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Dong Z, Zhao X, Zheng H, Zheng H, Chen D, Cao J, Xiao Z, Sun Y, Zhuang Q, Wu S, Xia J, Ning M, Qin B, Zhou H, Bao J, Wan X. Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study. EClinicalMedicine 2024; 73:102656. [PMID: 38828130 PMCID: PMC11137341 DOI: 10.1016/j.eclinm.2024.102656] [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: 02/24/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024] Open
Abstract
Background Gastrointestinal stromal tumors (GISTs) represent the most prevalent type of subepithelial lesions (SELs) with malignant potential. Current imaging tools struggle to differentiate GISTs from leiomyomas. This study aimed to create and assess a real-time artificial intelligence (AI) system using endoscopic ultrasonography (EUS) images to differentiate between GISTs and leiomyomas. Methods The AI system underwent development and evaluation using EUS images from 5 endoscopic centers in China between January 2020 and August 2023. EUS images of 1101 participants with SELs were retrospectively collected for AI system development. A cohort of 241 participants with SELs was recruited for external AI system evaluation. Another cohort of 59 participants with SELs was prospectively enrolled to assess the real-time clinical application of the AI system. The AI system's performance was compared to that of endoscopists. This study is registered with Chictr.org.cn, Number ChiCT2000035787. Findings The AI system displayed an area under the curve (AUC) of 0.948 (95% CI: 0.921-0.969) for discriminating GISTs and leiomyomas. The AI system's accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) reached 91.7% (95% CI 87.5%-94.6%), 90.3% (95% CI 83.4%-94.5%), 93.0% (95% CI 87.2%-96.3%), 91.9% (95% CI 85.3%-95.7%), and 91.5% (95% CI 85.5%-95.2%), respectively. Moreover, the AI system exhibited excellent performance in diagnosing ≤20 mm SELs (ACC 93.5%, 95% CI 0.900-0.969). In a prospective real-time clinical application trial, the AI system achieved an AUC of 0.865 (95% CI 0.764-0.966) and 0.864 (95% CI 0.762-0.966) for GISTs and leiomyomas diagnosis, respectively, markedly surpassing endoscopists [AUC 0.698 (95% CI 0.562-0.834) for GISTs and AUC 0.695 (95% CI 0.546-0.825) for leiomyomas]. Interpretation We successfully developed a real-time AI-assisted EUS diagnostic system. The incorporation of the real-time AI system during EUS examinations can assist endoscopists in rapidly and accurately differentiating various types of SELs in clinical practice, facilitating improved diagnostic and therapeutic decision-making. Funding Science and Technology Commission Foundation of Shanghai Municipality, Science and Technology Commission Foundation of the Xuhui District, the Interdisciplinary Program of Shanghai Jiao Tong University and the Research Funds of Shanghai Sixth people's Hospital.
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Affiliation(s)
- Zhixia Dong
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangyun Zhao
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hangbin Zheng
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - HanYao Zheng
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - Dafan Chen
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jia Cao
- Endoscopy Center, Department of Gastroenterology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zili Xiao
- Digestive Endoscopic Department, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Yunwei Sun
- Department of Gastroenterology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Zhuang
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wu
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Xia
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Ning
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Binjie Qin
- School of Biomedical Engineering, Shanghai Jiao Tong University, China
| | - Hui Zhou
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinsong Bao
- College of Mechanical Engineering, Dong Hua University, Shanghai, China
| | - Xinjian Wan
- Digestive Endoscopic Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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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. [PMID: 38547927 DOI: 10.1055/a-2296-5696] [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: 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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Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, González-Suárez B, Keuchel M, McAlindon M, Finta Á, Rosztóczy A, Dray X, Salvi D, Riccioni ME, Benamouzig R, Chattree A, Humphries A, Saurin JC, Despott EJ, Murino A, Johansson GW, Giordano A, Baltes P, Sidhu R, Szalai M, Helle K, Nemeth A, Nowak T, Lin R, Costamagna G. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health 2024; 6:e345-e353. [PMID: 38670743 DOI: 10.1016/s2589-7500(24)00048-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/20/2024] [Accepted: 03/04/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints. METHODS Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349. FINDINGS From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6-19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001). INTERPRETATION AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. FUNDING ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.
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Affiliation(s)
- Cristiano Spada
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Stefania Piccirelli
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Cesare Hassan
- IRCCS Humanitas Research Hospital, Department of Biomedical Sciences, Rozzano, Milan, Italy
| | - Clarissa Ferrari
- Unit of Research and Clinical Trials, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Ervin Toth
- Skåne University Hospital, Lund University, Department of Gastroenterology, Malmö, Sweden
| | - Begoña González-Suárez
- Hospital Clínic of Barcelona, Endoscopy Unit, Gastroenterology Department, Barcelona, Spain
| | - Martin Keuchel
- Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, Clinic for Internal Medicine, Hamburg, Germany
| | - Marc McAlindon
- Sheffield Teaching Hospitals NHS Trust, Academic Department of Gastroenterology and Hepatology, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Ádám Finta
- Endo-Kapszula Health Centre and Endoscopy Unit, Department of Gastroenterology, Székesfehérvár, Hungary
| | - András Rosztóczy
- University of Szeged, Department of Internal Medicine, Szeged, Hungary
| | - Xavier Dray
- Sorbonne University, Saint Antoine Hospital, APHP, Centre for Digestive Endoscopy, Paris, France
| | - Daniele Salvi
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Maria Elena Riccioni
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Digestive Endoscopy Unit, Rome, Italy
| | - Robert Benamouzig
- Hôpital Avicenne, Université Paris 13, Service de Gastroenterologie, Bobigny, France
| | - Amit Chattree
- South Tyneside and Sunderland NHS Foundation Trust, Gastroenterology, Stockton-on-Tees, UK
| | - Adam Humphries
- St Mark's Hospital and Academic Institute, Department of Gastroenterology, Middlesex, UK
| | - Jean-Christophe Saurin
- Hospices Civils de Lyon-Centre Hospitalier Universitaire, Gastroenterology Department, Lyon, France
| | - Edward J Despott
- The Royal Free Hospital and University College London (UCL) Institute for Liver and Digestive Health, Royal Free Unit for Endoscopy, London, UK
| | - Alberto Murino
- The Royal Free Hospital and University College London (UCL) Institute for Liver and Digestive Health, Royal Free Unit for Endoscopy, London, UK
| | | | - Antonio Giordano
- Hospital Clínic of Barcelona, Endoscopy Unit, Gastroenterology Department, Barcelona, Spain
| | - Peter Baltes
- Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, Clinic for Internal Medicine, Hamburg, Germany
| | - Reena Sidhu
- Sheffield Teaching Hospitals NHS Trust, Academic Department of Gastroenterology and Hepatology, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Milan Szalai
- Endo-Kapszula Health Centre and Endoscopy Unit, Department of Gastroenterology, Székesfehérvár, Hungary
| | - Krisztina Helle
- University of Szeged, Department of Internal Medicine, Szeged, Hungary
| | - Artur Nemeth
- Skåne University Hospital, Lund University, Department of Gastroenterology, Malmö, Sweden
| | | | - Rong Lin
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Gastroenterology, Wuhan, China
| | - Guido Costamagna
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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Okumura T, Imai K, Misawa M, Kudo SE, Hotta K, Ito S, Kishida Y, Takada K, Kawata N, Maeda Y, Yoshida M, Yamamoto Y, Minamide T, Ishiwatari H, Sato J, Matsubayashi H, Ono H. Evaluating false-positive detection in a computer-aided detection system for colonoscopy. J Gastroenterol Hepatol 2024; 39:927-934. [PMID: 38273460 DOI: 10.1111/jgh.16491] [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: 10/16/2023] [Revised: 12/21/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND AIM Computer-aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false-positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP. METHODS We analyzed CADe-assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre- and post-update using 1:1 propensity score matching. RESULTS During the study period, 191 colonoscopy videos (94 and 97 in the pre- and post-update groups, respectively) were recorded. Propensity score matching resulted in 146 videos (73 in each group). The mean number of FPs and median ET per colonoscopy were significantly lower in the post-update group than those in the pre-update group (4.2 ± 3.7 vs 18.1 ± 11.1; P < 0.001 and 0 vs 16 s; P < 0.001, respectively). Mucosal tags, bubbles, and folds had the strongest association with decreased FP post-update (pre-update vs post-update: 4.3 ± 3.6 vs 0.4 ± 0.8, 0.32 ± 0.70 vs 0.04 ± 0.20, and 8.6 ± 6.7 vs 1.6 ± 1.7, respectively). There was no significant decrease in the true positive rate (post-update vs pre-update: 95.0% vs 99.2%; P = 0.09) or the adenoma detection rate (post-update vs pre-update: 52.1% vs 49.3%; P = 0.87). CONCLUSIONS The updated CADe can reduce FP without impairing polyp detection. A reduction in FP may help relieve the burden on endoscopists.
