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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]
Abstract
BACKGROUND This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE). METHODS 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. RESULTS AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%-74.2%] to 78.0% [95%CI 74.0%-82.0%]; specificity 67.3% [95%CI 62.5%-72.2%] to 72.7% [95%CI 68.2%-77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. CONCLUSION BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice.
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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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Sachdeva K, Natarajan K, Iyer PG. Improving esophageal cancer screening across the globe: Translating knowledge into action. Indian J Gastroenterol 2024; 43:705-716. [PMID: 38848004 PMCID: PMC11345747 DOI: 10.1007/s12664-024-01543-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/26/2024] [Indexed: 08/02/2024]
Abstract
Esophageal cancer (EC) is a pressing global health concern, ranking as the eighth most common cancer and the sixth leading cause for cancer-related deaths worldwide. Esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC) are the two major histological types of esophageal cancer associated with distinct risk factors and geographical distributions. Unfortunately, the outcomes for both types of EC remain discouraging, with a five-year survival rate of less than 20% when diagnosed at advanced stages. Advanced endoscopic techniques have the potential to vastly enhance patient outcomes and impede the progression of pre-malignant lesions to cancer. However, low screening rates with endoscopy due to its invasive nature and high cost hinder its effectiveness. Despite extensive research on risk predictors, a significant number of cases still go undiagnosed, highlighting the need for improved screening techniques that can be implemented at the population level. To increase uptake, a shift towards minimally invasive, well-tolerated and cost-effective non-endoscopic technologies is crucial. The implementation of such devices in primary care settings, specifically targeting high-risk populations, can be a promising strategy. With early detection and enrollment in surveillance programs, there is hope for substantial improvement in morbidity and mortality rates through modern minimally invasive endoscopic and surgical techniques.
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Affiliation(s)
- Karan Sachdeva
- Division of Internal Medicine, Louisiana State University Health Science, Shreveport, LA, USA
| | | | - Prasad G Iyer
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA.
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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 2024:1-17. [PMID: 39068926 DOI: 10.1159/000540251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 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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Affiliation(s)
- Ryosuke Kikuchi
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuaki Okamoto
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Ozawa
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
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Jong MR, de Groof AJ. Advancement of artificial intelligence systems for surveillance endoscopy of Barrett's esophagus. Dig Liver Dis 2024; 56:1126-1130. [PMID: 38071181 DOI: 10.1016/j.dld.2023.11.038] [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: 09/29/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 06/29/2024]
Abstract
Barrett's esophagus (BE) is a precursor disease for esophageal adenocarcinoma. Timely detection and treatment has significant influence on patient outcomes. Over the last years, several artificial intelligence (AI) systems have emerged to assist the endoscopist. The primary focus of research has been computer aided detection (CADe). Several groups have succeeded in developing competitive models for neoplasia detection. Additionally, computer aided diagnosis (CADx) models have been developed for subsequent lesion characterization and assistance in clinical decision making. Future studies should focus on bridging the domain gap between academic development and integration in daily practice.
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Affiliation(s)
- M R Jong
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - A J de Groof
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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Chen Y, Gao R, Jing D, Shi L, Kuang F, Jing R. Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124030. [PMID: 38368818 DOI: 10.1016/j.saa.2024.124030] [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: 08/12/2023] [Revised: 01/27/2024] [Accepted: 02/08/2024] [Indexed: 02/20/2024]
Abstract
Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. This retrospective study collected 279 specimens in 89 non-surgical ESCC patients through endoscopic biopsy between January 2010 and January 2019. These patients were divided into a CRT response group (CR + PR group) and a CRT non-response group (SD + PD group). The WSIs have segmented approximately 1,206,000 non-overlapping patches. Two experienced pathologists manually delineated the eight types of tissues on 32 WSIs, including esophagus tumor cell (TUM), cancer-associated stroma (CAS), normal epithelium layer (NEL), smooth muscle (MUS), lymphocytes (LYM), Red cells (RED), debris (DEB), uneven areas (UNE). The chemoradiotherapy response prediction models were built using maximum relevance-minimum redundancy (MRMR) feature selection and least absolute shrinkage and selection operator (LASSO) regression. However, pathological features with p < 0.1 were selected and integrated to be further screened using a LASSO Cox regression model to build a multivariate Cox proportional hazards model for predicting the OS. The testing accuracy of the tissue classification model was 91.3 %. The pathological model created using two CAS in-depth features and eight TUM in-depth features performed best for the prediction of treatment response and achieved an AUC of 0.744. For the prediction of OS, the testing AUC of this model at one year and three years were 0.675 and 0.870, respectively. The TUM model showed the highest AUC at one year (0.712). With its high accuracy rate, the deep learning model has the potential to transform from bench to bedside in clinical practice, improve patient's quality of life, and prolong the OS rate.
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Affiliation(s)
- Yu Chen
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ruihuan Gao
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Di Jing
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Liting Shi
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271016, China
| | - Feng Kuang
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xiamen University, Teaching Hospital of Fujian Medical University, Xiamen, China
| | - Ran Jing
- Department of Cardiovascular Medicine, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 410008 Changsha, China.
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Jukema JB, Kusters CHJ, Jong MR, Fockens KN, Boers T, van der Putten JA, Pouw RE, Duits LC, Weusten BLAM, Herrero LA, Houben MHMG, Nagengast WB, Westerhof J, Alkhalaf A, Mallant-Hent R, Scholten P, Ragunath K, Seewald S, Elbe P, Silva FB, Barret M, Ortiz Fernández-Sordo J, Moral Villarejo G, Pech O, Beyna T, Montazeri NSM, der Sommen FV, de With PH, de Groof AJ, Bergman JJ. Computer-aided diagnosis improves characterization of Barrett's neoplasia by general endoscopists (with video). Gastrointest Endosc 2024:S0016-5107(24)00233-5. [PMID: 38636819 DOI: 10.1016/j.gie.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/25/2024] [Accepted: 04/08/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND AND AIMS Characterization of visible abnormalities in patients with Barrett's esophagus (BE) can be challenging, especially for inexperienced endoscopists. This results in suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CADx) systems may assist endoscopists. We aimed to develop, validate, and benchmark a CADx system for BE neoplasia. METHODS The CADx system received pretraining with ImageNet and then consecutive domain-specific pretraining with GastroNet, which includes 5 million endoscopic images. It was subsequently trained and internally validated using 1758 narrow-band imaging (NBI) images of early BE neoplasia (352 patients) and 1838 NBI images of nondysplastic BE (173 patients) from 8 international centers. CADx was tested prospectively on corresponding image and video test sets with 30 cases (20 patients) of BE neoplasia and 60 cases (31 patients) of nondysplastic BE. The test set was benchmarked by 44 general endoscopists in 2 phases (phase 1, no CADx assistance; phase 2, with CADx assistance). Ten international BE experts provided additional benchmark performance. RESULTS Stand-alone sensitivity and specificity of the CADx system were 100% and 98% for images and 93% and 96% for videos, respectively. CADx outperformed general endoscopists without CADx assistance in terms of sensitivity (P = .04). Sensitivity and specificity of general endoscopists increased from 84% to 96% and 90% to 98% with CAD assistance (P < .001). CADx assistance increased endoscopists' confidence in characterization (P < .001). CADx performance was similar to that of the BE experts. CONCLUSIONS CADx assistance significantly increased characterization performance of BE neoplasia by general endoscopists to the level of expert endoscopists. The use of this CADx system may thereby improve daily Barrett surveillance.
