Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Iwagami H, Ishihara R, Aoyama K, Fukuda H, Shimamoto Y, Kono M, Nakahira H, Matsuura N, Shichijo S, Kanesaka T, Kanzaki H, Ishii T, Nakatani Y, Tada T. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol 2021;36:131-136. [PMID: 32511793 DOI: 10.1111/jgh.15136] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/03/2020] [Accepted: 06/05/2020] [Indexed: 12/12/2022]

For:	Iwagami H, Ishihara R, Aoyama K, Fukuda H, Shimamoto Y, Kono M, Nakahira H, Matsuura N, Shichijo S, Kanesaka T, Kanzaki H, Ishii T, Nakatani Y, Tada T. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol 2021;36:131-136. [PMID: 32511793 DOI: 10.1111/jgh.15136] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/03/2020] [Accepted: 06/05/2020] [Indexed: 12/12/2022]

Number

Cited by Other Article(s)

Tun HM, Rahman HA, Naing L, Malik OA. Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review. BMC Cancer 2025;25:703. [PMID: 40234807 PMCID: PMC12001681 DOI: 10.1186/s12885-025-14026-x] [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: 09/27/2024] [Accepted: 03/26/2025] [Indexed: 04/17/2025] Open

Abstract

BACKGROUND

Cancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as a tool for cancer screening, its utilization in the ASEAN region is unexplored.

PURPOSE

This study aims to identify and evaluate different cancer screening programs across ASEAN, with a focus on assessing the integration and impact of AI in these programs.

METHODS

A scoping review was conducted using PRISMA-ScR guidelines to provide a comprehensive overview of cancer screening programs and AI usage across ASEAN. Data were collected from government health ministries, official guidelines, literature databases, and relevant documents. The use of AI in cancer screening reviews involved searches through PubMed, Scopus, and Google Scholar with the inclusion criteria of only included studies that utilized data from the ASEAN region from January 2019 to May 2024.

RESULTS

The findings reveal diverse cancer screening approaches in ASEAN. Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia and Timor-Leste primarily adopt opportunistic screening, while Singapore, Malaysia, and Thailand focus on organized programs. Cervical cancer screening is widespread, using both opportunistic and organized methods. Fourteen studies were included in the scoping review, covering breast (5 studies), cervical (2 studies), colon (4 studies), hepatic (1 study), lung (1 study), and oral (1 study) cancers. Studies revealed that different stages of AI integration for cancer screening: prospective clinical evaluation (50%), silent trial (36%) and exploratory model development (14%), with promising results in enhancing cancer screening accuracy and efficiency.

CONCLUSION

Cancer screening programs in the ASEAN region require more organized approaches targeting appropriate age groups at regular intervals to meet the WHO's 2030 screening targets. Efforts to integrate AI in Singapore, Malaysia, Vietnam, Thailand, and Indonesia show promise in optimizing screening processes, reducing costs, and improving early detection. AI technology integration enhances cancer identification accuracy during screening, improving early detection and cancer management across the ASEAN region.

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Enslin S, Kaul V. Past, Present, and Future: A History Lesson in Artificial Intelligence. Gastrointest Endosc Clin N Am 2025;35:265-278. [PMID: 40021228 DOI: 10.1016/j.giec.2024.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]

Meinikheim M, Mendel R, Palm C, Probst A, Muzalyova A, Scheppach MW, Nagl S, Schnoy E, Römmele C, Schulz DAH, Schlottmann J, Prinz F, Rauber D, Rückert T, Matsumura T, Fernández-Esparrach G, Parsa N, Byrne MF, Messmann H, Ebigbo A. Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial. Endoscopy 2024;56:641-649. [PMID: 38547927 DOI: 10.1055/a-2296-5696] [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: 05/04/2024]

Affiliation(s)

