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Introzzi L, Zonca J, Cabitza F, Cherubini P, Reverberi C. Enhancing human-AI collaboration: The case of colonoscopy. Dig Liver Dis 2024; 56:1131-1139. [PMID: 37940501 DOI: 10.1016/j.dld.2023.10.018] [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: 08/03/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023]
Abstract
Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.
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Affiliation(s)
- Luca Introzzi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy
| | - Joshua Zonca
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy
| | - Federico Cabitza
- Department of Informatics, Systems and Communication, Università Milano - Bicocca, Milano, Italy; IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | - Paolo Cherubini
- Department of Brain and Behavioral Sciences, Università Statale di Pavia, Pavia, Italy
| | - Carlo Reverberi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy.
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Xu Z, Miao Y, Chen G, Liu S, Chen H. GLGFormer: Global Local Guidance Network for Mucosal Lesion Segmentation in Gastrointestinal Endoscopy Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01162-2. [PMID: 38940891 DOI: 10.1007/s10278-024-01162-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 05/05/2024] [Accepted: 06/03/2024] [Indexed: 06/29/2024]
Abstract
Automatic mucosal lesion segmentation is a critical component in computer-aided clinical support systems for endoscopic image analysis. Image segmentation networks currently rely mainly on convolutional neural networks (CNNs) and Transformers, which have demonstrated strong performance in various applications. However, they cannot cope with blurred lesion boundaries and lesions of different scales in gastrointestinal endoscopy images. To address these challenges, we propose a new Transformer-based network, named GLGFormer, for the task of mucosal lesion segmentation. Specifically, we design the global guidance module to guide single-scale features patch-wise, enabling them to incorporate global information from the global map without information loss. Furthermore, a partial decoder is employed to fuse these enhanced single-scale features, achieving single-scale to multi-scale enhancement. Additionally, the local guidance module is designed to refocus attention on the neighboring patch, thus enhancing local features and refining lesion boundary segmentation. We conduct experiments on a private atrophic gastritis segmentation dataset and four public gastrointestinal polyp segmentation datasets. Compared to the current lesion segmentation networks, our proposed GLGFormer demonstrates outstanding learning and generalization capabilities. On the public dataset ClinicDB, GLGFormer achieved a mean intersection over union (mIoU) of 91.0% and a mean dice coefficient (mDice) of 95.0%. On the private dataset Gastritis-Seg, GLGFormer achieved an mIoU of 90.6% and an mDice of 94.6%.
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Affiliation(s)
- Zhiyang Xu
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, School of Information and Control Engineering, Advanced Robotics Research Center, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, P. R. China
| | - Yanzi Miao
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, School of Information and Control Engineering, Advanced Robotics Research Center, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, P. R. China.
| | - Guangxia Chen
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, 221002, P. R. China
| | - Shiyu Liu
- Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, 221002, P. R. China
| | - Hu Chen
- The First Clinical Medical School of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, P. R. China
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Toyoshima O, Nishizawa T, Hiramatsu T, Matsuno T, Yoshida S, Mizutani H, Ebinuma H, Matsuda T, Saito Y, Fujishiro M. Colorectal adenoma detection rate using texture and color enhancement imaging versus white light imaging with chromoendoscopy: a propensity score matching study. J Gastroenterol Hepatol 2024. [PMID: 38872367 DOI: 10.1111/jgh.16655] [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: 02/08/2024] [Revised: 05/07/2024] [Accepted: 05/29/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND AND AIM Few studies have evaluated the adenoma detection rate (ADR) of colonoscopy with texture and color enhancement imaging (TXI), a novel image-enhancing technology. This study compares the detection of colorectal polyps using TXI to that using white light imaging (WLI). METHODS This single-center retrospective study used propensity-matched scoring based on the patients' baseline characteristics (age, sex, indication, bowel preparation, endoscopist, colonoscope type, and withdrawal time) to compare the results of patients who underwent chromoendoscopy using WLI or TXI at the Toyoshima Endoscopy Clinic. The differences in polyp detection rates and the mean number of detected polyps per colonoscopy were determined between the TXI and WLI groups. RESULTS After propensity score matching, 1970 patients were enrolled into each imaging modality group. The mean patient age was 57.2 ± 12.5 years, and 44.5% of the cohort were men. The ADR was higher in the TXI group than in the WLI group (55.0% vs 49.4%, odds ratio: 1.25). High-risk ADR were more common in the TXI group than in the WLI group (17.6% vs 12.8%; OR: 1.45). The mean number of adenomas per colonoscopy (APC) was higher in the TXI group than in the WLI group (1.187 vs 0.943, OR: 1.12). APC with a flat morphology (1.093 vs 0.848, OR: 1.14) and APC of <6 mm (0.992 vs 0.757, OR: 1.16) were higher in the TXI group than in the WLI group. CONCLUSION Compared to WLI, TXI improved the ADR in patients who underwent chromoendoscopy based on actual clinical data.