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Affiliation(s)
- Jelmer B Jukema
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Martijn R Jong
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Kiki N Fockens
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Tim Boers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Joost A van der Putten
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Roos E Pouw
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Lucas C Duits
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bas L A M Weusten
- Department of Gastroenterology and Hepatology, UMC Utrecht, University of Utrecht, Utrecht, the Netherlands; Department of Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, the Netherlands
| | - Lorenza Alvarez Herrero
- Department of Gastroenterology and Hepatology, Sint Antonius Hospital, Nieuwegein, the Netherlands
| | - Martin H M G Houben
- Department of Gastroenterology and Hepatology, HagaZiekenhuis Den Haag, Den Haag, the Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, the Netherlands
| | - Jessie Westerhof
- Department of Gastroenterology and Hepatology, UMC Groningen, University of Groningen, Groningen, the Netherlands
| | - Alaa Alkhalaf
- Department of Gastroenterology and Hepatology, Isala Hospital Zwolle, Zwolle, the Netherlands
| | - Rosalie Mallant-Hent
- Department of Gastroenterology and Hepatology, Flevoziekenhuis Almere, Almere, the Netherlands
| | - Pieter Scholten
- Department of Gastroenterology and Hepatology, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands
| | - Krish Ragunath
- Department of Gastroenterology and Hepatology, Royal Perth Hospital, Curtin University, Perth, WA, Australia
| | - Stefan Seewald
- Department of Gastroenterology and Hepatology, Hirslanden Klinik, Zurich, Switzerland
| | - Peter Elbe
- Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Division of Surgery, Department of Clinical Science, Intervention and Technology, CLINTEC, Karolinska Institutet, Stockholm, Sweden
| | - Francisco Baldaque Silva
- Department of Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden; Center for Advanced Endoscopy Carlos Moreira da Silva, Gastroenterology Department, Pedro Hispano Hospital, ULSM Matosinhos, Portugal
| | - Maximilien Barret
- Department of Gastroenterology and Hepatology, Cochin Hospital Paris, Paris, France
| | - Jacobo Ortiz Fernández-Sordo
- Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Guiomar Moral Villarejo
- Department of Gastroenterology and Hepatology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Oliver Pech
- Department of Gastroenterology and Hepatology, St. John of God Hospital, Regensburg, Germany
| | - Torsten Beyna
- Department of Gastroenterology and Hepatology, Evangalisches Krankenhaus Düsseldorf, Düsseldorf, Germany
| | - Nahid S M Montazeri
- Biostatistics Unit, Department of Gastroenterology and Hepatology, Amsterdam University Medical Center, location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Peter H de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - A Jeroen de Groof
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Jacques J Bergman
- Department of Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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Zilberstein N, Godbee M, Mehta NA, Waxman I. Advanced endoscopic imaging for detection of Barrett's esophagus. Clin Endosc 2024; 57:1-10. [PMID: 38178326 PMCID: PMC10834296 DOI: 10.5946/ce.2023.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 07/27/2023] [Accepted: 08/29/2023] [Indexed: 01/06/2024] Open
Abstract
Barrett's esophagus (BE) is the precursor to esophageal adenocarcinoma (EAC), and is caused by chronic gastroesophageal reflux. BE can progress over time from metaplasia to dysplasia, and eventually to EAC. EAC is associated with a poor prognosis, often due to advanced disease at the time of diagnosis. However, if BE is diagnosed early, pharmacologic and endoscopic treatments can prevent progression to EAC. The current standard of care for BE surveillance utilizes the Seattle protocol. Unfortunately, a sizable proportion of early EAC and BE-related high-grade dysplasia (HGD) are missed due to poor adherence to the Seattle protocol and sampling errors. New modalities using artificial intelligence (AI) have been proposed to improve the detection of early EAC and BE-related HGD. This review will focus on AI technology and its application to various endoscopic modalities such as high-definition white light endoscopy, narrow-band imaging, and volumetric laser endomicroscopy.
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Affiliation(s)
- Netanel Zilberstein
- Division of Digestive Diseases and Nutrition, Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Michelle Godbee
- Division of Digestive Diseases and Nutrition, Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Neal A. Mehta
- Division of Digestive Diseases and Nutrition, Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Irving Waxman
- Division of Digestive Diseases and Nutrition, Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
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9
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Li X, Wu Q, Wang M, Wu K. Uncertainty-aware network for fine-grained and imbalanced reflux esophagitis grading. Comput Biol Med 2024; 168:107751. [PMID: 38016373 DOI: 10.1016/j.compbiomed.2023.107751] [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: 07/15/2023] [Revised: 10/22/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
Computer-aided diagnosis (CAD) assists endoscopists in analyzing endoscopic images, reducing misdiagnosis rates and enabling timely treatment. A few studies have focused on CAD for gastroesophageal reflux disease, but CAD studies on reflux esophagitis (RE) are still inadequate. This paper presents a CAD study on RE using a dataset collected from hospital, comprising over 3000 images. We propose an uncertainty-aware network with handcrafted features, utilizing representation and classifier decoupling with metric learning to address class imbalance and achieve fine-grained RE classification. To enhance interpretability, the network estimates uncertainty through test time augmentation. The experimental results demonstrate that the proposed network surpasses previous methods, achieving an accuracy of 90.2% and an F1 score of 90.1%.