Michael Meinikheim Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Robert Mendel Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Christoph Palm Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Andreas Probst Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Anna Muzalyova Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Markus W Scheppach Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Sandra Nagl Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Elisabeth Schnoy Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Christoph Römmele Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Dominik A H Schulz Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Jakob Schlottmann Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Friederike Prinz Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
David Rauber Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Tobias Rückert Regensburg Medical Image Computing, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany
Tomoaki Matsumura Department of Gastroenterology, Chiba University Graduate School of Medicine, Chiba, Japan
Glòria Fernández-Esparrach Endoscopy Unit, Gastroenterology Department, ICMDM, Hospital Clínic de Barcelona, Barcelona, Spain Faculty of Medicine, University of Barcelona, Barcelona, Spain Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
Nasim Parsa Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, United States Satisfai Health, Vancouver, Canada
Michael F Byrne Satisfai Health, Vancouver, Canada Gastroenterology, Vancouver General Hospital, The University of British Columbia, Vancouver, Canada
Helmut Messmann Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany
Alanna Ebigbo Department of Gastroenterology, University Hospital Augsburg, Augsburg, Germany

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Kikuchi R, Okamoto K, Ozawa T, Shibata J, Ishihara S, Tada T. Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms. Digestion 2024;105:419-435. [PMID: 39068926 DOI: 10.1159/000540251] [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: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/30/2024]

Abstract

BACKGROUND

Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated.

SUMMARY

In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation.

KEY MESSAGES

Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.

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Tao Y, Fang L, Qin G, Xu Y, Zhang S, Zhang X, Du S. Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer. Thorac Cancer 2024;15:1296-1304. [PMID: 38685604 PMCID: PMC11147664 DOI: 10.1111/1759-7714.15261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 05/02/2024] Open

Takeda T, Asaoka D, Ueyama H, Abe D, Suzuki M, Inami Y, Uemura Y, Yamamoto M, Iwano T, Uchida R, Utsunomiya H, Oki S, Suzuki N, Ikeda A, Akazawa Y, Matsumoto K, Ueda K, Hojo M, Nojiri S, Tada T, Nagahara A. Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett's Esophagus. J Clin Med 2024;13:1990. [PMID: 38610762 PMCID: PMC11012507 DOI: 10.3390/jcm13071990] [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: 03/01/2024] [Revised: 03/21/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open

Affiliation(s)

Tsutomu Takeda Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Daisuke Asaoka Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
Hiroya Ueyama Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Daiki Abe Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Maiko Suzuki Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
Yoshihiro Inami Department of Gastroenterology, Juntendo Tokyo Koto Geriatric Medical Center, Tokyo 136-0075, Japan; (D.A.); (M.S.); (Y.I.)
Yasuko Uemura Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Momoko Yamamoto Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Tomoyo Iwano Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Ryota Uchida Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Hisanori Utsunomiya Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Shotaro Oki Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Nobuyuki Suzuki Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Atsushi Ikeda Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Yoichi Akazawa Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Kohei Matsumoto Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Kumiko Ueda Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Mariko Hojo Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)
Shuko Nojiri Department of Medical Technology Innovation Center, Juntendo University School of Medicine, Tokyo 113-8421, Japan;
Tomohiro Tada AI Medical Service Inc., Tokyo 171-0013, Japan;
Akihito Nagahara Department of Gastroenterology, Juntendo University School of Medicine, Tokyo 113-8421, Japan; (H.U.); (D.A.); (Y.U.); (M.Y.); (T.I.); (R.U.); (H.U.); (S.O.); (N.S.); (A.I.); (Y.A.); (K.M.); (K.U.); (M.H.); (A.N.)