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Affiliation(s)
- Taishi Okumura
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kenichiro Imai
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kinichi Hotta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Sayo Ito
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Kazunori Takada
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Noboru Kawata
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yuki Maeda
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masao Yoshida
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Yoichi Yamamoto
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | | | - Junya Sato
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Hiroyuki Ono
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
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Wang L, Liu ZQ, Ma LL, Zhou PH. Endoscopic resection of a massive ileal tumor causing intussusception with incomplete obstruction. J Dig Dis 2024; 25:266-268. [PMID: 38778749 DOI: 10.1111/1751-2980.13273] [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] [Received: 10/25/2023] [Revised: 03/10/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024]
Affiliation(s)
- Li Wang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Zu Qiang Liu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Li Li Ma
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Ping Hong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
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Mascarenhas Saraiva M, Spindler L, Fathallah N, Beaussier H, Mamma C, Ribeiro T, Afonso J, Carvalho M, Moura R, Cardoso P, Mendes F, Martins M, Adam J, Ferreira J, Macedo G, de Parades V. Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors. Clin Transl Gastroenterol 2024; 15:e00681. [PMID: 38270249 DOI: 10.14309/ctg.0000000000000681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation). METHODS A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated. RESULTS The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00. DISCUSSION The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
| | - Lucas Spindler
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Nadia Fathallah
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Hélene Beaussier
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Célia Mamma
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Mariana Carvalho
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Rita Moura
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Julien Adam
- Department of Pathology, GH Paris Saint-Joseph, Paris, France
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
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van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5:90574. [DOI: 10.37126/aige.v5.i1.90574] [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: 12/07/2023] [Revised: 01/11/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.
AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.
METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.
RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.
CONCLUSION Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.
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Affiliation(s)
- Quirine Eunice Wennie van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Ramon M Schreuder
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
| | - Ayla Thijssen
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Nikoo Dehghani
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Thom Scheeve
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, Postbus 616, 6200 MD Maastricht, Netherlands
- School for Public Health and Primary Care, Maastricht University, Maastricht 6200 MD, Netherlands
| | | | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Ad A M Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024:S1590-8658(24)00249-4. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [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: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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Wang L, Liu ZQ, Zhang JY, Li QL, Chen SY, Zhong YS, Zhang YQ, Chen WF, Qin WZ, Hu JW, Cai MY, Yao LQ, Ma LL, Zhou PH. Feasibility and safety of endoscopic resection for the jejunoileal lesions. J Gastroenterol Hepatol 2024; 39:527-534. [PMID: 37974384 DOI: 10.1111/jgh.16413] [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: 08/09/2023] [Revised: 10/23/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Endoscopic resection (ER) for jejunoileal lesions (JILs) has been technically challenging. We aimed to characterize the clinicopathologic characteristics, feasibility, and safety of ER for JILs. METHOD We retrospectively investigated 52 patients with JILs who underwent ER from January 2012 to February 2022. We collected and analyzed clinicopathological characteristics, procedure-related parameters, outcomes, and follow-up data. RESULTS The mean age was 49.4 years. Of the 52 JILs, 33 ileal tumors within 20 cm from the ileocecal valve were resected with colonoscopy, while 19 tumors in the jejunum or the ileum over 20 cm from the ileocecal valve received enteroscopy resection. The mean procedure duration was 49.0 min. The en bloc resection and en bloc with R0 resection rates were 86.5% and 84.6%, respectively. Adverse events (AEs) included one (1.9%) major AE (delayed bleeding) and five (9.6%) minor AEs. During a median follow-up of 36.5 months, two patients had local recurrence (3.8%), while none had metastases. The 5-year recurrence-free survival (RFS) and disease-specific survival (DSS) were 92.9% and 94.1%, respectively. Compared with the enteroscopy group, overall AEs were significantly lower in the colonoscopy group (P < 0.05), but no statistical differences were observed in RFS (P = 0.412) and DSS (P = 0.579). There were no significant differences in AEs, RFS, and DSS between the endoscopic submucosal dissection (ESD) and the endoscopic mucosal resection (EMR) group. CONCLUSIONS ER of JILs has favorable short-term and long-term outcomes. Both ESD and EMR can safely and effectively resect JILs in appropriately selected cases.
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Affiliation(s)
- Li Wang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Zu-Qiang Liu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Ji-Yuan Zhang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Quan-Lin Li
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Shi-Yao Chen
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Yun-Shi Zhong
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Yi-Qun Zhang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Wei-Feng Chen
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Wen-Zheng Qin
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Jian-Wei Hu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Ming-Yan Cai
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Li-Qing Yao
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Li-Li Ma
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Ping-Hong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
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Mohan BP. Artificial intelligence-aided colonoscopy in 10 years. Gastrointest Endosc 2024; 99:452-453. [PMID: 38000478 DOI: 10.1016/j.gie.2023.11.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/15/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023]
Affiliation(s)
- Babu P Mohan
- Gastroenterology and Hepatology, Orlando Gastroenterology PA, Orlando, Forida, USA
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Lau LHS, Ho JCL, Lai JCT, Ho AHY, Wu CWK, Lo VWH, Lai CMS, Scheppach MW, Sia F, Ho KHK, Xiao X, Yip TCF, Lam TYT, Kwok HYH, Chan HCH, Lui RN, Chan TT, Wong MTL, Ho MF, Ko RCW, Hon SF, Chu S, Futaba K, Ng SSM, Yip HC, Tang RSY, Wong VWS, Chan FKL, Chiu PWY. Effect of Real-Time Computer-Aided Polyp Detection System (ENDO-AID) on Adenoma Detection in Endoscopists-in-Training: A Randomized Trial. Clin Gastroenterol Hepatol 2024; 22:630-641.e4. [PMID: 37918685 DOI: 10.1016/j.cgh.2023.10.019] [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: 07/17/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND The effect of computer-aided polyp detection (CADe) on adenoma detection rate (ADR) among endoscopists-in-training remains unknown. METHODS We performed a single-blind, parallel-group, randomized controlled trial in Hong Kong between April 2021 and July 2022 (NCT04838951). Eligible subjects undergoing screening/surveillance/diagnostic colonoscopies were randomized 1:1 to receive colonoscopies with CADe (ENDO-AID[OIP-1]) or not (control) during withdrawal. Procedures were performed by endoscopists-in-training with <500 procedures and <3 years' experience. Randomization was stratified by patient age, sex, and endoscopist experience (beginner vs intermediate level, <200 vs 200-500 procedures). Image enhancement and distal attachment devices were disallowed. Subjects with incomplete colonoscopies or inadequate bowel preparation were excluded. Treatment allocation was blinded to outcome assessors. The primary outcome was ADR. Secondary outcomes were ADR for different adenoma sizes and locations, mean number of adenomas, and non-neoplastic resection rate. RESULTS A total of 386 and 380 subjects were randomized to CADe and control groups, respectively. The overall ADR was significantly higher in the CADe group than in the control group (57.5% vs 44.5%; adjusted relative risk, 1.41; 95% CI, 1.17-1.72; P < .001). The ADRs for <5 mm (40.4% vs 25.0%) and 5- to 10-mm adenomas (36.8% vs 29.2%) were higher in the CADe group. The ADRs were higher in the CADe group in both the right colon (42.0% vs 30.8%) and left colon (34.5% vs 27.6%), but there was no significant difference in advanced ADR. The ADRs were higher in the CADe group among beginner (60.0% vs 41.9%) and intermediate-level (56.5% vs 45.5%) endoscopists. Mean number of adenomas (1.48 vs 0.86) and non-neoplastic resection rate (52.1% vs 35.0%) were higher in the CADe group. CONCLUSIONS Among endoscopists-in-training, the use of CADe during colonoscopies was associated with increased overall ADR. (ClinicalTrials.gov, Number: NCT04838951).