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Affiliation(s)
- Xingcun Li
- School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Qinghua Wu
- School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Mi Wang
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Kun Wu
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
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10
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Zhang JQ, Mi JJ, Wang R. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World J Gastrointest Oncol 2023; 15:1998-2016. [DOI: 10.4251/wjgo.v15.i11.1998] [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: 08/01/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.
AIM To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).
METHODS PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.
RESULTS A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).
CONCLUSION CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.
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Affiliation(s)
- Jun-Qi Zhang
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Jun-Jie Mi
- Department of Gastroenterology, Shanxi Provincial People’s Hospital, Taiyuan 030012, Shanxi Province, China
| | - Rong Wang
- Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan 030012, Shanxi Province, China
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11
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Cui R, Wang L, Lin L, Li J, Lu R, Liu S, Liu B, Gu Y, Zhang H, Shang Q, Chen L, Tian D. Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions. Bioengineering (Basel) 2023; 10:1239. [PMID: 38002363 PMCID: PMC10669008 DOI: 10.3390/bioengineering10111239] [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: 08/30/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/26/2023] Open
Abstract
Barrett's esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the "black box" nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.
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Affiliation(s)
- Ruichen Cui
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Lei Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
- West China School of Nursing, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China
| | - Lin Lin
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
- West China School of Nursing, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China
| | - Jie Li
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
- West China School of Nursing, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China
| | - Runda Lu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Shixiang Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Bowei Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Yimin Gu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Qixin Shang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Longqi Chen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
| | - Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu 610041, China; (R.C.); (L.W.); (L.L.); (J.L.); (R.L.); (S.L.); (B.L.); (Y.G.); (H.Z.); (Q.S.)
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12
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Lazo JF, Rosa B, Catellani M, Fontana M, Mistretta FA, Musi G, de Cobelli O, de Mathelin M, De Momi E. Semi-Supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images. IEEE Trans Biomed Eng 2023; 70:2822-2833. [PMID: 37037233 DOI: 10.1109/tbme.2023.3265679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
OBJECTIVE Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. METHOD We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. CONCLUSION The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. SIGNIFICANCE This study shows the potential of using semi-supervised GAN-based bladder tissue classification when annotations are limited in multi-domain data.
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Tee CHN, Ravi R, Ang TL, Li JW. Role of artificial intelligence in Barrett’s esophagus. Artif Intell Gastroenterol 2023; 4:28-35. [DOI: 10.35712/aig.v4.i2.28] [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: 03/06/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 09/07/2023] Open
Abstract
The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.
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Affiliation(s)
- Chin Hock Nicholas Tee
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Rajesh Ravi
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
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Popovic D, Glisic T, Milosavljevic T, Panic N, Marjanovic-Haljilji M, Mijac D, Stojkovic Lalosevic M, Nestorov J, Dragasevic S, Savic P, Filipovic B. The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy. Diagnostics (Basel) 2023; 13:2862. [PMID: 37761229 PMCID: PMC10528171 DOI: 10.3390/diagnostics13182862] [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: 07/30/2023] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.
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Affiliation(s)
- Dusan Popovic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Tijana Glisic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | | | - Natasa Panic
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Marija Marjanovic-Haljilji
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Dragana Mijac
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Milica Stojkovic Lalosevic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Jelena Nestorov
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Sanja Dragasevic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Predrag Savic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Surgery, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia
| | - Branka Filipovic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
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15
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Kellerman R, Bleiweiss A, Samuel S, Margalit-Yehuda R, Aflalo E, Barzilay O, Ben-Horin S, Eliakim R, Zimlichman E, Soffer S, Klang E, Kopylov U. Spatiotemporal analysis of small bowel capsule endoscopy videos for outcomes prediction in Crohn's disease. Therap Adv Gastroenterol 2023; 16:17562848231172556. [PMID: 37440929 PMCID: PMC10333642 DOI: 10.1177/17562848231172556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 07/15/2023] Open
Abstract
Background Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined. Objectives We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients. Design This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software. Methods CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis. Results The patient cohort included 101 patients. The median duration of follow-up was 902 (354-1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74. Conclusion Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD.
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Affiliation(s)
| | | | | | - Reuma Margalit-Yehuda
- Department of Gastroenterology, Sheba Medical
Center, Tel Hashomer, Israel and Tel Aviv University, Tel Aviv, Israel
| | | | - Oranit Barzilay
- Department of Internal Medicine F, Sheba
Medical Center, Tel Hashomer, Israel and Tel Aviv University, Tel Aviv,
Israel
| | - Shomron Ben-Horin
- Department of Gastroenterology, Sheba Medical
Center, Tel Hashomer, Israel and Tel Aviv University, Tel Aviv, Israel
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical
Center, Tel Hashomer, Israel and Tel Aviv University, Tel Aviv, Israel
| | - Eyal Zimlichman
- Sheba ARC and Hospital Management, Sheba
Medical Center, Tel Hashomer, Israel and Tel Aviv University, Tel Aviv,
Israel
| | - Shelly Soffer
- Department of Internal Medicine B, Assuta
Medical Center, 7747629, Ashdod, Israel
- Ben-Gurion University of the Negev, Be’er
Sheva, Israel
| | - Eyal Klang
- Sheba ARC, Sheba Medical Center, Tel Hashomer,
Israel and Tel Aviv University, Tel Aviv, Israel
| | - Uri Kopylov
- Department of Gastroenterology, Sheba Medical
Center, Tel Hashomer, Israel and Tel Aviv University, Tel Aviv, Israel
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16
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Du RC, Ouyang YB, Hu Y. Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022. World J Gastroenterol 2023; 29:3561-3573. [PMID: 37389238 PMCID: PMC10303508 DOI: 10.3748/wjg.v29.i22.3561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/03/2023] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
BACKGROUND Recently, artificial intelligence (AI) has been widely used in gastrointestinal endoscopy examinations.
AIM To comprehensively evaluate the application of AI-assisted endoscopy in detecting different digestive diseases using bibliometric analysis.
METHODS Relevant publications from the Web of Science published from 1990 to 2022 were extracted using a combination of the search terms “AI” and “endoscopy”. The following information was recorded from the included publications: Title, author, institution, country, endoscopy type, disease type, performance of AI, publication, citation, journal and H-index.