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Fockens KN, Jong MR, Jukema JB, Boers TGW, Kusters CHJ, van der Putten JA, Pouw RE, Duits LC, Montazeri NSM, van Munster SN, Weusten BLAM, Alvarez Herrero L, Houben MHMG, Nagengast WB, Westerhof J, Alkhalaf A, Mallant-Hent RC, Scholten P, Ragunath K, Seewald S, Elbe P, Baldaque-Silva F, Barret M, Ortiz Fernández-Sordo J, Villarejo GM, Pech O, Beyna T, van der Sommen F, de With PH, de Groof AJ, Bergman JJ. A deep learning system for detection of early Barrett's neoplasia: a model development and validation study. Lancet Digit Health 2023;5:e905-e916. [PMID: 38000874 DOI: 10.1016/s2589-7500(23)00199-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/22/2023] [Accepted: 09/18/2023] [Indexed: 11/26/2023]

Abstract

BACKGROUND

Computer-aided detection (CADe) systems could assist endoscopists in detecting early neoplasia in Barrett's oesophagus, which could be difficult to detect in endoscopic images. The aim of this study was to develop, test, and benchmark a CADe system for early neoplasia in Barrett's oesophagus.

METHODS

The CADe system was first pretrained with ImageNet followed by domain-specific pretraining with GastroNet. We trained the CADe system on a dataset of 14 046 images (2506 patients) of confirmed Barrett's oesophagus neoplasia and non-dysplastic Barrett's oesophagus from 15 centres. Neoplasia was delineated by 14 Barrett's oesophagus experts for all datasets. We tested the performance of the CADe system on two independent test sets. The all-comers test set comprised 327 (73 patients) non-dysplastic Barrett's oesophagus images, 82 (46 patients) neoplastic images, 180 (66 of the same patients) non-dysplastic Barrett's oesophagus videos, and 71 (45 of the same patients) neoplastic videos. The benchmarking test set comprised 100 (50 patients) neoplastic images, 300 (125 patients) non-dysplastic images, 47 (47 of the same patients) neoplastic videos, and 141 (82 of the same patients) non-dysplastic videos, and was enriched with subtle neoplasia cases. The benchmarking test set was evaluated by 112 endoscopists from six countries (first without CADe and, after 6 weeks, with CADe) and by 28 external international Barrett's oesophagus experts. The primary outcome was the sensitivity of Barrett's neoplasia detection by general endoscopists without CADe assistance versus with CADe assistance on the benchmarking test set. We compared sensitivity using a mixed-effects logistic regression model with conditional odds ratios (ORs; likelihood profile 95% CIs).

FINDINGS

Sensitivity for neoplasia detection among endoscopists increased from 74% to 88% with CADe assistance (OR 2·04; 95% CI 1·73-2·42; p<0·0001 for images and from 67% to 79% [2·35; 1·90-2·94; p<0·0001] for video) without compromising specificity (from 89% to 90% [1·07; 0·96-1·19; p=0·20] for images and from 96% to 94% [0·94; 0·79-1·11; ] for video; p=0·46). In the all-comers test set, CADe detected neoplastic lesions in 95% (88-98) of images and 97% (90-99) of videos. In the benchmarking test set, the CADe system was superior to endoscopists in detecting neoplasia (90% vs 74% [OR 3·75; 95% CI 1·93-8·05; p=0·0002] for images and 91% vs 67% [11·68; 3·85-47·53; p<0·0001] for video) and non-inferior to Barrett's oesophagus experts (90% vs 87% [OR 1·74; 95% CI 0·83-3·65] for images and 91% vs 86% [2·94; 0·99-11·40] for video).

INTERPRETATION

CADe outperformed endoscopists in detecting Barrett's oesophagus neoplasia and, when used as an assistive tool, it improved their detection rate. CADe detected virtually all neoplasia in a test set of consecutive cases.

FUNDING

Olympus.

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Affiliation(s)