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Affiliation(s)
- Louis H S Lau
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Jacky C L Ho
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Jimmy C T Lai
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Agnes H Y Ho
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Claudia W K Wu
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Vincent W H Lo
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Carol M S Lai
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Markus W Scheppach
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Gastroenterology, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Felix Sia
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR
| | - Kyle H K Ho
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR
| | - Xiang Xiao
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Medical Data Analytic Centre, The Chinese University of Hong Kong, Hong Kong SAR
| | - Terry C F Yip
- Medical Data Analytic Centre, The Chinese University of Hong Kong, Hong Kong SAR
| | - Thomas Y T Lam
- Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong SAR
| | - Hanson Y H Kwok
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Heyson C H Chan
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Rashid N Lui
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Ting-Ting Chan
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Marc T L Wong
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Man-Fung Ho
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Rachel C W Ko
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Sok-Fei Hon
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Simon Chu
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Koari Futaba
- Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Simon S M Ng
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Hon-Chi Yip
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Raymond S Y Tang
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Vincent W S Wong
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Francis K L Chan
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR
| | - Philip W Y Chiu
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR; Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR.
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Oh DJ, Hwang Y, Kim SH, Nam JH, Jung MK, Lim YJ. Reading of small bowel capsule endoscopy after frame reduction using an artificial intelligence algorithm. BMC Gastroenterol 2024; 24:80. [PMID: 38388860 PMCID: PMC10885475 DOI: 10.1186/s12876-024-03156-4] [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: 01/24/2023] [Accepted: 02/04/2024] [Indexed: 02/24/2024] Open
Abstract
OBJECTIVES Poorly visualized images that appear during small bowel capsule endoscopy (SBCE) can confuse the interpretation of small bowel lesions and increase the physician's workload. Using a validated artificial intelligence (AI) algorithm that can evaluate the mucosal visualization, we aimed to assess whether SBCE reading after the removal of poorly visualized images could affect the diagnosis of SBCE. METHODS A study was conducted to analyze 90 SBCE cases in which a small bowel examination was completed. Two experienced endoscopists alternately performed two types of readings. They used the AI algorithm to remove poorly visualized images for the frame reduction reading (AI user group) and conducted whole frame reading without AI (AI non-user group) for the same patient. A poorly visualized image was defined as an image with < 50% mucosal visualization. The study outcomes were diagnostic concordance and reading time between the two groups. The SBCE diagnosis was classified as Crohn's disease, bleeding, polyp, angiodysplasia, and nonspecific finding. RESULTS The final SBCE diagnoses between the two groups showed statistically significant diagnostic concordance (k = 0.954, p < 0.001). The mean number of lesion images was 3008.5 ± 9964.9 in the AI non-user group and 1401.7 ± 4811.3 in the AI user group. There were no cases in which lesions were completely removed. Compared with the AI non-user group (120.9 min), the reading time was reduced by 35.6% in the AI user group (77.9 min). CONCLUSIONS SBCE reading after reducing poorly visualized frames using the AI algorithm did not have a negative effect on the final diagnosis. SBCE reading method integrated with frame reduction and mucosal visualization evaluation will help improve AI-assisted SBCE interpretation.
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Affiliation(s)
- Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang, 10326, Republic of Korea
| | - Youngbae Hwang
- Department of Electronics Engineering, Chungbuk National University, Cheongju, Republic of Korea
| | - Sang Hoon Kim
- Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Chung-Ang University College of Medicine, Gwangmyeong, Republic of Korea
| | - Ji Hyung Nam
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang, 10326, Republic of Korea
| | - Min Kyu Jung
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang, 10326, Republic of Korea.
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Maan ADI, Sharma P, Koch AD. Curative criteria for endoscopic treatment of oesophageal adenocarcinoma. Best Pract Res Clin Gastroenterol 2024; 68:101886. [PMID: 38522884 DOI: 10.1016/j.bpg.2024.101886] [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: 08/28/2023] [Accepted: 01/23/2024] [Indexed: 03/26/2024]
Abstract
The incidence of oesophageal adenocarcinoma has been increasing rapidly in the Western world. A well-known risk factor for developing this type of tumour is reflux disease, which can cause metaplasia from the squamous cell mucosa to columnar epithelium (Barrett's Oesophagus) which can progress to dysplasia and eventually adenocarcinoma. With the rise of the incidence of oesophageal adenocarcinoma, research on the best way to manage this disease is of great importance and has changed treatment modalities over the last decades. The gold standard for superficial adenocarcinoma has shifted from surgical to endoscopic management when certain criteria are met. This review will discuss the different curative criteria for endoscopic treatment of oesophageal adenocarcinoma.
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Affiliation(s)
- Annemijn D I Maan
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, Dr. Molewaterplein 40, 3015 GD, Rotterdam, the Netherlands.
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas and VA Medical Centre, 4801 E Linwood Blvd, Kansas City, USA.
| | - Arjun D Koch
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, Dr. Molewaterplein 40, 3015 GD, Rotterdam, the Netherlands.
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Libanio D, Antonelli G, Marijnissen F, Spaander MC, Hassan C, Dinis-Ribeiro M, Areia M. Combined gastric and colorectal cancer endoscopic screening may be cost-effective in Europe with the implementation of artificial intelligence: an economic evaluation. Eur J Gastroenterol Hepatol 2024; 36:155-161. [PMID: 38131423 DOI: 10.1097/meg.0000000000002680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
BACKGROUND/AIMS Endoscopic screening for gastric cancer (GC) is not recommended in low-intermediate incidence countries. Artificial intelligence (AI) has high accuracy in GC detection and might increase the cost-effectiveness of screening strategies. We aimed to assess the cost-effectiveness of AI for GC detection in settings with different GC incidence and different accuracies of AI systems. METHODS Cost-effectiveness analysis (using Markov model) comparing different screening strategies (no screening versus single esophagogastroduodenoscopy (EGD) at 50 years versus stand-alone EGD every 5/10 years versus combined EGD and screening colonoscopy once or twice per decade in Netherlands, Italy and Portugal) with variable AI accuracy settings. The primary outcome was the incremental cost-effectiveness ratio of the different strategies versus no screening. Deterministic and probabilistic sensitivity analyses were conducted. RESULTS Without AI, one single EGD at 50 years (Netherlands, Italy, Portugal), EGD combined with screening colonoscopy once per decade (Italy and Portugal) and EGD combined with screening colonoscopy twice per decade (Portugal) are cost-effective when compared with no screening. If AI increases the accuracy of EGD by at least 1% in comparison to the accuracy of white-light endoscopy accuracy (89%), combined screening twice per decade also becomes cost-effective in Italy. If AI accuracy reaches at least 96%, combined screening once per decade is also cost-effective in the Netherlands. DISCUSSION In European countries, AI-assisted EGD may improve the cost-effectiveness of GC screening with combined EGD and screening colonoscopy. The actual effect of AI on cost-effectiveness may vary dependent on the accuracy and costs of the AI system.
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Affiliation(s)
- Diogo Libanio
- Department of Gastroenterology, Porto Comprehensive Cancer Center/ RISE@CI-IPOP (Health Research Network)
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy
| | - Fleur Marijnissen
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Maanon Cw Spaander
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center/ RISE@CI-IPOP (Health Research Network)
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
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Schöler J, Alavanja M, de Lange T, Yamamoto S, Hedenström P, Varkey J. Impact of AI-aided colonoscopy in clinical practice: a prospective randomised controlled trial. BMJ Open Gastroenterol 2024; 11:e001247. [PMID: 38290758 PMCID: PMC10870789 DOI: 10.1136/bmjgast-2023-001247] [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: 09/04/2023] [Accepted: 01/11/2024] [Indexed: 02/01/2024] Open
Abstract
OBJECTIVE Colorectal cancer (CRC) has a significant role in cancer-related mortality. Colonoscopy, combined with adenoma removal, has proven effective in reducing CRC incidence. However, suboptimal colonoscopy quality often leads to missed polyps. The impact of artificial intelligence (AI) on adenoma and polyp detection rate (ADR, PDR) is yet to be established. DESIGN We conducted a randomised controlled trial at Sahlgrenska University Hospital in Sweden. Patients underwent colonoscopy with or without the assistance of AI (AI-C or conventional colonoscopy (CC)). Examinations were performed with two different AI systems, that is, Fujifilm CADEye and Medtronic GI Genius. The primary outcome was ADR. RESULTS Among 286 patients, 240 underwent analysis (average age: 66 years). The ADR was 42% for all patients, and no significant difference emerged between AI-C and CC groups (41% vs 43%). The overall PDR was 61%, with a trend towards higher PDR in the AI-C group. Subgroup analysis revealed higher detection rates for sessile serrated lesions (SSL) with AI assistance (AI-C 22%, CC 11%, p=0.004). No difference was noticed in the detection of polyps or adenomas per colonoscopy. Examinations were most often performed by experienced endoscopists, 78% (n=86 AI-C, 100 CC). CONCLUSION Amidst the ongoing AI integration, ADR did not improve with AI. Particularly noteworthy is the enhanced detection rates for SSL by AI assistance, especially since they pose a risk for postcolonoscopy CRC. The integration of AI into standard colonoscopy practice warrants further investigation and the development of improved software might be necessary before enforcing its mandatory implementation. TRIAL REGISTRATION NUMBER NCT05178095.