RESULTS A total of 446 studies were included. The number of articles reached its peak in 2021, and the annual citation numbers increased after 2006. China, the United States and Japan were dominant countries in this field, accounting for 28.7%, 16.8%, and 15.7% of publications, respectively. The Tada Tomohiro Institute of Gastroenterology and Proctology was the most influential institution. “Cancer” and “polyps” were the hotspots in this field. Colorectal polyps were the most concerning and researched disease, followed by gastric cancer and gastrointestinal bleeding. Conventional endoscopy was the most common type of examination. The accuracy of AI in detecting Barrett’s esophagus, colorectal polyps and gastric cancer from 2018 to 2022 is 87.6%, 93.7% and 88.3%, respectively. The detection rates of adenoma and gastrointestinal bleeding from 2018 to 2022 are 31.3% and 96.2%, respectively.
CONCLUSION AI could improve the detection rate of digestive tract diseases and a convolutional neural network-based diagnosis program for endoscopic images shows promising results.
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Affiliation(s)
- Ren-Chun Du
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Yao-Bin Ouyang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
- Department of Oncology, Mayo Clinic, Rochester, MN 55905, United States
| | - Yi Hu
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong 999077, China
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Iyer PG, Chak A. Surveillance in Barrett's Esophagus: Challenges, Progress, and Possibilities. Gastroenterology 2023; 164:707-718. [PMID: 36746210 PMCID: PMC10079619 DOI: 10.1053/j.gastro.2023.01.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 02/08/2023]
Abstract
Endoscopic surveillance of Barrett's esophagus, aiming to detect prevalent dysplasia and adenocarcinoma, followed by effective endoscopic treatment, is an integral part of the esophageal adenocarcinoma prevention paradigm. However, several limitations, such as the subtle appearance of dysplasia, sampling error (inherent in current surveillance protocols), and noncompliance with surveillance recommendations, lead to missed dysplasia and neoplasia, reducing the effectiveness of surveillance as currently practiced. Careful endoscopic assessment with high-resolution white-light endoscopy, dye-based or electronic chromoendoscopy, and comprehensive sampling of the BE mucosa, remains the cornerstone of endoscopic surveillance. Emerging innovations in this area span the gamut of more efficient sampling methods, advanced imaging tools, artificial intelligence, and molecular marker-powered approaches as adjuncts, to identify prevalent and predict incident dysplasia or adenocarcinoma. Development and implementation of validated quality indicators will allow additional advancement of this critical field. These approaches will hopefully enable efficient and effective cancer prevention and treatment.
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Affiliation(s)
- Prasad G Iyer
- Barrett's Esophagus Unit, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.
| | - Amitabh Chak
- Division of Gastroenterology and Hepatology, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio
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18
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Hussein M, Lines D, González-Bueno Puyal J, Kader R, Bowman N, Sehgal V, Toth D, Ahmad OF, Everson M, Esteban JM, Bisschops R, Banks M, Haefner M, Mountney P, Stoyanov D, Lovat LB, Haidry R. Computer-aided characterization of early cancer in Barrett's esophagus on i-scan magnification imaging: a multicenter international study. Gastrointest Endosc 2023; 97:646-654. [PMID: 36460087 PMCID: PMC10590905 DOI: 10.1016/j.gie.2022.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/08/2022] [Accepted: 11/18/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND AND AIMS We aimed to develop a computer-aided characterization system that could support the diagnosis of dysplasia in Barrett's esophagus (BE) on magnification endoscopy. METHODS Videos were collected in high-definition magnification white-light and virtual chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and nondysplastic BE (NDBE) from 4 centers. We trained a neural network with a Resnet101 architecture to classify frames as dysplastic or nondysplastic. The network was tested on 3 different scenarios: high-quality still images, all available video frames, and a selected sequence within each video. RESULTS Fifty-seven patients, each with videos of magnification areas of BE (34 dysplasia, 23 NDBE), were included. Performance was evaluated by a leave-1-patient-out cross-validation method. In all, 60,174 (39,347 dysplasia, 20,827 NDBE) magnification video frames were used to train the network. The testing set included 49,726 i-scan-3/optical enhancement magnification frames. On 350 high-quality still images, the network achieved a sensitivity of 94%, specificity of 86%, and area under the receiver operator curve (AUROC) of 96%. On all 49,726 available video frames, the network achieved a sensitivity of 92%, specificity of 82%, and AUROC of 95%. On a selected sequence of frames per case (total of 11,471 frames), we used an exponentially weighted moving average of classifications on consecutive frames to characterize dysplasia. The network achieved a sensitivity of 92%, specificity of 84%, and AUROC of 96%. The mean assessment speed per frame was 0.0135 seconds (SD ± 0.006). CONCLUSION Our network can characterize BE dysplasia with high accuracy and speed on high-quality magnification images and sequence of video frames, moving it toward real-time automated diagnosis.
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Affiliation(s)
- Mohamed Hussein
- Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
| | | | - Juana González-Bueno Puyal
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Odin Vision, UK
| | - Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
| | - Nicola Bowman
- Division of Surgery and Interventional Sciences, University College London, UK
| | - Vinay Sehgal
- Department of Gastroenterology, University College London Hospital, UK
| | | | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
| | - Martin Everson
- Division of Surgery and Interventional Sciences, University College London, UK
| | - Jose Miguel Esteban
- Department of Gastroenterology and Hepatology, Clínico San Carlos, Madrid, Spain
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
| | - Matthew Banks
- Department of Gastroenterology, University College London Hospital, UK
| | - Michael Haefner
- Krankenhaus der Barmherzigen Schwestern, Department of Internal Medicine II, Vienna, Austria
| | | | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK
| | - Laurence B Lovat
- Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
| | - Rehan Haidry
- Division of Surgery and Interventional Sciences, University College London, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Department of Gastroenterology, University College London Hospital, UK
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19
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Wong MW, Rogers BD, Liu MX, Lei WY, Liu TT, Yi CH, Hung JS, Liang SW, Tseng CW, Wang JH, Wu PA, Chen CL. Application of Artificial Intelligence in Measuring Novel pH-Impedance Metrics for Optimal Diagnosis of GERD. Diagnostics (Basel) 2023; 13:diagnostics13050960. [PMID: 36900104 PMCID: PMC10000892 DOI: 10.3390/diagnostics13050960] [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: 01/17/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
Novel metrics extracted from pH-impedance monitoring can augment the diagnosis of gastroesophageal reflux disease (GERD). Artificial intelligence (AI) is being widely used to improve the diagnostic capabilities of various diseases. In this review, we update the current literature regarding applications of artificial intelligence in measuring novel pH-impedance metrics. AI demonstrates high performance in the measurement of impedance metrics, including numbers of reflux episodes and post-reflux swallow-induced peristaltic wave index and, furthermore, extracts baseline impedance from the entire pH-impedance study. AI is expected to play a reliable role in facilitating measuring novel impedance metrics in patients with GERD in the near future.