K N Fockens Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
M R Jong Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
J B Jukema Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
T G W Boers Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
C H J Kusters Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
J A van der Putten Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
R E Pouw Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
L C Duits Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
N S M Montazeri Biostatistics Unit, Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
S N van Munster Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Department of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, Netherlands
B L A M Weusten Department of Gastroenterology and Hepatology, UMC Utrecht, University of Utrecht, Utrecht, Netherlands; Department of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, Netherlands
L Alvarez Herrero Department of Gastroenterology and Hepatology, St Antonius Hospital, Nieuwegein, Netherlands
M H M G Houben Department of Gastroenterology and Hepatology, HagaZiekenhuis Den Haag, Den Haag, Netherlands
W B Nagengast Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, Netherlands
J Westerhof Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, Netherlands
A Alkhalaf Department of Gastroenterology and Hepatology, Isala Hospital Zwolle, Zwolle, Netherlands
R C Mallant-Hent Department of Gastroenterology and Hepatology, Flevoziekenhuis Almere, Almere, Netherlands
P Scholten Department of Gastroenterology and Hepatology, Onze Lieve Vrouwe Gasthuis, Amsterdam, Netherlands
K Ragunath Department of Gastroenterology and Hepatology, Royal Perth Hospital, Curtin University, Perth, WA, Australia
S Seewald Department of Gastroenterology and Hepatology, Hirslanden Klinik, Zurich, Switzerland
P Elbe Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Division of Surgery, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
F Baldaque-Silva Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Center for Advanced Endoscopy Carlos Moreira da Silva, Gastroenterology Department, Pedro Hispano Hospital, Matosinhos, Portugal
M Barret Department of Gastroenterology and Hepatology, Cochin Hospital Paris, Paris, France
J Ortiz Fernández-Sordo Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, UK
G Moral Villarejo Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, UK
O Pech Department of Gastroenterology and Hepatology, St John of God Hospital, Regensburg, Germany
T Beyna Department of Gastroenterology and Hepatology, Evangalisches Krankenhaus Düsseldorf, Düsseldorf, Germany
F van der Sommen Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
P H de With Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
A J de Groof Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
J J Bergman Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.

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Tang S, Yu X, Cheang CF, Ji X, Yu HH, Choi IC. CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023;231:107399. [PMID: 36780717 DOI: 10.1016/j.cmpb.2023.107399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/03/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]

Meinikheim M, Messmann H, Ebigbo A. Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia. Clin Endosc 2023;56:14-22. [PMID: 36646423 PMCID: PMC9902686 DOI: 10.5946/ce.2022.247] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/25/2022] [Indexed: 01/18/2023] Open

Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022;3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open

Islam MM, Poly TN, Walther BA, Yeh CY, Seyed-Abdul S, Li YC(J, Lin MC. Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022;14:cancers14235996. [PMID: 36497480 PMCID: PMC9736434 DOI: 10.3390/cancers14235996] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/17/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open

Abstract

Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.

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Affiliation(s)

Md. Mohaimenul Islam Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
Tahmina Nasrin Poly Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
Bruno Andreas Walther Deep Sea Ecology and Technology, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, D-27570 Bremerhaven, Germany
Chih-Yang Yeh Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
Shabbir Seyed-Abdul Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
Yu-Chuan (Jack) Li Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan Department of Dermatology, Wan Fang Hospital, Taipei 116, Taiwan TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
Ming-Chin Lin Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan Taipei Neuroscience Institute, Taipei Medical University, Taipei 11031, Taiwan Correspondence:

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Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022;12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open

Okagawa Y, Abe S, Yamada M, Oda I, Saito Y. Artificial Intelligence in Endoscopy. Dig Dis Sci 2022;67:1553-1572. [PMID: 34155567 DOI: 10.1007/s10620-021-07086-z] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/14/2021] [Indexed: 12/15/2022]

Okimoto E, Ishimura N, Adachi K, Kinoshita Y, Ishihara S, Tada T. Application of Convolutional Neural Networks for Diagnosis of Eosinophilic Esophagitis Based on Endoscopic Imaging. J Clin Med 2022;11:jcm11092529. [PMID: 35566653 PMCID: PMC9105792 DOI: 10.3390/jcm11092529] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/17/2022] [Accepted: 04/29/2022] [Indexed: 02/04/2023] Open

Artificial Intelligence in the Management of Barrett’s Esophagus and Early Esophageal Adenocarcinoma. Cancers (Basel) 2022;14:cancers14081918. [PMID: 35454824 PMCID: PMC9028107 DOI: 10.3390/cancers14081918] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/02/2022] [Accepted: 04/07/2022] [Indexed: 02/06/2023] Open

