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Afonso J, Mascarenhas M, Ribeiro T, Cardoso H, Andrade P, Ferreira JP, Saraiva MM, Macedo G. Deep Learning for Automatic Identification and Characterization of the Bleeding Potential of Enteric Protruding Lesions in Capsule Endoscopy. GASTRO HEP ADVANCES 2022; 1:835-843. [PMID: 39131843 PMCID: PMC11307543 DOI: 10.1016/j.gastha.2022.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/12/2022] [Indexed: 08/13/2024]
Abstract
Background and Aims Capsule endoscopy (CE) revolutionized the study of the small intestine, overcoming the limitations of conventional endoscopy. Nevertheless, reviewing CE images is time-consuming. Convolutional Neural Networks (CNNs) are an artificial intelligence architecture with high performance levels for image analysis. Protruding lesions of the small intestine exhibit enormous morphologic diversity in CE images. We aimed to develop a CNN-based algorithm for automatic detection of varied small-bowel protruding lesions. Methods A CNN was developed using a pool of CE images containing protruding lesions or normal mucosa/other findings. A total of 2565 patients were included. These images were inserted into a CNN model with transfer learning. We evaluated the performance of the network by calculating its sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results A CNN was developed based on a total of 21,320 CE images. Training and validation data sets comprising 80% and 20% of the total pool of images, respectively, were constructed for development and testing of the network. The algorithm automatically detected small-bowel protruding lesions with an accuracy of 97.1%. Our CNN had a sensitivity, specificity, positive, and negative predictive values of 95.9%, 97.1%, 83.0%, and 95.7%, respectively. The CNN operated at a rate of approximately 355 frames per second. Conclusion We developed an accurate CNN for automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.
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Affiliation(s)
- João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Mascarenhas
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João P.S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
| | | | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
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202
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Borri C, Centi S, Chioccioli S, Bogani P, Micheletti F, Gai M, Grandi P, Laschi S, Tona F, Barucci A, Zoppetti N, Pini R, Ratto F. Paper-based genetic assays with bioconjugated gold nanorods and an automated readout pipeline. Sci Rep 2022; 12:6223. [PMID: 35418671 PMCID: PMC9007582 DOI: 10.1038/s41598-022-10227-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/21/2022] [Indexed: 01/10/2023] Open
Abstract
Paper-based biosensors featuring immunoconjugated gold nanoparticles have gained extraordinary momentum in recent times as the platform of choice in key cases of field applications, including the so-called rapid antigen tests for SARS-CoV-2. Here, we propose a revision of this format, one that may leverage on the most recent advances in materials science and data processing. In particular, we target an amplifiable DNA rather than a protein analyte, and we replace gold nanospheres with anisotropic nanorods, which are intrinsically brighter by a factor of ~ 10, and multiplexable. By comparison with a gold-standard method for dot-blot readout with digoxigenin, we show that gold nanorods entail much faster and easier processing, at the cost of a higher limit of detection (from below 1 to 10 ppm in the case of plasmid DNA containing a target transgene, in our current setup). In addition, we test a complete workflow to acquire and process photographs of dot-blot membranes with custom-made hardware and regression tools, as a strategy to gain more analytical sensitivity and potential for quantification. A leave-one-out approach for training and validation with as few as 36 sample instances already improves the limit of detection reached by the naked eye by a factor around 2. Taken together, we conjecture that the synergistic combination of new materials and innovative tools for data processing may bring the analytical sensitivity of paper-based biosensors to approach the level of lab-grade molecular tests.
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Affiliation(s)
- Claudia Borri
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy
| | - Sonia Centi
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy.
| | - Sofia Chioccioli
- Dipartimento di Biologia, Università degli Studi di Firenze, 50019, Sesto Fiorentino, FI, Italy
| | - Patrizia Bogani
- Dipartimento di Biologia, Università degli Studi di Firenze, 50019, Sesto Fiorentino, FI, Italy
| | - Filippo Micheletti
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy
| | - Marco Gai
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy
| | - Paolo Grandi
- Laboratori Victoria S.R.L, 51100, Pistoia, Italy
| | - Serena Laschi
- Ecobioservices & Researches S.R.L, 50019, Sesto Fiorentino, FI, Italy
| | - Francesco Tona
- Ecobioservices & Researches S.R.L, 50019, Sesto Fiorentino, FI, Italy
| | - Andrea Barucci
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy
| | - Nicola Zoppetti
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy
| | - Roberto Pini
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy
| | - Fulvio Ratto
- Istituto di Fisica Applicata "Nello Carrara", Consiglio Nazionale delle Ricerche, 50019, Sesto Fiorentino, FI, Italy
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203
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Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation. Int J Mol Sci 2022; 23:ijms23084216. [PMID: 35457044 PMCID: PMC9032062 DOI: 10.3390/ijms23084216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/04/2022] [Accepted: 04/04/2022] [Indexed: 02/04/2023] Open
Abstract
The maintaining and initiating mechanisms of atrial fibrillation (AF) remain controversial. Deep learning is emerging as a powerful tool to better understand AF and improve its treatment, which remains suboptimal. This paper aims to provide a solution to automatically identify rotational activity drivers in endocardial electrograms (EGMs) with convolutional recurrent neural networks (CRNNs). The CRNN model was compared with two other state-of-the-art methods (SimpleCNN and attention-based time-incremental convolutional neural network (ATI-CNN)) for different input signals (unipolar EGMs, bipolar EGMs, and unipolar local activation times), sampling frequencies, and signal lengths. The proposed CRNN obtained a detection score based on the Matthews correlation coefficient of 0.680, an ATI-CNN score of 0.401, and a SimpleCNN score of 0.118, with bipolar EGMs as input signals exhibiting better overall performance. In terms of signal length and sampling frequency, no significant differences were found. The proposed architecture opens the way for new ablation strategies and driver detection methods to better understand the AF problem and its treatment.