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Affiliation(s)
- Ming-Wun Wong
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
- School of Post-Baccalaureate Chinese Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - Benjamin D. Rogers
- Division of Gastroenterology, Hepatology and Nutrition, University of Louisville, Louisville, KY 40292, USA
- Division of Gastroenterology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Min-Xiang Liu
- AI Innovation Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan
| | - Wei-Yi Lei
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Tso-Tsai Liu
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Chih-Hsun Yi
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Jui-Sheng Hung
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Shu-Wei Liang
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Chiu-Wang Tseng
- NVIDIA AI Technology Center, NVIDIA Corporation, Taipei 11492, Taiwan
| | - Jen-Hung Wang
- Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan
| | - Ping-An Wu
- AI Innovation Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan
| | - Chien-Lin Chen
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
- Institute of Medical Sciences, Tzu Chi University, Hualien 97004, Taiwan
- Correspondence:
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Abstract
Barrett's esophagus has been an important issue in clinical medicine for many years. In the 70 s of the last century, the association between gastroesophageal reflux disease and BE was detected. Only ten years later, the association between BE and adenocarcinoma of the esophagus was reported. Starting in the 90s in Europe, endoscopic resection and ablation have seen an expanding role in the management of BE. In analogy with other diseases, patients are undergoing individualized surveillance and treatment strategies. In non-neoplastic Barrett's esophagus, surveillance intervals are clearly defined by national and European guidelines. Only in case of malignant transformation of BE which may range from low-grade dysplasia to high-grade dysplasia and early Barrett's cancer, endoscopic therapy is indicated. At present, there are emerging techniques of artificial intelligence. Due to these rapid developments in BE management, it is important to keep an eye on the current status of BE management.
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21
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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: 0] [Impact Index Per Article: 0] [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
Abstract
Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett's esophagus and elaborate on potential artificial intelligence in the future.
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Affiliation(s)
- Michael Meinikheim
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany,Correspondence: Michael Meinikheim Department of Gastroenterology, University Hospital of Augsburg, Stenglinstr. 2, D-86156 Augsburg, Germany E-mail:
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Alanna Ebigbo
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
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22
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Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:7023731. [PMID: 36852218 PMCID: PMC9966565 DOI: 10.1155/2023/7023731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/16/2022] [Accepted: 02/06/2023] [Indexed: 02/20/2023]
Abstract
This study is to evaluate the feasibility of deep learning (DL) models in the multiclassification of reflux esophagitis (RE) endoscopic images, according to the Los Angeles (LA) classification for the first time. The images were divided into three groups, namely, normal, LA classification A + B, and LA C + D. The images from the HyperKvasir dataset and Suzhou hospital were divided into the training and validation datasets as a ratio of 4 : 1, while the images from Jintan hospital were the independent test set. The CNNs- or Transformer-architectures models (MobileNet, ResNet, Xception, EfficientNet, ViT, and ConvMixer) were transfer learning via Keras. The visualization of the models was proposed using Gradient-weighted Class Activation Mapping (Grad-CAM). Both in the validation set and the test set, the EfficientNet model showed the best performance as follows: accuracy (0.962 and 0.957), recall for LA A + B (0.970 and 0.925) and LA C + D (0.922 and 0.930), Marco-recall (0.946 and 0.928), Matthew's correlation coefficient (0.936 and 0.884), and Cohen's kappa (0.910 and 0.850), which was better than the other models and the endoscopists. According to the EfficientNet model, the Grad-CAM was plotted and highlighted the target lesions on the original images. This study developed a series of DL-based computer vision models with the interpretable Grad-CAM to evaluate the feasibility in the multiclassification of RE endoscopic images. It firstly suggests that DL-based classifiers show promise in the endoscopic diagnosis of esophagitis.
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23
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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
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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24
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Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med (Lausanne) 2022; 9:1018937. [PMID: 36405592 PMCID: PMC9672666 DOI: 10.3389/fmed.2022.1018937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
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Affiliation(s)
- Om Parkash
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Uswa Jiwani
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Rind
- Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
| | - Zahra Ali Padhani
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
| | - Arjumand Rizvi
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jai K. Das
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- *Correspondence: Jai K. Das,
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25
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Leenhardt R, Koulaouzidis A, Histace A, Baatrup G, Beg S, Bourreille A, de Lange T, Eliakim R, Iakovidis D, Dam Jensen M, Keuchel M, Margalit Yehuda R, McNamara D, Mascarenhas M, Spada C, Segui S, Smedsrud P, Toth E, Tontini GE, Klang E, Dray X, Kopylov U. Key research questions for implementation of artificial intelligence in capsule endoscopy. Therap Adv Gastroenterol 2022; 15:17562848221132683. [PMID: 36338789 PMCID: PMC9629556 DOI: 10.1177/17562848221132683] [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: 04/22/2022] [Accepted: 09/27/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. OBJECTIVES In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. DESIGN Modified three-round Delphi consensus online survey. METHODS The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. RESULTS Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). CONCLUSION In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.