Nagao S, Tani Y, Shibata J, Tsuji Y, Tada T, Ishihara R, Fujishiro M. Implementation of artificial intelligence in upper gastrointestinal endoscopy. DEN OPEN 2022;2:e72. [PMID: 35873509 PMCID: PMC9302271 DOI: 10.1002/deo2.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/11/2021] [Accepted: 10/16/2021] [Indexed: 12/24/2022]

Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022;55:528-540. [PMID: 35098562 PMCID: PMC9305819 DOI: 10.1111/apt.16778] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/09/2022] [Accepted: 01/09/2022] [Indexed: 12/12/2022]

Hamada K, Kawahara Y, Tanimoto T, Ohto A, Toda A, Aida T, Yamasaki Y, Gotoda T, Ogawa T, Abe M, Okanoue S, Takei K, Kikuchi S, Kuroda S, Fujiwara T, Okada H. Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images. J Gastroenterol Hepatol 2022;37:352-357. [PMID: 34713495 DOI: 10.1111/jgh.15725] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 10/14/2021] [Accepted: 10/24/2021] [Indexed: 12/30/2022]

Affiliation(s)

Kenta Hamada Department of Endoscopy, Okayama University Hospital, Okayama, Japan.,Department of Practical Gastrointestinal Endoscopy, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Yoshiro Kawahara Department of Practical Gastrointestinal Endoscopy, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Takayoshi Tanimoto Business Strategy Division, Ryobi Systems Co., Ltd., Okayama, Japan
Akimitsu Ohto Health Care Company, Ryobi Systems Co., Ltd., Okayama, Japan
Akira Toda Business Strategy Division, Ryobi Systems Co., Ltd., Okayama, Japan
Toshiaki Aida Okayama University Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama, Japan
Yasushi Yamasaki Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Tatsuhiro Gotoda Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Taiji Ogawa Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Makoto Abe Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Shotaro Okanoue Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Kensuke Takei Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Satoru Kikuchi Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Shinji Kuroda Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Toshiyoshi Fujiwara Department of Gastroenterological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
Hiroyuki Okada Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan

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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022;56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]

Con D, van Langenberg DR, Vasudevan A. Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study. World J Gastroenterol 2021;27:6476-6488. [PMID: 34720536 PMCID: PMC8517788 DOI: 10.3748/wjg.v27.i38.6476] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/26/2021] [Accepted: 09/06/2021] [Indexed: 02/06/2023] Open

Abstract

BACKGROUND

Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional approaches are unable to take advantage of more complex data structures such as repeated measurements. Deep learning methods have the potential ability to automatically find and learn complex, hidden relationships between predictive markers and outcomes, but their application to clinical prediction in CD and IBD has not been explored previously.

AIM

To determine and compare the utility of deep learning with conventional algorithms in predicting response to anti-tumor necrosis factor (anti-TNF) therapy in CD.

METHODS

This was a retrospective single-center cohort study of all CD patients who commenced anti-TNF therapy (either adalimumab or infliximab) from January 1, 2010 to December 31, 2015. Remission was defined as a C-reactive protein (CRP) < 5 mg/L at 12 mo after anti-TNF commencement. Three supervised learning algorithms were compared: (1) A conventional statistical learning algorithm using multivariable logistic regression on baseline data only; (2) A deep learning algorithm using a feed-forward artificial neural network on baseline data only; and (3) A deep learning algorithm using a recurrent neural network on repeated data. Predictive performance was assessed using area under the receiver operator characteristic curve (AUC) after 10× repeated 5-fold cross-validation.