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Affiliation(s)
- Johanna Schöler
- Medical Department, Sahlgrenska University Hospital, Goteborg, Sweden
| | - Marko Alavanja
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Goteborg, Sweden
- Department of Medicine, Sahlgrenska University Hospital, Goteborg, Sweden
| | - Thomas de Lange
- Medical Department, Sahlgrenska University Hospital, Goteborg, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Goteborg, Sweden
| | - Shunsuke Yamamoto
- Department of Medicine, Sahlgrenska University Hospital, Goteborg, Sweden
- Department of Gastroenterology and Hepatology, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Per Hedenström
- Medical Department, Sahlgrenska University Hospital, Goteborg, Sweden
- Department of Medicine, Sahlgrenska University Hospital, Goteborg, Sweden
| | - Jonas Varkey
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Goteborg, Sweden
- Division of Gastroenterology, Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
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16
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Waddingham W, Graham DG, Banks MR. Latest Advances in Endoscopic Detection of Oesophageal and Gastric Neoplasia. Diagnostics (Basel) 2024; 14:301. [PMID: 38337817 PMCID: PMC10855581 DOI: 10.3390/diagnostics14030301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
Endoscopy is the gold standard for the diagnosis of cancers and cancer precursors in the oesophagus and stomach. Early detection of upper GI cancers requires high-quality endoscopy and awareness of the subtle features these lesions carry. Endoscopists performing surveillance of high-risk patients including those with Barrett's oesophagus, previous squamous neoplasia or chronic atrophic gastritis should be familiar with endoscopic features, classification systems and sampling techniques to maximise the detection of early cancer. In this article, we review the current approach to diagnosis of these conditions and the latest advanced imaging and diagnostic techniques.
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Affiliation(s)
- William Waddingham
- Department of Gastroenterology, Royal Free London NHS Foundation Trust, London NW3 2QG, UK
| | - David G. Graham
- Department of Gastroenterology, University College London NHS Foundation Trust, London NW1 2BU, UK
| | - Matthew R. Banks
- Department of Gastroenterology, University College London NHS Foundation Trust, London NW1 2BU, UK
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17
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Mota J, Almeida MJ, Mendes F, Martins M, Ribeiro T, Afonso J, Cardoso P, Cardoso H, Andrade P, Ferreira J, Mascarenhas M, Macedo G. From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy? Diagnostics (Basel) 2024; 14:291. [PMID: 38337807 PMCID: PMC10855436 DOI: 10.3390/diagnostics14030291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/09/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
- Digestive Artificial Intelligence Development, R. Alfredo Allen 455-461, 4200-135 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- ManopH Gastroenterology Clinic, R. de Sá da Bandeira 752, 4000-432 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal (G.M.)
- WGO Gastroenterology and Hepatology Training Center, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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18
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Brodersen JB, Jensen MD, Leenhardt R, Kjeldsen J, Histace A, Knudsen T, Dray X. Artificial Intelligence-assisted Analysis of Pan-enteric Capsule Endoscopy in Patients with Suspected Crohn's Disease: A Study on Diagnostic Performance. J Crohns Colitis 2024; 18:75-81. [PMID: 37527554 DOI: 10.1093/ecco-jcc/jjad131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Indexed: 08/03/2023]
Abstract
BACKGROUND AND AIM Pan-enteric capsule endoscopy [PCE] is a highly sensitive but time-consuming tool for detecting pathology. Artificial intelligence [AI] algorithms might offer a possibility to assist in the review and reduce the analysis time of PCE. This study examines the agreement between PCE assessments aided by AI technology and standard evaluations, in patients suspected of Crohn's disease [CD]. METHOD PCEs from a prospective, blinded, multicentre study, including patients suspected of CD, were processed by the deep learning solution AXARO® [Augmented Endoscopy, Paris, France]. Based on the image output, two observers classified the patient's PCE as normal or suggestive of CD, ulcerative colitis, or cancer. The primary outcome was per-patient sensitivities and specificities for detecting CD and inflammatory bowel disease [IBD]. Complete reading of PCE served as the reference standard. RESULTS A total of 131 patients' PCEs were analysed, with a median recording time of 303 min. The AXARO® framework reduced output to a median of 470 images [2.1%] per patient, and the pooled median review time was 3.2 min per patient. For detecting CD, the observers had a sensitivity of 96% and 92% and a specificity of 93% and 90%, respectively. For the detection of IBD, both observers had a sensitivity of 97% and had a specificity of 91% and 90%, respectively. The negative predictive value was 95% for CD and 97% for IBD. CONCLUSIONS Using the AXARO® framework reduced the initial review time substantially while maintaining high diagnostic accuracy-suggesting its use as a rapid tool to rule out IBD in PCEs of patients suspected of Crohn's disease.
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Affiliation(s)
- Jacob Broder Brodersen
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Michael Dam Jensen
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
| | - Romain Leenhardt
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
- Sorbonne University, Center for Digestive Endoscopy, Saint-Antoine Hospital, APHP, Paris, France
| | - Jens Kjeldsen
- Department of Medical Gastroenterology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- OPEN Odense Patient Data Explorative Network, Odense University Hospital, Odense, Denmark
| | - Aymeric Histace
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
| | - Torben Knudsen
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Xavier Dray
- Équipes Traitement de l'Information et Systèmes, ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, Cergy, France
- Sorbonne University, Center for Digestive Endoscopy, Saint-Antoine Hospital, APHP, Paris, France
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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19
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Fantasia S, Cortegoso Valdivia P, Kayali S, Koulaouzidis G, Pennazio M, Koulaouzidis A. The Role of Capsule Endoscopy in the Diagnosis and Management of Small Bowel Tumors: A Narrative Review. Cancers (Basel) 2024; 16:262. [PMID: 38254753 PMCID: PMC10813471 DOI: 10.3390/cancers16020262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/21/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Small bowel tumors (SBT) are relatively rare, but have had a steadily increasing incidence in the last few decades. Small bowel capsule endoscopy (SBCE) and device-assisted enteroscopy are the main endoscopic techniques for the study of the small bowel, the latter additionally providing sampling and therapeutic options, and hence acting complementary to SBCE in the diagnostic work-up. Although a single diagnostic modality is often insufficient in the setting of SBTs, SBCE is a fundamental tool to drive further management towards a definitive diagnosis. The aim of this paper is to provide a concise narrative review of the role of SBCE in the diagnosis and management of SBTs.
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Affiliation(s)
- Stefano Fantasia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Pablo Cortegoso Valdivia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
| | - Stefano Kayali
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - George Koulaouzidis
- Department of Biochemical Sciences, Pomeranian Medical University, 70204 Szczecin, Poland;
| | - Marco Pennazio
- University Division of Gastroenterology, City of Health and Science University Hospital, University of Turin, 10126 Turin, Italy;
| | - Anastasios Koulaouzidis
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark;
- Department of Gastroenterology, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70204 Szczecin, Poland
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20
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Hookey L. "AI for the new GI": What role does artificial intelligence have in early colonoscopy training? Gastrointest Endosc 2024; 99:100-101. [PMID: 38097302 DOI: 10.1016/j.gie.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 09/05/2023] [Indexed: 12/18/2023]
Affiliation(s)
- Lawrence Hookey
- Division of Gastroenterology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
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21
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Okamoto T, Hirasawa T. Quality indicators in endoscopic screening and the role of artificial intelligence. Dig Endosc 2024; 36:16-18. [PMID: 37872869 DOI: 10.1111/den.14701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/02/2023] [Indexed: 10/25/2023]
Affiliation(s)
- Takeshi Okamoto
- Department of Hepato-Biliary-Pancreatic Medicine, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan
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22
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Mascarenhas M, Martins M, Afonso J, Ribeiro T, Cardoso P, Mendes F, Andrade P, Cardoso H, Ferreira J, Macedo G. The Future of Minimally Invasive Capsule Panendoscopy: Robotic Precision, Wireless Imaging and AI-Driven Insights. Cancers (Basel) 2023; 15:5861. [PMID: 38136403 PMCID: PMC10742312 DOI: 10.3390/cancers15245861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.
