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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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Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions. Medicina (B Aires) 2023; 59:medicina59010172. [PMID: 36676796 PMCID: PMC9865285 DOI: 10.3390/medicina59010172] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 01/17/2023] Open
Abstract
Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn's disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision-recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm's accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.
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Medical Applications of Artificial Intelligence (Legal Aspects and Future Prospects). LAWS 2021. [DOI: 10.3390/laws11010003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background: Cutting-edge digital technologies are being actively introduced into healthcare. The recent successful efforts of artificial intelligence in diagnosing, predicting and studying diseases, as well as in surgical assisting demonstrate its high efficiency. The AI’s ability to promptly take decisions and learn independently has motivated large corporations to focus on its development and gradual introduction into everyday life. Legal aspects of medical activities are of particular importance, yet the legal regulation of AI’s performance in healthcare is still in its infancy. The state is to a considerable extent responsible for the formation of a legal regime that would meet the needs of modern society (digital society). Objective: This study aims to determine the possible modes of AI’s functioning, to identify the participants in medical-legal relations, to define the legal personality of AI and circumscribe the scope of its competencies. Of importance is the issue of determining the grounds for imposing legal liability on persons responsible for the performance of an AI system. Results: The present study identifies the prospects for a legal assessment of AI applications in medicine. The article reviews the sources of legal regulation of AI, including the unique sources of law sanctioned by the state. Particular focus is placed on medical-legal customs and medical practices. Conclusions: The presented analysis has allowed formulating the approaches to the legal regulation of AI in healthcare.
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Hatta W, Koike T, Ogata Y, Kondo Y, Ara N, Uno K, Asano N, Imatani A, Masamune A. Comparison of Magnifying Endoscopy with Blue Light Imaging and Narrow Band Imaging for Determining the Invasion Depth of Superficial Esophageal Squamous Cell Carcinoma by the Japanese Esophageal Society's Intrapapillary Capillary Loop Classification. Diagnostics (Basel) 2021; 11:diagnostics11111941. [PMID: 34829288 PMCID: PMC8625194 DOI: 10.3390/diagnostics11111941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/12/2021] [Accepted: 10/16/2021] [Indexed: 12/24/2022] Open
Abstract
Blue light imaging (BLI) and narrow-band imaging (NBI) are two modalities that enable narrow-band light observation. We aimed to compare the diagnostic ability of magnifying endoscopy with BLI (ME-BLI) and NBI (ME-NBI) for determining the invasion depth of superficial esophageal squamous cell carcinoma (SESCC) by the Japanese Esophageal Society’s intrapapillary capillary loop (IPCL) classification. We enrolled 81 patients between 2014 and 2018, and the still endoscopic images for diagnosing the invasion depth at the same part in ME-BLI and ME-NBI were registered. Two blinded investigators reviewed them and diagnosed the invasion depth by the IPCL classification. Subsequently, the diagnostic yields in two modalities were compared. The overall accuracies for the invasion depth by the IPCL classification in ME-BLI and ME-NBI did not differ significantly (67.9–71.6% vs. 72.8–74.1%). In the analysis based on the invasion depth, the sensitivities and positive predictive values in tumors invading the muscularis mucosa or submucosa ≤200 µm were low (23.1–30.8% and 16.7–25.0%, respectively) in both modalities. In conclusion, the diagnostic ability for determining the invasion depth of SESCC by the IPCL classification was relatively similar in ME-BLI and ME-NBI, but diagnosis by magnifying endoscopy alone might not be satisfactory.
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Affiliation(s)
- Waku Hatta
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (T.K.); (Y.O.); (K.U.); (N.A.); (A.I.); (A.M.)
- Correspondence: ; Tel.: +81-22-717-7171; Fax: +81-22-717-7177
| | - Tomoyuki Koike
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (T.K.); (Y.O.); (K.U.); (N.A.); (A.I.); (A.M.)
| | - Yohei Ogata
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (T.K.); (Y.O.); (K.U.); (N.A.); (A.I.); (A.M.)
| | - Yutaka Kondo
- Division of Gastroenterology, Tohoku Rosai Hospital, 4-3-21 Dainohara, Aoba-ku, Sendai 981-8563, Japan;
| | - Nobuyuki Ara
- National Hospital Organization Sendai Medical Center, Department of Gastroenterology, 2-11-12 Miyagino, Miyagino-ku, Sendai 983-8520, Japan;
| | - Kaname Uno
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (T.K.); (Y.O.); (K.U.); (N.A.); (A.I.); (A.M.)
| | - Naoki Asano
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (T.K.); (Y.O.); (K.U.); (N.A.); (A.I.); (A.M.)
| | - Akira Imatani
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (T.K.); (Y.O.); (K.U.); (N.A.); (A.I.); (A.M.)
| | - Atsushi Masamune
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (T.K.); (Y.O.); (K.U.); (N.A.); (A.I.); (A.M.)
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