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Affiliation(s)
| | - Anastasios Koulaouzidis
- Department of Social Medicine and Public Health, Pomeranian Medical University, Szczecin, Poland,Department of Surgery, Odense University Hospital, Odense, Denmark,Department of Clinical research, University of Southern Denmark, Odense, Denmark
| | - Aymeric Histace
- ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | - Gunnar Baatrup
- Department of Surgery, Odense University Hospital, Odense, Denmark,Department of Clinical research, University of Southern Denmark, Odense, Denmark
| | - Sabina Beg
- Department of Gastroenterology, Imperial College NHS Healthcare Trust, London, UK
| | - Arnaud Bourreille
- Nantes Université, CHU Nantes, Institut des maladies de l’appareil digestif (IMAD), Hépato-gastroentérologie, Nantes, France
| | - Thomas de Lange
- Department of Medicine and emergencies-Mölndal, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden,Department of Molecular and Clinical and Medicine, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Dimitris Iakovidis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Michael Dam Jensen
- Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
| | - Martin Keuchel
- Clinic for Internal Medicine, Agaplesion Bethesda Krankenhaus Bergedorf, Hamburg, Germany
| | - Reuma Margalit Yehuda
- Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Deirdre McNamara
- Trinity Academic Gastroenterology Group, Department of Clinical Medicine, Tallaght Hospital, Trinity College Dublin, Dublin, Ireland
| | - Miguel Mascarenhas
- Department of Gastroenterology, Centro Hospitalar São João, Porto, Portugal
| | - Cristiano Spada
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy,Digestive Endoscopy Unit, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Santi Segui
- Department of Mathematics and Computer Science, Universitat de Barcelona, Barcelona, Spain
| | - Pia Smedsrud
- Simula Metropolitan Centre for Digital Engineering, University of Oslo, Augere Medical AS, Oslo, Norway
| | - Ervin Toth
- Department of Gastroenterology, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Gian Eugenio Tontini
- Department of Pathophysiology and Transplantation, University of Milan and Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Eyal Klang
- Sheba ARC, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - Xavier Dray
- Sorbonne Université, Centre of Digestive Endoscopy, Hôpital Saint-Antoine, AP-HP, Paris, France,ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | - Uri Kopylov
- Department of Gastroenterology, Sheba Medical Center and Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
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26
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Uno K, Koike T, Hatta W, Saito M, Tanabe M, Masamune A. Development of Advanced Imaging and Molecular Imaging for Barrett's Neoplasia. Diagnostics (Basel) 2022; 12:2437. [PMID: 36292126 PMCID: PMC9600913 DOI: 10.3390/diagnostics12102437] [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] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
Barrett esophagus (BE) is a precursor to a life-threatening esophageal adenocarcinoma (EAC). Surveillance endoscopy with random biopsies is recommended for early intervention against EAC, but its adherence in the clinical setting is poor. Dysplastic lesions with flat architecture and patchy distribution in BE are hardly detected by high-resolution endoscopy, and the surveillance protocol entails issues of time and labor and suboptimal interobserver agreement for diagnosing dysplasia. Therefore, the development of advanced imaging technologies is necessary for Barrett's surveillance. Recently, non-endoscopic or endoscopic technologies, such as cytosponge, endocytoscopy, confocal laser endomicroscopy, autofluorescence imaging, and optical coherence tomography/volumetric laser endomicroscopy, were developed, but most of them are not clinically available due to the limited view field, expense of the equipment, and significant time for the learning curve. Another strategy is focused on the development of molecular biomarkers, which are also not ready to use. However, a combination of advanced imaging techniques together with specific biomarkers is expected to identify morphological abnormalities and biological disorders at an early stage in the surveillance. Here, we review recent developments in advanced imaging and molecular imaging for Barrett's neoplasia. Further developments in multiple biomarker panels specific for Barrett's HGD/EAC include wide-field imaging systems for targeting 'red flags', a high-resolution imaging system for optical biopsy, and a computer-aided diagnosis system with artificial intelligence, all of which enable a real-time and accurate diagnosis of dysplastic BE in Barrett's surveillance and provide information for precision medicine.
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Affiliation(s)
- Kaname Uno
- Division of Gastroenterology, Tohoku University Hospital, Sendai 981-8574, Japan
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27
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Tan JL, Chinnaratha MA, Woodman R, Martin R, Chen HT, Carneiro G, Singh R. Diagnostic Accuracy of Artificial Intelligence (AI) to Detect Early Neoplasia in Barrett's Esophagus: A Non-comparative Systematic Review and Meta-Analysis. Front Med (Lausanne) 2022; 9:890720. [PMID: 35814747 PMCID: PMC9258946 DOI: 10.3389/fmed.2022.890720] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
Background and Aims Artificial Intelligence (AI) is rapidly evolving in gastrointestinal (GI) endoscopy. We undertook a systematic review and meta-analysis to assess the performance of AI at detecting early Barrett's neoplasia. Methods We searched Medline, EMBASE and Cochrane Central Register of controlled trials database from inception to the 28th Jan 2022 to identify studies on the detection of early Barrett's neoplasia using AI. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies – 2 (QUADAS-2). A random-effects model was used to calculate pooled sensitivity, specificity, and diagnostics odds ratio (DOR). Forest plots and a summary of the receiving operating characteristics (SROC) curves displayed the outcomes. Heterogeneity was determined by I2, Tau2 statistics and p-value. The funnel plots and Deek's test were used to assess publication bias. Results Twelve studies comprising of 1,361 patients (utilizing 532,328 images on which the various AI models were trained) were used. The SROC was 0.94 (95% CI: 0.92–0.96). Pooled sensitivity, specificity and diagnostic odds ratio were 90.3% (95% CI: 87.1–92.7%), 84.4% (95% CI: 80.2–87.9%) and 48.1 (95% CI: 28.4–81.5), respectively. Subgroup analysis of AI models trained only on white light endoscopy was similar with pooled sensitivity and specificity of 91.2% (95% CI: 85.7–94.7%) and 85.1% (95% CI: 81.6%−88.1%), respectively. Conclusions AI is highly accurate at detecting early Barrett's neoplasia and validated for patients with at least high-grade dysplasia and above. Further well-designed prospective randomized controlled studies of all histopathological subtypes of early Barrett's neoplasia are needed to confirm these findings further.