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Affiliation(s)
- Osamu Toyoshima
- Department of Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo, Japan
| | - Toshihiro Nishizawa
- Department of Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo, Japan
- Department of Gastroenterology and Hepatology, International University of Health and Welfare, Narita Hospital, Narita, Japan
| | - Takuma Hiramatsu
- Department of Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tatsuya Matsuno
- Department of Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo, Japan
| | - Shuntaro Yoshida
- Department of Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo, Japan
- Department of Internal Medicine, Yoshida Clinic, Fukaya, Japan
| | - Hiroya Mizutani
- Department of Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hirotoshi Ebinuma
- Department of Gastroenterology and Hepatology, International University of Health and Welfare, Narita Hospital, Narita, Japan
| | | | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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López-Serrano A, Voces A, Lorente JR, Santonja FJ, Algarra A, Latorre P, Del Pozo P, Paredes JM. Artificial intelligence for dysplasia detection during surveillance colonoscopy in patients with ulcerative colitis: A cross-sectional, non-inferiority, diagnostic test comparison study. GASTROENTEROLOGIA Y HEPATOLOGIA 2024:S0210-5705(24)00168-7. [PMID: 38740327 DOI: 10.1016/j.gastrohep.2024.502210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND AND STUDY AIM High-definition virtual chromoendoscopy, along with targeted biopsies, is recommended for dysplasia surveillance in ulcerative colitis patients at risk for colorectal cancer. Computer-aided detection (CADe) systems aim to improve colonic adenoma detection, however their efficacy in detecting polyps and adenomas in this context remains unclear. This study evaluates the CADe Discovery™ system's effectiveness in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer. PATIENTS AND METHODS A prospective cross-sectional, non-inferiority, diagnostic test comparison study was conducted on ulcerative colitis patients undergoing colorectal cancer surveillance colonoscopy between January 2021 and April 2021. Patients underwent virtual chromoendoscopy (VCE) with iSCAN 1 and 3 with optical enhancement. One endoscopist, blinded to CADe Discovery™ system results, examined colon sections, while a second endoscopist concurrently reviewed CADe images. Suspicious areas detected by both techniques underwent resection. Proportions of dysplastic lesions and patients with dysplasia detected by VCE or CADe were calculated. RESULTS Fifty-two patients were included, and 48 lesions analyzed. VCE and CADe each detected 9 cases of dysplasia (21.4% and 20.0%, respectively; p=0.629) in 8 patients and 7 patients (15.4% vs. 13.5%, respectively; p=0.713). Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy for dysplasia detection using VCE or CADe were 90% and 90%, 13% and 5%, 21% and 2%, 83% and 67%, and 29.2% and 22.9%, respectively. CONCLUSIONS The CADe Discovery™ system shows similar diagnostic performance to VCE with iSCAN in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer.
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Affiliation(s)
- Antonio López-Serrano
- Gastroenterology Department, Hospital Universitari Dr. Peset, Valencia, Spain; Department of Medicine, Universitat de Valencia, Valencia, Spain.