RESULTS

A total of 146 patients were included (median age 36 years, 48% male). Concomitant therapy at anti-TNF commencement included thiopurines (68%), methotrexate (18%), corticosteroids (44%) and aminosalicylates (33%). After 12 mo, 64% had CRP < 5 mg/L. The conventional learning algorithm selected the following baseline variables for the predictive model: Complex disease behavior, albumin, monocytes, lymphocytes, mean corpuscular hemoglobin concentration and gamma-glutamyl transferase, and had a cross-validated AUC of 0.659, 95% confidence interval (CI): 0.562-0.756. A feed-forward artificial neural network using only baseline data demonstrated an AUC of 0.710 (95%CI: 0.622-0.799; P = 0.25 vs conventional). A recurrent neural network using repeated biomarker measurements demonstrated significantly higher AUC compared to the conventional algorithm (0.754, 95%CI: 0.674-0.834; P = 0.036).

CONCLUSION

Deep learning methods are feasible and have the potential for stronger predictive performance compared to conventional model building methods when applied to predicting remission after anti-TNF therapy in CD.

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Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021;2:185-197. [DOI: 10.37126/aige.v2.i4.185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 06/25/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open

Zhou J, Hu N, Huang ZY, Song B, Wu CC, Zeng FX, Wu M. Application of artificial intelligence in gastrointestinal disease: a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2021;9:1188. [PMID: 34430629 PMCID: PMC8350704 DOI: 10.21037/atm-21-3001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 06/29/2021] [Indexed: 02/05/2023]

Zhang SM, Wang YJ, Zhang ST. Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images: A systematic review and meta-analysis. J Dig Dis 2021;22:318-328. [PMID: 33871932 PMCID: PMC8361665 DOI: 10.1111/1751-2980.12992] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/02/2021] [Accepted: 04/15/2021] [Indexed: 12/14/2022]

Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021;2:12-24. [DOI: 10.35713/aic.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/02/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open

Senbekov M, Saliev T, Bukeyeva Z, Almabayeva A, Zhanaliyeva M, Aitenova N, Toishibekov Y, Fakhradiyev I. The Recent Progress and Applications of Digital Technologies in Healthcare: A Review. Int J Telemed Appl 2020;2020:8830200. [PMID: 33343657 PMCID: PMC7732404 DOI: 10.1155/2020/8830200] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023] Open

Abstract

BACKGROUND

The implementation of medical digital technologies can provide better accessibility and flexibility of healthcare for the public. It encompasses the availability of open information on the health, treatment, complications, and recent progress on biomedical research. At present, even in low-income countries, diagnostic and medical services are becoming more accessible and available. However, many issues related to digital health technologies remain unmet, including the reliability, safety, testing, and ethical aspects.

PURPOSE

The aim of the review is to discuss and analyze the recent progress on the application of big data, artificial intelligence, telemedicine, block-chain platforms, smart devices in healthcare, and medical education. Basic Design. The publication search was carried out using Google Scholar, PubMed, Web of Sciences, Medline, Wiley Online Library, and CrossRef databases. The review highlights the applications of artificial intelligence, "big data," telemedicine and block-chain technologies, and smart devices (internet of things) for solving the real problems in healthcare and medical education. Major Findings. We identified 252 papers related to the digital health area. However, the number of papers discussed in the review was limited to 152 due to the exclusion criteria. The literature search demonstrated that digital health technologies became highly sought due to recent pandemics, including COVID-19. The disastrous dissemination of COVID-19 through all continents triggered the need for fast and effective solutions to localize, manage, and treat the viral infection. In this regard, the use of telemedicine and other e-health technologies might help to lessen the pressure on healthcare systems. Summary. Digital platforms can help optimize diagnosis, consulting, and treatment of patients. However, due to the lack of official regulations and recommendations, the stakeholders, including private and governmental organizations, are facing the problem with adequate validation and approbation of novel digital health technologies. In this regard, proper scientific research is required before a digital product is deployed for the healthcare sector.

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Syed T, Doshi A, Guleria S, Syed S, Shah T. Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia. Dig Dis Sci 2020;65:3448-3455. [PMID: 33057945 PMCID: PMC8139616 DOI: 10.1007/s10620-020-06643-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 09/26/2020] [Indexed: 12/15/2022]