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Affiliation(s)
- Jin Lin Tan
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Mohamed Asif Chinnaratha
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Richard Woodman
- Flinders Centre for Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Rory Martin
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Hsiang-Ting Chen
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Gustavo Carneiro
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Rajvinder Singh
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
- *Correspondence: Rajvinder Singh
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28
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Endoscopic Management of Barrett's Esophagus. Dig Dis Sci 2022; 67:1469-1479. [PMID: 35226224 DOI: 10.1007/s10620-022-07395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 12/09/2022]
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29
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Braden B. Radiofrequency ablation: time to think of poor responders. Endoscopy 2022; 54:241-242. [PMID: 34521119 DOI: 10.1055/a-1577-3187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Affiliation(s)
- Barbara Braden
- Translational Gastroenterology Unit, Oxford University Hospitals, Oxford, United Kingdom
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30
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Schmitz R, Werner R, Repici A, Bisschops R, Meining A, Zornow M, Messmann H, Hassan C, Sharma P, Rösch T. Artificial intelligence in GI endoscopy: stumbling blocks, gold standards and the role of endoscopy societies. Gut 2022; 71:451-454. [PMID: 33479051 DOI: 10.1136/gutjnl-2020-323115] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 02/06/2023]
Affiliation(s)
- Rüdiger Schmitz
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Rene Werner
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alessandro Repici
- Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.,Humanitas University, Department of Biomedical Sciences, Milan, Italy
| | - Raf Bisschops
- Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium
| | - Alexander Meining
- Department of Gastroenterology, University of Würzburg, Würzburg, Germany
| | - Michael Zornow
- Chair for Public and European Law, University of Göttingen, Göttingen, Germany
| | - Helmut Messmann
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, Veterans Affairs Medical Center and University of Kansas, Lawrence, Kansas, USA
| | - Thomas Rösch
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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31
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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: 19] [Impact Index Per Article: 9.5] [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]
Abstract
BACKGROUND Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs). AIM We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD. METHODS We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated. RESULTS For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively. CONCLUSIONS AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
| | - Brigida Barberio
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
- Department of Medical ScienceUniversity of FerraraFerraraItaly
| | - Matteo Martinato
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas UniversityVia Rita Levi Montalcini 420072 Pieve Emanuele, MilanItaly
- IRCCS Humanitas Research Hospitalvia Manzoni 5620089 Rozzano, MilanItaly
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical CenterKansas CityMissouriUSA
| | - Edoardo Savarino
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Nicola de Bortoli
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
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32
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Dutta AK. Are we Missing Barrett's Esophagus in Our Busy Endoscopy Practice? Improving Detection. JOURNAL OF DIGESTIVE ENDOSCOPY 2022. [DOI: 10.1055/s-0041-1741465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
AbstractBarrett's esophagus (BE) denotes the replacement of stratified squamous epithelium of esophagus by columnar epithelium. It is associated with a significantly increased risk of esophageal adenocarcinoma and hence patients with BE are advised endoscopic surveillance for early detection of dysplastic and neoplastic lesions. Esophageal cancer is the sixth most common cancer in terms of incidence and mortality in India. Around 15 to 25% of esophageal cancers are adenocarcinoma. BE is likely to be an important precursor of esophageal adenocarcinoma and we may be missing patients with BE in our busy endoscopy practice. The detection of BE may be improved by identifying high-risk groups, performing thorough endoscopic examination, and applying newer imaging techniques. The high-risk group includes patients with chronic gastroesophageal reflux disease, obesity, smoking, etc. During endoscopic examination, a careful assessment of the gastroesophageal junction and identification of important landmarks such as gastroesophageal junction and Z line are essential to detect BE. Management of BE depends on the detection of dysplasia and for this four quadrant mucosal biopsy is recommended every 1 to 2 cm. However, random biopsy samples only a small area of mucosa and advanced technologies for real-time detection of dysplasia and neoplasia may overcome this limitation. In this review, we discuss the current scenario of BE in India and ways to improve the detection of BE including dysplastic lesions.
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Affiliation(s)
- Amit Kumar Dutta
- Department of Gastrointestinal Sciences, Christian Medical College and Hospital, Vellore, Tamil Nadu, India
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33
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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: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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34
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van der Putten J, van der Sommen F. AIM in Barrett’s Esophagus. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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35
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Du W, Rao N, Yong J, Wang Y, Hu D, Gan T, Zhu L, Zeng B. Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning. J Med Syst 2021; 46:4. [PMID: 34807297 DOI: 10.1007/s10916-021-01782-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 10/11/2021] [Indexed: 02/05/2023]
Abstract
The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks.
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Affiliation(s)
- Wenju Du
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Nini Rao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Jiahao Yong
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yingchun Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dingcan Hu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, 610017, China.
| | - Linlin Zhu
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, 610017, China
| | - Bing Zeng
- School of Information and Communication Engineering, University Electronic Science and Technology of China, Chengdu, 610054, China
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36
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Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. THE VISUAL COMPUTER 2021; 39:2207-2222. [PMID: 34744231 PMCID: PMC8557108 DOI: 10.1007/s00371-021-02322-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/30/2021] [Indexed: 05/07/2023]
Abstract
With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Jixiang Zhang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei China
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37
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Li N, Jin SZ. Artificial intelligence and early esophageal cancer. Artif Intell Gastrointest Endosc 2021; 2:198-210. [DOI: 10.37126/aige.v2.i5.198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/23/2021] [Accepted: 10/27/2021] [Indexed: 02/06/2023] Open
Abstract
The development of esophageal cancer (EC) from early to advanced stage results in a high mortality rate and poor prognosis. Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society. Endoscopy is of great value for the diagnosis of EC, especially in the screening of Barrett’s esophagus and early EC. However, at present, endoscopy has a low diagnostic rate for early tumors. In recent years, artificial intelligence (AI) has made remarkable progress in the diagnosis of digestive system tumors, providing a new model for clinicians to diagnose and treat these tumors. In this review, we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results. We analyze and summarize the recent research on AI and early EC. We find that based on deep learning (DL) and convolutional neural network methods, the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis. Based on powerful computing and DL capabilities, the diagnostic accuracy of AI is close to or better than that of endoscopy specialists. We also analyze the shortcomings in the current AI research and corresponding improvement strategies. We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.
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Affiliation(s)
- Ning Li
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Shi-Zhu Jin
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
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38
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Pecere S, Milluzzo SM, Esposito G, Dilaghi E, Telese A, Eusebi LH. Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics (Basel) 2021; 11:diagnostics11091575. [PMID: 34573917 PMCID: PMC8469485 DOI: 10.3390/diagnostics11091575] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 12/16/2022] Open
Abstract
The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.
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Affiliation(s)
- Silvia Pecere
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Center for Endoscopic Research Therapeutics and Training (CERTT), Catholic University, 00168 Rome, Italy
- Correspondence: (S.P.); (L.H.E.)
| | - Sebastian Manuel Milluzzo
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Fondazione Poliambulanza Istituto Ospedaliero, 25121 Brescia, Italy
| | - Gianluca Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Emanuele Dilaghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Andrea Telese
- Department of Gastroenterology, University College London Hospital (UCLH), London NW1 2AF, UK;
| | - Leonardo Henry Eusebi
- Division of Gastroenterology and Endoscopy, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40121 Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, 40121 Bologna, Italy
- Correspondence: (S.P.); (L.H.E.)