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Affiliation(s)
- Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Patrícia Andrade
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - Helder Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanic Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, 455/461, 4200-135 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (M.M.); (J.A.); (T.R.); (P.C.); (F.M.); (P.A.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal
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23
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Mulki R, Qayed E, Yang D, Chua TY, Singh A, Yu JX, Bartel MJ, Tadros MS, Villa EC, Lightdale JR. The 2022 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board. Gastrointest Endosc 2023; 98:1009-1016. [PMID: 37977661 DOI: 10.1016/j.gie.2023.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/09/2023] [Accepted: 08/29/2023] [Indexed: 11/19/2023]
Abstract
Using a systematic literature search of original articles published during 2022 in Gastrointestinal Endoscopy and other high-impact medical and gastroenterology journals, the 10-member Editorial Board of the American Society for Gastrointestinal Endoscopy composed a list of the 10 most significant topic areas in GI endoscopy during the study year. Each Editorial Board member was directed to consider 3 criteria in generating candidate lists-significance, novelty, and global impact on clinical practice-and subject matter consensus was facilitated by the Chair through electronic voting. The 10 identified areas collectively represent advances in the following endoscopic spheres: artificial intelligence, endoscopic submucosal dissection, Barrett's esophagus, interventional EUS, endoscopic resection techniques, pancreaticobiliary endoscopy, management of acute pancreatitis, endoscopic environmental sustainability, the NordICC trial, and spiral enteroscopy. Each board member was assigned a consensus topic area around which to summarize relevant important articles, thereby generating this précis of the "top 10" endoscopic advances of 2022.
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Affiliation(s)
- Ramzi Mulki
- Division of Gastroenterology and Hepatology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Emad Qayed
- Division of Digestive Diseases, Department of Medicine, Emory University, Atlanta, Georgia, USA
| | - Dennis Yang
- Center of Interventional Endoscopy (CIE) Advent Health, Orlando, Florida, USA
| | - Tiffany Y Chua
- Division of Digestive Diseases, Harbor-University of California Los Angeles, Torrance, California, USA
| | - Ajaypal Singh
- Division of Digestive Diseases and Nutrition, Rush University Medical Center, Chicago, Illinois, USA
| | - Jessica X Yu
- Division of Gastroenterology and Hepatology, Oregon Health & Science University, Portland, Oregon, USA
| | | | | | - Edward C Villa
- NorthShore University Health System, Chicago, Illinois, USA
| | - Jenifer R Lightdale
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Boston, Massachusetts, USA
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24
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Weusten BLAM, Bisschops R, Dinis-Ribeiro M, di Pietro M, Pech O, Spaander MCW, Baldaque-Silva F, Barret M, Coron E, Fernández-Esparrach G, Fitzgerald RC, Jansen M, Jovani M, Marques-de-Sa I, Rattan A, Tan WK, Verheij EPD, Zellenrath PA, Triantafyllou K, Pouw RE. Diagnosis and management of Barrett esophagus: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy 2023; 55:1124-1146. [PMID: 37813356 DOI: 10.1055/a-2176-2440] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
MR1 : ESGE recommends the following standards for Barrett esophagus (BE) surveillance:- a minimum of 1-minute inspection time per cm of BE length during a surveillance endoscopy- photodocumentation of landmarks, the BE segment including one picture per cm of BE length, and the esophagogastric junction in retroflexed position, and any visible lesions- use of the Prague and (for visible lesions) Paris classification- collection of biopsies from all visible abnormalities (if present), followed by random four-quadrant biopsies for every 2-cm BE length.Strong recommendation, weak quality of evidence. MR2: ESGE suggests varying surveillance intervals for different BE lengths. For BE with a maximum extent of ≥ 1 cm and < 3 cm, BE surveillance should be repeated every 5 years. For BE with a maximum extent of ≥ 3 cm and < 10 cm, the interval for endoscopic surveillance should be 3 years. Patients with BE with a maximum extent of ≥ 10 cm should be referred to a BE expert center for surveillance endoscopies. For patients with an irregular Z-line/columnar-lined esophagus of < 1 cm, no routine biopsies or endoscopic surveillance are advised.Weak recommendation, low quality of evidence. MR3: ESGE suggests that, if a patient has reached 75 years of age at the time of the last surveillance endoscopy and/or the patient's life expectancy is less than 5 years, the discontinuation of further surveillance endoscopies can be considered. Weak recommendation, very low quality of evidence. MR4: ESGE recommends offering endoscopic eradication therapy using ablation to patients with BE and low grade dysplasia (LGD) on at least two separate endoscopies, both confirmed by a second experienced pathologist.Strong recommendation, high level of evidence. MR5: ESGE recommends endoscopic ablation treatment for BE with confirmed high grade dysplasia (HGD) without visible lesions, to prevent progression to invasive cancer.Strong recommendation, high level of evidence. MR6: ESGE recommends offering complete eradication of all remaining Barrett epithelium by ablation after endoscopic resection of visible abnormalities containing any degree of dysplasia or esophageal adenocarcinoma (EAC).Strong recommendation, moderate quality of evidence. MR7: ESGE recommends endoscopic resection as curative treatment for T1a Barrett's cancer with well/moderate differentiation and no signs of lymphovascular invasion.Strong recommendation, high level of evidence. MR8: ESGE suggests that low risk submucosal (T1b) EAC (i. e. submucosal invasion depth ≤ 500 µm AND no [lympho]vascular invasion AND no poor tumor differentiation) can be treated by endoscopic resection, provided that adequate follow-up with gastroscopy, endoscopic ultrasound (EUS), and computed tomography (CT)/positrion emission tomography-computed tomography (PET-CT) is performed in expert centers.Weak recommendation, low quality of evidence. MR9: ESGE suggests that submucosal (T1b) esophageal adenocarcinoma with deep submucosal invasion (tumor invasion > 500 µm into the submucosa), and/or (lympho)vascular invasion, and/or a poor tumor differentiation should be considered high risk. Complete staging and consideration of additional treatments (chemotherapy and/or radiotherapy and/or surgery) or strict endoscopic follow-up should be undertaken on an individual basis in a multidisciplinary discussion.Strong recommendation, low quality of evidence. MR10 A: ESGE recommends that the first endoscopic follow-up after successful endoscopic eradication therapy (EET) of BE is performed in an expert center.Strong recommendation, very low quality of evidence. B: ESGE recommends careful inspection of the neo-squamocolumnar junction and neo-squamous epithelium with high definition white-light endoscopy and virtual chromoendoscopy during post-EET surveillance, to detect recurrent dysplasia.Strong recommendation, very low level of evidence. C: ESGE recommends against routine four-quadrant biopsies of neo-squamous epithelium after successful EET of BE.Strong recommendation, low level of evidence. D: ESGE suggests, after successful EET, obtaining four-quadrant random biopsies just distal to a normal-appearing neo-squamocolumnar junction to detect dysplasia in the absence of visible lesions.Weak recommendation, low level of evidence. E: ESGE recommends targeted biopsies are obtained where there is a suspicion of recurrent BE in the tubular esophagus, or where there are visible lesions suspicious for dysplasia.Strong recommendation, very low level of evidence. MR11: After successful EET, ESGE recommends the following surveillance intervals:- For patients with a baseline diagnosis of HGD or EAC:at 1, 2, 3, 4, 5, 7, and 10 years after last treatment, after which surveillance may be stopped.- For patients with a baseline diagnosis of LGD:at 1, 3, and 5 years after last treatment, after which surveillance may be stopped.Strong recommendation, low quality of evidence.