| | - Alba Voces
- Gastroenterology Department, Hospital Universitari Dr. Peset, Valencia, Spain
| | - José Ramón Lorente
- Gastroenterology Department, Hospital Universitari Dr. Peset, Valencia, Spain
| | | | - Angela Algarra
- Gastroenterology Department, Hospital Universitari Dr. Peset, Valencia, Spain
| | - Patricia Latorre
- Gastroenterology Department, Hospital Universitari Dr. Peset, Valencia, Spain
| | - Pablo Del Pozo
- Gastroenterology Department, Hospital Universitari Dr. Peset, Valencia, Spain
| | - José María Paredes
- Gastroenterology Department, Hospital Universitari Dr. Peset, Valencia, Spain
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Robertson DJ, Rex DK, Ciani O, Drummond MF. Colonoscopy vs the Fecal Immunochemical Test: Which is Best? Gastroenterology 2024; 166:758-771. [PMID: 38342196 DOI: 10.1053/j.gastro.2023.12.027] [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: 06/21/2023] [Revised: 12/18/2023] [Accepted: 12/29/2023] [Indexed: 02/13/2024]
Abstract
Although there is no debate around the effectiveness of colorectal cancer screening in reducing disease burden, there remains a question regarding the most effective and cost-effective screening modality. Current United States guidelines present a panel of options that include the 2 most commonly used modalities, colonoscopy and stool testing with the fecal immunochemical test (FIT). Large-scale comparative effectiveness trials comparing colonoscopy and FIT for colorectal cancer outcomes are underway, but results are not yet available. This review will separately state the "best case" for FIT and colonoscopy as the screening tool of first choice. In addition, the review will examine these modalities from a health economics perspective to provide the reader further context about the relative advantages of these commonly used tests.
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Affiliation(s)
- Douglas J Robertson
- VA Medical Center, White River Junction, Vermont; Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
| | - Douglas K Rex
- Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Oriana Ciani
- Centre for Research on Health and Social Care Management, SDA Bocconi School of Management, Milan, Italy
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Lee MCM, Parker CH, Liu LWC, Farahvash A, Jeyalingam T. Impact of study design on adenoma detection in the evaluation of artificial intelligence-aided colonoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2024; 99:676-687.e16. [PMID: 38272274 DOI: 10.1016/j.gie.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/19/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND AIMS Randomized controlled trials (RCTs) have reported that artificial intelligence (AI) improves endoscopic polyp detection. Different methodologies-namely, parallel and tandem designs-have been used to evaluate the efficacy of AI-assisted colonoscopy in RCTs. Systematic reviews and meta-analyses have reported a pooled effect that includes both study designs. However, it is unclear whether there are inconsistencies in the reported results of these 2 designs. Here, we aimed to determine whether study characteristics moderate between-trial differences in outcomes when evaluating the effectiveness of AI-assisted polyp detection. METHODS A systematic search of Ovid MEDLINE, Embase, Cochrane Central, Web of Science, and IEEE Xplore was performed through March 1, 2023, for RCTs comparing AI-assisted colonoscopy with routine high-definition colonoscopy in polyp detection. The primary outcome of interest was the impact of study type on the adenoma detection rate (ADR). Secondary outcomes included the impact of the study type on adenomas per colonoscopy and withdrawal time, as well as the impact of geographic location, AI system, and endoscopist experience on ADR. Pooled event analysis was performed using a random-effects model. RESULTS Twenty-four RCTs involving 17,413 colonoscopies (AI assisted: 8680; non-AI assisted: 8733) were included. AI-assisted colonoscopy improved overall ADR (risk ratio [RR], 1.24; 95% confidence interval [CI], 1.17-1.31; I2 = 53%; P < .001). Tandem studies collectively demonstrated improved ADR in AI-aided colonoscopies (RR, 1.18; 95% CI, 1.08-1.30; I2 = 0%; P < .001), as did parallel studies (RR, 1.26; 95% CI, 1.17-1.35; I2 = 62%; P < .001), with no statistical subgroup difference between study design. Both tandem and parallel study designs revealed improvement in adenomas per colonoscopy in AI-aided colonoscopies, but this improvement was more marked among tandem studies (P < .001). AI assistance significantly increased withdrawal times for parallel (P = .002), but not tandem, studies. ADR improvement was more marked among studies conducted in Asia compared to Europe and North America in a subgroup analysis (P = .007). Type of AI system used or endoscopist experience did not affect overall improvement in ADR. CONCLUSIONS Either parallel or tandem study design can capture the improvement in ADR resulting from the use of AI-assisted polyp detection systems. Tandem studies powered to detect differences in endoscopic performance through paired comparison may be a resource-efficient method of evaluating new AI-assisted technologies.