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39
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Kim SH, Hong SJ. Current Status of Image-Enhanced Endoscopy for Early Identification of Esophageal Neoplasms. Clin Endosc 2021; 54:464-476. [PMID: 34304482 PMCID: PMC8357583 DOI: 10.5946/ce.2021.186] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/14/2021] [Indexed: 12/14/2022] Open
Abstract
Advanced esophageal cancer is known to have a poor prognosis. The early detection of esophageal neoplasms, including esophageal dysplasia and early esophageal cancer, is highly important for the accurate treatment of the disease. However, esophageal dysplasia and early esophageal cancer are usually subtle and can be easily missed. In addition to the early detection, proper pretreatment evaluation of the depth of invasion of esophageal cancer is very important for curative treatment. The progression of non-invasive diagnosis via image-enhanced endoscopy techniques has been shown to aid the early detection and estimate the depth of invasion of early esophageal cancer and, as a result, may provide additional opportunities for curative treatment. Here, we review the advancement of image-enhanced endoscopy-related technologies and their role in the early identification of esophageal neoplasms.
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Affiliation(s)
- Shin Hee Kim
- Digestive Disease Center and Research Institute, Department of Internal Medicine, Department of Biostatistics, Soonchunghyang University School of Medicine, Bucheon, Korea
| | - Su Jin Hong
- Digestive Disease Center and Research Institute, Department of Internal Medicine, Department of Biostatistics, Soonchunghyang University School of Medicine, Bucheon, Korea
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40
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Visaggi P, Barberio B, Ghisa M, Ribolsi M, Savarino V, Fassan M, Valmasoni M, Marchi S, de Bortoli N, Savarino E. Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence. Cancers (Basel) 2021; 13:cancers13133162. [PMID: 34202763 PMCID: PMC8268190 DOI: 10.3390/cancers13133162] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 12/16/2022] Open
Abstract
Simple Summary Esophageal cancer (EC) has a poor prognosis when the diagnosis is delayed, but curative treatment is possible if the diagnosis is timely. The disease subtly progresses before symptoms prompt patients to seek medical attention. Effective pre-symptomatic screening strategies may improve the outcome of the disease. Recent evidence provided insights into early diagnosis of EC via blood tests, advanced endoscopic imaging, and artificial intelligence. Accordingly, we reviewed available strategies to diagnose early EC. Abstract Esophageal cancer (EC) is the seventh most common cancer and the sixth cause of cancer death worldwide. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) account for up to 90% and 20% of all ECs, respectively. Clinical symptoms such as dysphagia, odynophagia, and bolus impaction occur late in the natural history of the disease, and the diagnosis is often delayed. The prognosis of ESCC and EAC is poor in advanced stages, being survival rates less than 20% at five years. However, when the diagnosis is achieved early, curative treatment is possible, and survival exceeds 80%. For these reasons, mass screening strategies for EC are highly desirable, and several options are currently under investigation. Blood biomarkers offer an inexpensive, non-invasive screening strategy for cancers, and novel technologies have allowed the identification of candidate markers for EC. The esophagus is easily accessible via endoscopy, and endoscopic imaging represents the gold standard for cancer surveillance. However, lesion recognition during endoscopic procedures is hampered by interobserver variability. To fill this gap, artificial intelligence (AI) has recently been explored and provided encouraging results. In this review, we provide a summary of currently available options to achieve early diagnosis of EC, focusing on blood biomarkers, advanced endoscopy, and AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56124 Pisa, Italy; (P.V.); (S.M.); (N.d.B.)
| | - Brigida Barberio
- Division of Gastroenterology, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35121 Padua, Italy; (B.B.); (M.G.)
| | - Matteo Ghisa
- Division of Gastroenterology, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35121 Padua, Italy; (B.B.); (M.G.)
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, 00128 Roma, Italy;
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, 16143 Genoa, Italy;
| | - Matteo Fassan
- Surgical Pathology & Cytopathology Unit, Department of Medicine (DIMED), University of Padua, 35121 Padua, Italy;
| | - Michele Valmasoni
- Department of Surgical, Oncological and Gastroenterological Sciences, Center for Esophageal Disease, University of Padova, 35124 Padova, Italy;
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56124 Pisa, Italy; (P.V.); (S.M.); (N.d.B.)
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56124 Pisa, Italy; (P.V.); (S.M.); (N.d.B.)
| | - Edoardo Savarino
- Division of Gastroenterology, Department of Surgery, Oncology and Gastroenterology, University of Padua, 35121 Padua, Italy; (B.B.); (M.G.)
- Correspondence:
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41
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Yan T, Wong PK, Qin YY. Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review. World J Gastroenterol 2021; 27:2531-2544. [PMID: 34092974 PMCID: PMC8160615 DOI: 10.3748/wjg.v27.i20.2531] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 03/27/2021] [Accepted: 04/09/2021] [Indexed: 02/06/2023] Open
Abstract
Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy. However, unlike GI cancers, precancerous lesions in the upper GI tract can be subtle and difficult to detect. Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, might help endoscopists identify the precancerous lesions and reduce interobserver variability. In this review, a systematic literature search was undertaken of the Web of Science, PubMed, Cochrane Library and Embase, with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract. The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized. The challenges and recommendations targeting this field are comprehensively analyzed for future research.
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Affiliation(s)
- Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Ye-Ying Qin
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
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42
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van der Putten J, van der Sommen F. AIM in Barrett’s Esophagus. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_166-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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43
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Samarasena JB. Guns, germs, and steel…and artificial intelligence. Gastrointest Endosc 2021; 93:99-101. [PMID: 33353643 DOI: 10.1016/j.gie.2020.07.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Jason B Samarasena
- H. H. Chao Comprehensive Digestive Disease Center, Division of Gastroenterology and Hepatology, University of California Irvine, Orange, California, USA
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44
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de Souza LA, Passos LA, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP. Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks. Comput Biol Med 2020; 126:104029. [PMID: 33059236 DOI: 10.1016/j.compbiomed.2020.104029] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 09/08/2020] [Accepted: 09/30/2020] [Indexed: 12/12/2022]
Abstract
Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.
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Affiliation(s)
- Luis A de Souza
- Department of Computing, São Carlos Federal University, UFSCar, Brazil; Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany
| | - Leandro A Passos
- Department of Computing, São Paulo State University, UNESP, Brazil
| | - Robert Mendel
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany; Regensburg Center of Health Sciences and Technology (RCHST), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany
| | - Alanna Ebigbo
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Andreas Probst
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany; Regensburg Center of Health Sciences and Technology (RCHST), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Germany
| | - João P Papa
- Department of Computing, São Paulo State University, UNESP, Brazil.
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