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Affiliation(s)
- Bas L A M Weusten
- Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Gastroenterology and Hepatology, St. Antonius Hospital Nieuwegein, Nieuwegein, The Netherlands
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID, Leuven, Belgium
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto Portugal
| | - Massimiliano di Pietro
- Early Cancer Institute, University of Cambridge and Department of Gastroenterology, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, St. John of God Hospital, Regensburg, Germany
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Francisco Baldaque-Silva
- Advanced Endoscopy Center Carlos Moreira da Silva, Department of Gastroenterology, Pedro Hispano Hospital, Matosinhos, Portugal
- Division of Medicine, Department of Upper Gastrointestinal Diseases, Karolinska University Hospital and Karolinska Institute, Stockholm, Sweden
| | - Maximilien Barret
- Department of Gastroenterology and Digestive Oncology, Cochin Hospital and University of Paris, Paris, France
| | - Emmanuel Coron
- Institut des Maladies de l'Appareil Digestif, IMAD, Centre hospitalier universitaire Hôtel-Dieu, Nantes, Nantes, France
- Department of Gastroenterology and Hepatology, University Hospital of Geneva (HUG), Geneva, Switzerland
| | - Glòria Fernández-Esparrach
- Endoscopy Unit, Department of Gastroenterology, Hospital Clínic of Barcelona, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Biomedical Research Network on Hepatic and Digestive Diseases (CIBEREHD), Barcelona, Spain
| | - Rebecca C Fitzgerald
- Early Cancer Institute, University of Cambridge and Department of Gastroenterology, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Marnix Jansen
- Department of Histopathology, University College London Hospital NHS Trust, London, UK
| | - Manol Jovani
- Division of Gastroenterology, Maimonides Medical Center, New York, New York, USA
| | - Ines Marques-de-Sa
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto Portugal
| | - Arti Rattan
- Department of Gastroenterology, Wollongong Hospital, Wollongong, New South Wales, Australia
| | - W Keith Tan
- Early Cancer Institute, University of Cambridge and Department of Gastroenterology, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Eva P D Verheij
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers location University of Amsterdam, Amsterdam Gastroenterology, Endocrinology and Metabolism, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Pauline A Zellenrath
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, Second Department of Propaedeutic Internal Medicine, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens, Greece
| | - Roos E Pouw
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers location University of Amsterdam, Amsterdam Gastroenterology, Endocrinology and Metabolism, Cancer Center Amsterdam, Amsterdam, The Netherlands
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Young E, Edwards L, Singh R. The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization. Cancers (Basel) 2023; 15:5126. [PMID: 37958301 PMCID: PMC10647850 DOI: 10.3390/cancers15215126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.
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Affiliation(s)
- Edward Young
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
| | - Louisa Edwards
- Faculty of Health and Medical Sciences, University of Adelaide, Queen Elizabeth Hospital, Port Rd, Woodville South, SA 5011, Australia
| | - Rajvinder Singh
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
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Sung JJY, Savulescu J, Ngiam KY, An B, Ang TL, Yeoh KG, Cham TJ, Tsao S, Chua TS. Artificial intelligence for gastroenterology: Singapore artificial intelligence for Gastroenterology Working Group Position Statement. J Gastroenterol Hepatol 2023; 38:1669-1676. [PMID: 37277693 DOI: 10.1111/jgh.16241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Successful implementation of artificial intelligence in gastroenterology and hepatology practice requires more than technology. There are ethical, legal, and social issues that need to be settled. AIM A group consisting of AI developers (engineer), AI users (gastroenterologist, hepatologist, and surgeon) and AI regulators (ethicist and administrator) formed a Working Group to draft these Positions Statements with the objective of arousing public and professional interest and dialogue, to promote ethical considerations when implementing AI technology, to suggest to policy makers and health authorities relevant factors to take into account when approving and regulating the use of AI tools, and to engage the profession in preparing for change in clinical practice. STATEMENTS These series of Position Statements point out the salient issues to maintain the trust between care provider and care receivers, and to legitimize the use of a non-human tool in healthcare delivery. It is based on fundamental principles such as respect, autonomy, privacy, responsibility, and justice. Enforcing the use of AI without considering these factor risk damaging the doctor-patient relationship.
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Affiliation(s)
- Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Julian Savulescu
- Centre for Biomedical Ethics, National University of Singapore, Singapore
| | - K Y Ngiam
- Department of Surgery, National University Hospital, Singapore
| | - Bo An
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Tiing Leong Ang
- Singapore Health Service, Changi General Hospital, Singapore
| | - K G Yeoh
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore
| | - Tat-Jen Cham
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Stephen Tsao
- National Healthcare Group, Tan Tock Seng Hospital Singapore, Singapore
- Gastroenterological Society of Singapore, Singapore
| | - T S Chua
- Gastroenterology Chapter, Academy of Medicine, Singapore
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O'Hara FJ, Mc Namara D. Capsule endoscopy with artificial intelligence-assisted technology: Real-world usage of a validated AI model for capsule image review. Endosc Int Open 2023; 11:E970-E975. [PMID: 37828977 PMCID: PMC10567136 DOI: 10.1055/a-2161-1816] [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: 01/31/2023] [Accepted: 08/25/2023] [Indexed: 10/14/2023] Open
Abstract
Background and study aims Capsule endoscopy is a time-consuming procedure with a significance error rate. Artificial intelligence (AI) can potentially reduce reading time significantly by reducing the number of images that need human review. An OMOM Artificial Intelligence-enabled small bowel capsule has been recently trained and validated for small bowel capsule endoscopy video review. This study aimed to assess its performance in a real-world setting in comparison with standard reading methods. Patients and methods In this single-center retrospective study, 40 patient studies performed using the OMOM capsule were analyzed first with standard reading methods and later using AI-assisted reading. Reading time, pathology identified, intestinal landmark identification and bowel preparation assessment (Brotz Score) were compared. Results Overall diagnosis correlated 100% between the two reading methods. In a per-lesion analysis, 1293 images of significant lesions were identified combining standard and AI-assisted reading methods. AI-assisted reading captured 1268 (98.1%, 95% CI 97.15-98.7) of these findings while standard reading mode captured 1114 (86.2%, 95% confidence interval 84.2-87.9), P < 0.001. Mean reading time went from 29.7 minutes with standard reading to 2.3 minutes with AI-assisted reading ( P < 0.001), for an average time saving of 27.4 minutes per study. Time of first cecal image showed a wide discrepancy between AI and standard reading of 99.2 minutes (r = 0.085, P = 0.68). Bowel cleansing evaluation agreed in 97.4% (r = 0.805 P < 0.001). Conclusions AI-assisted reading has shown significant time savings without reducing sensitivity in this study. Limitations remain in the evaluation of other indicators.
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Affiliation(s)
- Fintan John O'Hara
- Gastroenterology, Tallaght University Hospital, Dublin, Ireland
- Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland
| | - Deirdre Mc Namara
- Gastroenterology, Tallaght University Hospital, Dublin, Ireland
- Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland
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Baumer S, Streicher K, Alqahtani SA, Brookman-Amissah D, Brunner M, Federle C, Muehlenberg K, Pfeifer L, Salzberger A, Schorr W, Zustin J, Pech O. Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center. Endosc Int Open 2023; 11:E818-E828. [PMID: 37727511 PMCID: PMC10506867 DOI: 10.1055/a-2096-2960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 09/21/2023] Open
Abstract
Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. The primary outcome was accuracy of the AI classifying the polyps into "neoplastic" and "non-neoplastic." The secondary outcome was accuracy of the classification by the endoscopists. Sessile serrated lesions were classified as neoplastic. Results We included 156 patients (mean age 65; 57 women) with 262 polyps ≤10 mm. Eighty-four were hyperplastic polyps (32.1%), 158 adenomas (60.3%), seven sessile serrated lesions (2.7%) and 13 other entities (normal/inflammatory colonmucosa, lymphoidic polyp) (4.9%) on histological diagnosis. Sensitivity, specificity and accuracy of AI were 89.70% (95% confidence interval [CI]: 84.02%-93.88%), 75.26% (95% CI: 65.46%-83.46%) and 84.35% (95% CI:79.38%-88.53%), respectively. Sensitivity, specificity and accuracy for less experienced endoscopists (2-5 years of endoscopy) were 95.56% (95% CI: 84.85%-99.46%), 61.54% (95% CI: 40.57%-79.77%) and 83.10% (95% CI: 72.34%-90.95%) and for experienced endoscopists 90.83% (95% CI: 84.19%-95.33%), 71.83% (95% CI: 59.90%-81.87%) and 83.77% (95% CI: 77.76%-88.70%), respectively. Conclusion Accuracy for polyp characterization by a new commercially available AI system is high, but does not fulfill the criteria for a "resect-and-discard" strategy.