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Affiliation(s)
- Michelle C M Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Colleen H Parker
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Louis W C Liu
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Armin Farahvash
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Thurarshen Jeyalingam
- Division of Gastroenterology and Hepatology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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Tiankanon K, Aniwan S, Kerr SJ, Mekritthikrai K, Kongtab N, Wisedopas N, Piyachaturawat P, Kulpatcharapong S, Linlawan S, Phromnil P, Muangpaisarn P, Orprayoon T, Chanyaswad J, Sunthornwechapong P, Vateekul P, Kullavanijaya P, Rerknimitr R. Improvement of adenoma detection rate by two computer-aided colonic polyp detection systems in high adenoma detectors: a randomized multicenter trial. Endoscopy 2024; 56:273-282. [PMID: 37963587 DOI: 10.1055/a-2210-7999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
BACKGROUND This study aimed to evaluate the benefits of a self-developed computer-aided polyp detection system (SD-CADe) and a commercial system (CM-CADe) for high adenoma detectors compared with white-light endoscopy (WLE) as a control. METHODS Average-risk 50-75-year-old individuals who underwent screening colonoscopy at five referral centers were randomized to SD-CADe, CM-CADe, or WLE groups (1:1:1 ratio). Trainees and staff with an adenoma detection rate (ADR) of ≥35% were recruited. The primary outcome was ADR. Secondary outcomes were the proximal adenoma detection rate (pADR), advanced adenoma detection rate (AADR), and the number of adenomas, proximal adenomas, and advanced adenomas per colonoscopy (APC, pAPC, and AAPC, respectively). RESULTS The study enrolled 1200 participants. The ADR in the control, CM-CADe, and SD-CADe groups was 38.3%, 50.0%, and 54.8%, respectively. The pADR was 23.0%, 32.3%, and 38.8%, respectively. AADR was 6.0%, 10.3%, and 9.5%, respectively. After adjustment, the ADR and pADR in both intervention groups were significantly higher than in controls (all P<0.05). The APC in the control, CM-CADe, and SD-CADe groups was 0.66, 1.04, and 1.16, respectively. The pAPC was 0.33, 0.53, and 0.64, respectively, and the AAPC was 0.07, 0.12, and 0.10, respectively. Both CADe systems showed significantly higher APC and pAPC than WLE. AADR and AAPC were improved in both CADe groups versus control, although the differences were not statistically significant. CONCLUSION Even in high adenoma detectors, CADe significantly improved ADR and APC. The AADR tended to be higher with both systems, and this may enhance colorectal cancer prevention.
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Affiliation(s)
- Kasenee Tiankanon
- Division of Gastroenterology, Chulalongkorn University, Bangkok, Thailand
- Gastrointestinal Endoscopy Excellence Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Satimai Aniwan
- Division of Gastroenterology, Chulalongkorn University, Bangkok, Thailand
- Gastrointestinal Endoscopy Excellence Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Stephen J Kerr
- Biostatistics Excellence Center, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- The Kirby Institute, University of New South Wales, Sydney, Australia
| | - Krittaya Mekritthikrai
- Division of Gastroenterology, Chulalongkorn University, Bangkok, Thailand
- Gastrointestinal Endoscopy Excellence Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Natanong Kongtab
- Division of Gastroenterology, Chulalongkorn University, Bangkok, Thailand
- Gastrointestinal Endoscopy Excellence Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Naruemon Wisedopas
- Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | | | | | - Poonrada Phromnil
- Department of Medicine, Khlong Khlung Hospital, Kamphaeng Phet, Thailand
| | - Puth Muangpaisarn
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Prapokklao Hospital, Chanthaburi, Thailand
| | - Theerapat Orprayoon
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Prapokklao Hospital, Chanthaburi, Thailand
| | - Jaruwan Chanyaswad
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Prapokklao Hospital, Chanthaburi, Thailand
| | | | - Peerapon Vateekul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Pinit Kullavanijaya
- Division of Gastroenterology, Chulalongkorn University, Bangkok, Thailand
- Gastrointestinal Endoscopy Excellence Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Rungsun Rerknimitr
- Division of Gastroenterology, Chulalongkorn University, Bangkok, Thailand
- Gastrointestinal Endoscopy Excellence Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
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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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Lou S, Du F, Song W, Xia Y, Yue X, Yang D, Cui B, Liu Y, Han P. Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials. EClinicalMedicine 2023; 66:102341. [PMID: 38078195 PMCID: PMC10698672 DOI: 10.1016/j.eclinm.2023.102341] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 05/11/2024] Open
Abstract
