SNVA. De-identified SBCEs of patients with CD and SNVA were included. Probabilistic analysis of features on SBCE were used to predict severity of duodenal histology and to distinguish between CD and SNVA. Patients with higher Marsh scores were more likely to have a positive SBCE and a continuous distribution of macroscopic features of disease than those with lower Marsh scores. The same pattern was also true for patients with CD when compared to patients with SNVA. The validation accuracy when predicting the severity of Marsh scores and when distinguishing between CD and SNVA was 69.1% in both cases. When the proportions of each SBCE class group within the dataset were included in the classification model, to distinguish between the two pathologies, the validation accuracy increased to 75.3%. The findings of this work suggest that by using features of CD and SNVA on SBCE, predictions can be made of the type of pathology and the severity of disease.
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Affiliation(s)
- Stefania Chetcuti Zammit
- Academic Unit, Department of Gastroenterology, Sheffield Teaching Hospitals, Sheffield, UK.
- Gastroenterology Department, Royal Hallamshire Hospital, Glossop Road, Sheffield, S102JF, UK.
| | - Lawrence A Bull
- Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - David S Sanders
- Academic Unit, Department of Gastroenterology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Jessica Galvin
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nikolaos Dervilis
- Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Reena Sidhu
- Academic Unit, Department of Gastroenterology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Keith Worden
- Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
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Rahim T, Usman MA, Shin SY. A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging. Comput Med Imaging Graph 2020; 85:101767. [DOI: 10.1016/j.compmedimag.2020.101767] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 07/13/2020] [Accepted: 07/18/2020] [Indexed: 12/12/2022]
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Saito H, Aoki T, Aoyama K, Kato Y, Tsuboi A, Yamada A, Fujishiro M, Oka S, Ishihara S, Matsuda T, Nakahori M, Tanaka S, Koike K, Tada T. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2020; 92:144-151.e1. [PMID: 32084410 DOI: 10.1016/j.gie.2020.01.054] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/31/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Protruding lesions of the small bowel vary in wireless capsule endoscopy (WCE) images, and their automatic detection may be difficult. We aimed to develop and test a deep learning-based system to automatically detect protruding lesions of various types in WCE images. METHODS We trained a deep convolutional neural network (CNN), using 30,584 WCE images of protruding lesions from 292 patients. We evaluated CNN performance by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, using an independent set of 17,507 test images from 93 patients, including 7507 images of protruding lesions from 73 patients. RESULTS The developed CNN analyzed 17,507 images in 530.462 seconds. The AUC for detection of protruding lesions was 0.911 (95% confidence interval [Cl], 0.9069-0.9155). The sensitivity and specificity of the CNN were 90.7% (95% CI, 90.0%-91.4%) and 79.8% (95% CI, 79.0%-80.6%), respectively, at the optimal cut-off value of 0.317 for probability score. In a subgroup analysis of the category of protruding lesions, the sensitivities were 86.5%, 92.0%, 95.8%, 77.0%, and 94.4% for the detection of polyps, nodules, epithelial tumors, submucosal tumors, and venous structures, respectively. In individual patient analyses (n = 73), the detection rate of protruding lesions was 98.6%. CONCLUSION We developed and tested a new computer-aided system based on a CNN to automatically detect various protruding lesions in WCE images. Patient-level analyses with larger cohorts and efforts to achieve better diagnostic performance are necessary in further studies.
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Affiliation(s)
- Hiroaki Saito
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan.
| | - Tomonori Aoki
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | | | - Akiyoshi Tsuboi
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Atsuo Yamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Shiro Oka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | - Tomoki Matsuda
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Masato Nakahori
- Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
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Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM. SENSORS 2020; 20:s20051447. [PMID: 32155811 PMCID: PMC7085772 DOI: 10.3390/s20051447] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 02/21/2020] [Accepted: 03/03/2020] [Indexed: 12/13/2022]
Abstract
Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.
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