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Affiliation(s)
- Sebastian Baumer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Kilian Streicher
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Saleh A. Alqahtani
- Department of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, United States
- Liver Transplant Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Dominic Brookman-Amissah
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Monika Brunner
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Christoph Federle
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Klaus Muehlenberg
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Lukas Pfeifer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Andrea Salzberger
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Wolfgang Schorr
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Jozef Zustin
- Private Practice, Histopathology Service Private Practice, Regensburg, Germany
- Gerhard-Domagk-Institute of Pathology, Universitätsklinikum Münster, Munster, Germany
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
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Gomes RSA, de Oliveira GHP, de Moura DTH, Kotinda APST, Matsubayashi CO, Hirsch BS, Veras MDO, Ribeiro Jordão Sasso JG, Trasolini RP, Bernardo WM, de Moura EGH. Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis. World J Gastrointest Endosc 2023; 15:528-539. [PMID: 37663113 PMCID: PMC10473903 DOI: 10.4253/wjge.v15.i8.528] [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: 03/16/2023] [Revised: 06/15/2023] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Subepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology. AIM To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer. METHODS Electronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma (GIL), the diagnostic performance of experienced endoscopists for GIST, and GIST vs GIL. Pooled sensitivity, specificity, positive, and negative predictive values were calculated. The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed. RESULTS Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01), specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and area under the curve (AUC) of 0.949. For diagnosis of GIST vs GIL by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC of 0.966. The experienced endoscopists' values were sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819. CONCLUSION AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists' and improving their diagnostic performance in the absence of invasive procedures.
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Affiliation(s)
- Rômulo Sérgio Araújo Gomes
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | | | - Diogo Turiani Hourneaux de Moura
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Ana Paula Samy Tanaka Kotinda
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Carolina Ogawa Matsubayashi
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Bruno Salomão Hirsch
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | - Matheus de Oliveira Veras
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
| | | | - Roberto Paolo Trasolini
- Division of Hepatology and Endoscopy, Department of Gastroenterology, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, United States
| | - Wanderley Marques Bernardo
- Department of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil
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30
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Metter K, Weißinger SE, Várnai-Händel A, Grund KE, Dumoulin FL. Endoscopic Treatment of T1 Colorectal Cancer. Cancers (Basel) 2023; 15:3875. [PMID: 37568691 PMCID: PMC10417475 DOI: 10.3390/cancers15153875] [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: 06/12/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Commonly accepted criteria for curative resection of T1 colorectal cancer include R0 resection with horizontal and vertical clear margins (R0), absence of lympho-vascular or vessel infiltration (L0, V0), a low to moderate histological grading (G1/2), low tumor cell budding, and limited (<1000 µm) infiltration into the submucosa. However, submucosal infiltration depth in the absence of other high-risk features has recently been questioned as a high-risk situation for lymph-node metastasis. Consequently, endoscopic resection techniques should focus on the acquisition of qualitatively and quantitively sufficient submucosal tissue. Here, we summarize the current literature on lymph-node metastasis risk after endoscopic resection of T1 colorectal cancer. Moreover, we discuss different endoscopic resection techniques with respect to the quality of the resected specimen.
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Affiliation(s)
- Klaus Metter
- Klinik für Gastroenterologie, Hepatologie und Diabetologie, Alb Fils Kliniken, Klinik am Eichert, Eichertstraße 3, D-73035 Göppingen, Germany
| | - Stephanie Ellen Weißinger
- Institut für Pathologie, Alb Fils Kliniken, Klinik am Eichert, Eichertstraße 3, D-73035 Göppingen, Germany;
| | | | - Karl-Ernst Grund
- Experimentelle Chirurgische Endoskopie (CETEX), Universitätsklinikum Tübingen, Waldhörnlestraße 22, D-72072 Tübingen, Germany;
| | - Franz Ludwig Dumoulin
- Innere Medizin/Gastroenterologie, Gemeinschaftskrankenhaus Bonn, Prinz Albert Str. 40, D-53113 Bonn, Germany;
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31
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Biamonte P, D’Amico F, Fasulo E, Barà R, Bernardi F, Allocca M, Zilli A, Danese S, Furfaro F. New Technologies in Digestive Endoscopy for Ulcerative Colitis Patients. Biomedicines 2023; 11:2139. [PMID: 37626636 PMCID: PMC10452412 DOI: 10.3390/biomedicines11082139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Ulcerative colitis (UC) is a chronic inflammatory bowel disease primarily affecting the colon and rectum. Endoscopy plays a crucial role in the diagnosis and management of UC. Recent advancements in endoscopic technology, including chromoendoscopy, confocal laser endomicroscopy, endocytoscopy and the use of artificial intelligence, have revolutionized the assessment and treatment of UC patients. These innovative techniques enable early detection of dysplasia and cancer, more precise characterization of disease extent and severity and more targeted biopsies, leading to improved diagnosis and disease monitoring. Furthermore, these advancements have significant implications for therapeutic decision making, empowering clinicians to carefully consider a range of treatment options, including pharmacological therapies, endoscopic interventions and surgical approaches. In this review, we provide an overview of the latest endoscopic technologies and their applications for diagnosing and monitoring UC. We also discuss their impact on treatment decision making, highlighting the potential benefits and limitations of each technique.
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Affiliation(s)
- Paolo Biamonte
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Ferdinando D’Amico
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy
| | - Ernesto Fasulo
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Rukaia Barà
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Francesca Bernardi
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Mariangela Allocca
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Alessandra Zilli
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
| | - Silvio Danese
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
- Gastroenterology and Endoscopy, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Federica Furfaro
- Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, 20132 Milan, Italy; (P.B.); (E.F.); (R.B.); (F.B.); (M.A.); (A.Z.); (S.D.); (F.F.)
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Tumino E, Visaggi P, Bolognesi V, Ceccarelli L, Lambiase C, Coda S, Premchand P, Bellini M, de Bortoli N, Marciano E. Robotic Colonoscopy and Beyond: Insights into Modern Lower Gastrointestinal Endoscopy. Diagnostics (Basel) 2023; 13:2452. [PMID: 37510196 PMCID: PMC10378494 DOI: 10.3390/diagnostics13142452] [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: 06/19/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Lower gastrointestinal endoscopy is considered the gold standard for the diagnosis and removal of colonic polyps. Delays in colonoscopy following a positive fecal immunochemical test increase the likelihood of advanced adenomas and colorectal cancer (CRC) occurrence. However, patients may refuse to undergo conventional colonoscopy (CC) due to fear of possible risks and pain or discomfort. In this regard, patients undergoing CC frequently require sedation to better tolerate the procedure, increasing the risk of deep sedation or other complications related to sedation. Accordingly, the use of CC as a first-line screening strategy for CRC is hampered by patients' reluctance due to its invasiveness and anxiety about possible discomfort. To overcome the limitations of CC and improve patients' compliance, several studies have investigated the use of robotic colonoscopy (RC) both in experimental models and in vivo. Self-propelling robotic colonoscopes have proven to be promising thanks to their peculiar dexterity and adaptability to the shape of the lower gastrointestinal tract, allowing a virtually painless examination of the colon. In some instances, when alternatives to CC and RC are required, barium enema (BE), computed tomographic colonography (CTC), and colon capsule endoscopy (CCE) may be options. However, BE and CTC are limited by the need for subsequent investigations whenever suspicious lesions are found. In this narrative review, we discussed the current clinical applications of RC, CTC, and CCE, as well as the advantages and disadvantages of different endoscopic procedures, with a particular focus on RC.
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Affiliation(s)
- Emanuele Tumino
- Endoscopy Unit, Azienda Ospedaliero Universitaria Pisana, 56125 Pisa, Italy
| | - Pierfrancesco Visaggi
- Endoscopy Unit, Azienda Ospedaliero Universitaria Pisana, 56125 Pisa, Italy
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56100 Pisa, Italy
| | - Valeria Bolognesi
- Endoscopy Unit, Azienda Ospedaliero Universitaria Pisana, 56125 Pisa, Italy
| | - Linda Ceccarelli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56100 Pisa, Italy
| | - Christian Lambiase
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56100 Pisa, Italy
| | - Sergio Coda
- Digestive Disease Centre, Division of Surgery, Barking, Havering and Redbridge University Hospitals NHS Trust, Romford RM70AG, UK
| | - Purushothaman Premchand
- Digestive Disease Centre, Division of Surgery, Barking, Havering and Redbridge University Hospitals NHS Trust, Romford RM70AG, UK
| | - Massimo Bellini
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56100 Pisa, Italy
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56100 Pisa, Italy
| | - Emanuele Marciano
- Endoscopy Unit, Azienda Ospedaliero Universitaria Pisana, 56125 Pisa, Italy
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Lee J, Lee H, Chung JW. The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review. J Gastric Cancer 2023; 23:375-387. [PMID: 37553126 PMCID: PMC10412973 DOI: 10.5230/jgc.2023.23.e31] [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: 07/07/2023] [Revised: 07/31/2023] [Accepted: 07/31/2023] [Indexed: 08/10/2023] Open
Abstract
Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.