BACKGROUND The use of artificial intelligence (AI) in detecting colorectal neoplasia during colonoscopy holds the potential to enhance adenoma detection rates (ADRs) and reduce adenoma miss rates (AMRs). However, varied outcomes have been observed across studies. Thus, this study aimed to evaluate the potential advantages and disadvantages of employing AI-aided systems during colonoscopy. METHODS Using Medical Subject Headings (MeSH) terms and keywords, a comprehensive electronic literature search was performed of the Embase, Medline, and the Cochrane Library databases from the inception of each database until October 04, 2023, in order to identify randomized controlled trials (RCTs) comparing AI-assisted with standard colonoscopy for detecting colorectal neoplasia. Primary outcomes included AMR, ADR, and adenomas detected per colonoscopy (APC). Secondary outcomes comprised the poly missed detection rate (PMR), poly detection rate (PDR), and poly detected per colonoscopy (PPC). We utilized random-effects meta-analyses with Hartung-Knapp adjustment to consolidate results. The prediction interval (PI) and I2 statistics were utilized to quantify between-study heterogeneity. Moreover, meta-regression and subgroup analyses were performed to investigate the potential sources of heterogeneity. This systematic review and meta-analysis is registered with PROSPERO (CRD42023428658). FINDINGS This study encompassed 33 trials involving 27,404 patients. Those undergoing AI-aided colonoscopy experienced a significant decrease in PMR (RR, 0.475; 95% CI, 0.294-0.768; I2 = 87.49%) and AMR (RR, 0.495; 95% CI, 0.390-0.627; I2 = 48.76%). Additionally, a significant increase in PDR (RR, 1.238; 95% CI, 1.158-1.323; I2 = 81.67%) and ADR (RR, 1.242; 95% CI, 1.159-1.332; I2 = 78.87%), along with a significant increase in the rates of PPC (IRR, 1.388; 95% CI, 1.270-1.517; I2 = 91.99%) and APC (IRR, 1.390; 95% CI, 1.277-1.513; I2 = 86.24%), was observed. This resulted in 0.271 more PPCs (95% CI, 0.144-0.259; I2 = 65.61%) and 0.202 more APCs (95% CI, 0.144-0.259; I2 = 68.15%). INTERPRETATION AI-aided colonoscopy significantly enhanced the detection of colorectal neoplasia detection, likely by reducing the miss rate. However, future studies should focus on evaluating the cost-effectiveness and long-term benefits of AI-aided colonoscopy in reducing cancer incidence. FUNDING This work was supported by the Heilongjiang Provincial Natural Science Foundation of China (LH2023H096), the Postdoctoral research project in Heilongjiang Province (LBH-Z22210), the National Natural Science Foundation of China's General Program (82072640) and the Outstanding Youth Project of Heilongjiang Natural Science Foundation (YQ2021H023).
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Affiliation(s)
- Shenghan Lou
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Fenqi Du
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Wenjie Song
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Yixiu Xia
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Xinyu Yue
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Da Yang
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Binbin Cui
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Yanlong Liu
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Peng Han
- Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
- Key Laboratory of Tumor Immunology in Heilongjiang, No.150 Haping Road, Harbin, Heilongjiang, 150081, China
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10
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Kiesslich R. Next generation of virtual chromoendoscopy improves adenoma detection during colonoscopy. Endoscopy 2023; 55:1081-1082. [PMID: 37931783 DOI: 10.1055/a-2189-4890] [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] [Indexed: 11/08/2023]
Affiliation(s)
- Ralf Kiesslich
- Innere Medizin II, HELIOS Dr Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany
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11
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Berzin TM, Glissen Brown J. Navigating the "Trough of Disillusionment" for CADe Polyp Detection: What Can We Learn About Negative AI Trials and the Physician-AI Hybrid? Am J Gastroenterol 2023; 118:1743-1745. [PMID: 37141122 DOI: 10.14309/ajg.0000000000002286] [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: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 05/05/2023]
Affiliation(s)
- Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Jeremy Glissen Brown
- Division of Gastroenterology, Duke University Medical Center, Durham, North Carolina, USA
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12
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Jiao YF, Cheng ZY, Gao Y, Yu CT, Jiang HS, Wang TL, Deng Y, Lin H, Wang TJ, Wang W, Wan R, Li ZS, Xin L, Wang LW. Development, quality, and influencing factors of colonoscopy in China: results from the national census in 2013 and 2020. Front Oncol 2023; 13:1276520. [PMID: 37810978 PMCID: PMC10556871 DOI: 10.3389/fonc.2023.1276520] [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: 08/12/2023] [Accepted: 09/07/2023] [Indexed: 10/10/2023] Open
Abstract