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Affiliation(s)
- JunHo Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea
- Corp. CAIMI, Incheon, Korea
| | - Hanna Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea
- Corp. CAIMI, Incheon, Korea.
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Spadaccini M, Schilirò A, Sharma P, Repici A, Hassan C, Voza A. Adenoma detection rate in colonoscopy: how can it be improved? Expert Rev Gastroenterol Hepatol 2023; 17:1089-1099. [PMID: 37869781 DOI: 10.1080/17474124.2023.2273990] [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: 02/21/2023] [Accepted: 10/18/2023] [Indexed: 10/24/2023]
Abstract
INTRODUCTION The introduction of widespread colonoscopy screening programs has helped in decreasing the incidence of Colorectal Cancer (CRC). However, 'back-to-back' colonoscopies revealed relevant percentage of missed adenomas. Quality indicators were created to further homogenize detection performances and decrease the incidence of post-colonoscopy CRC. Among them, the Adenoma Detection Rate (ADR), defined as the percentage obtained by dividing the number of endoscopic procedures in which at least one adenoma was resected, by the total number of procedures, was found to be inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer. AREAS COVERED In this paper, we performed a comprehensive review of the literature focusing on promising new devices and technologies, which are meant to positively affect the endoscopist performance in detecting adenomas, therefore increasing ADR. EXPERT OPINION Considering the current knowledge, although several devices and technologies have been proposed with this intent, the recent implementation of AI ranked over all of the other strategies and it is likely to become the new standard within few years. However, the combination of different device/technologies need to be investigated in the future aiming at even further increasing of endoscopist detection performances.
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Schilirò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Antonio Voza
- Humanitas Clinical and Research Center -IRCCS-, Emergency Department, Rozzano, Italy
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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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Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers (Basel) 2023; 15:cancers15082193. [PMID: 37190122 DOI: 10.3390/cancers15082193] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Anjara Hernández-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
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Lee TC, Angelina CL, Kongkam P, Wang HP, Rerknimitr R, Han ML, Chang HT. Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy. Diagnostics (Basel) 2023; 13:diagnostics13071289. [PMID: 37046507 PMCID: PMC10093377 DOI: 10.3390/diagnostics13071289] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/05/2023] [Accepted: 03/16/2023] [Indexed: 04/14/2023] Open
Abstract
Accurate classification of pancreatic cystic lesions (PCLs) is important to facilitate proper treatment and to improve patient outcomes. We utilized the convolutional neural network (CNN) of VGG19 to develop a computer-aided diagnosis (CAD) system in the classification of subtypes of PCLs in endoscopic ultrasound-guided needle-based confocal laser endomicroscopy (nCLE). From a retrospectively collected 22,424 nCLE video frames (50 videos) as the training/validation set and 11,047 nCLE video frames (18 videos) as the test set, we developed and compared the diagnostic performance of three CNNs with distinct methods of designating the region of interest. The diagnostic accuracy for subtypes of PCLs by CNNs with manual, maximal rectangular, and U-Net algorithm-designated ROIs was 100%, 38.9%, and 66.7% on a per-video basis and 88.99%, 73.94%, and 76.12% on a per-frame basis, respectively. Our per-frame analysis suggested differential levels of diagnostic accuracy among the five subtypes of PCLs, where non-mucinous PCLs (serous cystic neoplasm: 93.11%, cystic neuroendocrine tumor: 84.31%, and pseudocyst: 98%) had higher diagnostic accuracy than mucinous PCLs (intraductal papillary mucinous neoplasm: 84.43% and mucinous cystic neoplasm: 86.1%). Our CNN demonstrated superior specificity compared to the state-of-the-art for the classification of mucinous PCLs (IPMN and MCN), with high specificity (94.3% and 92.8%, respectively) but low sensitivity (46% and 45.2%, respectively). This suggests the complimentary role of CNN-enabled CAD systems, especially for clinically suspected mucinous PCLs.
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Affiliation(s)
- Tsung-Chun Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Department of Internal Medicine, School of Medicine, College of Medicine, TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei 11031, Taiwan
| | - Clara Lavita Angelina
- Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
| | - Pradermchai Kongkam
- Excellent Center for Gastrointestinal Endoscopy and Division of Gastroenterology, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok 10330, Thailand
- Pancreas Research Unit, Division of Hospital and Ambulatory Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Hsiu-Po Wang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, College of Medicine, National Taiwan University Hospital, National Taiwan University, Taipei 10002, Taiwan
| | - Rungsun Rerknimitr
- Excellent Center for Gastrointestinal Endoscopy and Division of Gastroenterology, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok 10330, Thailand
| | - Ming-Lun Han
- Department of Integrated Diagnostics and Therapeutics, National Taiwan University Hospital, Taipei 10002, Taiwan
| | - Hsuan-Ting Chang
- Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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Kawamura M, Koike T, Ogata Y, Matsumoto R, Yano K, Hiratsuka T, Ohyama H, Sato I, Kayada K, Suzuki S, Hiratsuka S, Watanabe Y. Endoscopic Grading of Gastric Intestinal Metaplasia Using Magnifying and Nonmagnifying Narrow-Band Imaging Endoscopy. Diagnostics (Basel) 2022; 12:diagnostics12123012. [PMID: 36553019 PMCID: PMC9776966 DOI: 10.3390/diagnostics12123012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022] Open
Abstract
Several endoscopic findings obtained by magnifying image-enhanced endoscopy (IEE) are reportedly correlated with gastric intestinal metaplasia (IM); however, the differences between magnifying and nonmagnifying IEE for the diagnosis of gastric IM remain unknown. This study included 100 consecutive patients who underwent narrow-band imaging endoscopy. Four areas of the stomach were evaluated using nonmagnifying and magnifying IEE. Light-blue crest (LBC), white opaque substance (WOS), and endoscopic grading of the gastric IM (EGGIM) were assessed. The concordance rates between nonmagnifying and magnifying IEE were 80.5% for LBC and 93.3% for WOS. The strength of agreement between each observation technique showed good reproducibility, with a kappa value of 0.69 and 0.83 for LBC and WOS, respectively. The individual EGGIM score indicated a good correlation between nonmagnifying and magnifying IEE (concordance rate, 75%; kappa value, 0.67). The prevalence of a high EGGIM score in patients with and without gastric cancer (GC) showed a significant difference both with nonmagnifying IEE (odds ratio (OR), 3.3; 95% confidence interval (CI), 1.2-9.0), and magnifying IEE (OR, 3.1; 95% CI, 1.1-8.9). Nonmagnifying IEE has the potential to stratify the individual risk of GC, similar to magnifying IEE, warranting further investigation with histological assessment.
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Affiliation(s)
- Masashi Kawamura
- Department of Gastroenterology, Sendai City Hospital, 1-1-1, Asutonagamachi, Taihaku-ku, Sendai 982-8502, Miyagi, Japan
- Correspondence:
| | - Tomoyuki Koike
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Yohei Ogata
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Ryotaro Matsumoto
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Kota Yano
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Takashi Hiratsuka
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Hideaki Ohyama
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Isao Sato
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Kimiko Kayada
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Suguo Suzuki
- Division of Gastroenterology, Tohoku University Graduate School of Medicine, 1-1, Seiryo-machi, Sendai 980-8574, Miyagi, Japan
| | - Satsuki Hiratsuka
- Department of Gastroenterology, Sendai City Hospital, 1-1-1, Asutonagamachi, Taihaku-ku, Sendai 982-8502, Miyagi, Japan
| | - Yumiko Watanabe
- Department of Gastroenterology, Sendai City Hospital, 1-1-1, Asutonagamachi, Taihaku-ku, Sendai 982-8502, Miyagi, Japan
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