Background and Aim With the increasing burden of colorectal cancer (CRC), the practice of colonoscopy is gaining attention worldwide. However, it exhibits distinct trends between developing and developed countries. This study aims to explore its development and identify influencing factors in China. Methods The Chinese Digestive Endoscopy Censuses were conducted twice in mainland China under the supervision of health authorities. Information regarding the practice of colonoscopy was collected through a structured online questionnaire. The authenticity of the data was evaluated through logical tests, and a random selection of endoscopic reports underwent manual validation by Quality Control Centers. Potential factors associated with colonoscopy were analyzed using real-world information. Results From 2012 to 2019, the number of hospitals that performed colonoscopy increased from 3,210 to 6,325 (1.97-fold), and the volume increased from 5.83 to 12.92 million (2.21-fold). The utilization rate rose from 436.0 to 914.8 per 100,000 inhabitants (2.10-fold). However, there was an exacerbation of regional inequality in the adequacy of colonoscopy. Regions with higher incidence of CRC, higher gross domestic product per capita, more average numbers of endoscopists and tertiary hospitals tended to provide more accessible colonoscopy (P<0.001). Nationwide, the cecal intubation rate improved from 83.9% to 94.4% and the unadjusted adenoma detection rate (ADR) improved from 16.3% to 18.1%. Overall, hospital grading, educational background of endoscopists, economic income, and colonoscopy volume were observed as the significantly positive factors affecting ADR (P<0.05), but not the incidence of CRC or the number of endoscopists. Conclusions Tremendous progress in colonoscopy has been made in China, but some issues needed timely reflection. Our findings provide timely evidence for better colonoscopy strategies and measures, such as quality control and medical education of endoscopists.
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Affiliation(s)
- Yun-Fei Jiao
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Zhi-Yuan Cheng
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ye Gao
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Chu-Ting Yu
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Hui-Shan Jiang
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Ting-Lu Wang
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Ying Deng
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Han Lin
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Tian-Jiao Wang
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Wei Wang
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Rong Wan
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhao-Shen Li
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Lei Xin
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
| | - Luo-Wei Wang
- Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
- National Digestive Endoscopy Improvement System, Shanghai, China
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13
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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14
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Wang LM, Ang TL. Optimizing endoscopic ultrasound guided fine needle aspiration through artificial intelligence. J Gastroenterol Hepatol 2023; 38:839-840. [PMID: 37264500 DOI: 10.1111/jgh.16242] [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] [Indexed: 06/03/2023]
Affiliation(s)
- Lai Mun Wang
- Department of Anatomical Pathology, Changi General Hospital, SingHealth, Duke-NUS Medical School, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, SingHealth; Duke-NUS Medical School; Yong Loo Lin School of Medicine, National University of Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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15
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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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16
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Zimmermann-Fraedrich K, Rösch T. Artificial intelligence and the push for small adenomas: all we need? Endoscopy 2023; 55:320-323. [PMID: 36882088 DOI: 10.1055/a-2038-7078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Affiliation(s)
| | - Thomas Rösch
- Department of Interdisciplinary Endoscopy University Hospital Hamburg-Eppendorf, Hamburg, Germany
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17
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Ang TL, Jao-Yiu Sung J. Harnessing the potential of artificial intelligence-assisted colonoscopy to reduce the risk of post-colonoscopy interval colorectal cancer. J Gastroenterol Hepatol 2023; 38:157-158. [PMID: 36751949 DOI: 10.1111/jgh.16135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Affiliation(s)
- Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore, Singapore.,SingHealth, Duke-NUS Medical School, Singapore, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Joseph Jao-Yiu Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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18
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Big Data in Gastroenterology Research. Int J Mol Sci 2023; 24:ijms24032458. [PMID: 36768780 PMCID: PMC9916510 DOI: 10.3390/ijms24032458] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/28/2023] Open
Abstract
Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.
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