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204
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Vulpoi RA, Luca M, Ciobanu A, Olteanu A, Barboi OB, Drug VL. Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going? Diagnostics (Basel) 2022; 12:diagnostics12040927. [PMID: 35453975 PMCID: PMC9029251 DOI: 10.3390/diagnostics12040927] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
Artificial intelligence, a computer-based concept that tries to mimic human thinking, is slowly becoming part of the endoscopy lab. It has developed considerably since the first attempt at developing an automated medical diagnostic tool, today being adopted in almost all medical fields, digestive endoscopy included. The detection rate of preneoplastic lesions (i.e., polyps) during colonoscopy may be increased with artificial intelligence assistance. It has also proven useful in detecting signs of ulcerative colitis activity. In upper digestive endoscopy, deep learning models may prove to be useful in the diagnosis and management of upper digestive tract diseases, such as gastroesophageal reflux disease, Barrett’s esophagus, and gastric cancer. As is the case with all new medical devices, there are challenges in the implementation in daily medical practice. The regulatory, economic, organizational culture, and language barriers between humans and machines are a few of them. Even so, many devices have been approved for use by their respective regulators. Future studies are currently striving to develop deep learning models that can replicate a growing amount of human brain activity. In conclusion, artificial intelligence may become an indispensable tool in digestive endoscopy.
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Affiliation(s)
- Radu-Alexandru Vulpoi
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
| | - Mihaela Luca
- Institute of Computer Science, Romanian Academy—Iași Branch, 700481 Iași, Romania; (M.L.); (A.C.)
| | - Adrian Ciobanu
- Institute of Computer Science, Romanian Academy—Iași Branch, 700481 Iași, Romania; (M.L.); (A.C.)
| | - Andrei Olteanu
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
| | - Oana-Bogdana Barboi
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
- Correspondence: ; Tel.: +40-74-345-5012
| | - Vasile Liviu Drug
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
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205
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Nogueira-Rodríguez A, Reboiro-Jato M, Glez-Peña D, López-Fernández H. Performance of Convolutional Neural Networks for Polyp Localization on Public Colonoscopy Image Datasets. Diagnostics (Basel) 2022; 12:898. [PMID: 35453946 PMCID: PMC9027927 DOI: 10.3390/diagnostics12040898] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/31/2022] [Accepted: 04/01/2022] [Indexed: 01/10/2023] Open
Abstract
Colorectal cancer is one of the most frequent malignancies. Colonoscopy is the de facto standard for precancerous lesion detection in the colon, i.e., polyps, during screening studies or after facultative recommendation. In recent years, artificial intelligence, and especially deep learning techniques such as convolutional neural networks, have been applied to polyp detection and localization in order to develop real-time CADe systems. However, the performance of machine learning models is very sensitive to changes in the nature of the testing instances, especially when trying to reproduce results for totally different datasets to those used for model development, i.e., inter-dataset testing. Here, we report the results of testing of our previously published polyp detection model using ten public colonoscopy image datasets and analyze them in the context of the results of other 20 state-of-the-art publications using the same datasets. The F1-score of our recently published model was 0.88 when evaluated on a private test partition, i.e., intra-dataset testing, but it decayed, on average, by 13.65% when tested on ten public datasets. In the published research, the average intra-dataset F1-score is 0.91, and we observed that it also decays in the inter-dataset setting to an average F1-score of 0.83.
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Affiliation(s)
- Alba Nogueira-Rodríguez
- CINBIO, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain; (A.N.-R.); (M.R.-J.); (D.G.-P.)
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Miguel Reboiro-Jato
- CINBIO, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain; (A.N.-R.); (M.R.-J.); (D.G.-P.)
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Daniel Glez-Peña
- CINBIO, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain; (A.N.-R.); (M.R.-J.); (D.G.-P.)
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Hugo López-Fernández
- CINBIO, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, Universidade de Vigo, 32004 Ourense, Spain; (A.N.-R.); (M.R.-J.); (D.G.-P.)
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
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206
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Lam AY, Duloy AM, Keswani RN. Quality Indicators for the Detection and Removal of Colorectal Polyps and Interventions to Improve Them. Gastrointest Endosc Clin N Am 2022; 32:329-349. [PMID: 35361339 DOI: 10.1016/j.giec.2021.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Modifiable risk factors for postcolonoscopy colorectal cancer include suboptimal lesion detection (missed neoplasms) and inadequate lesion removal (incomplete polypectomy) during colonoscopy. Competent detection and removal of colorectal polyps are thus fundamental to ensuring adequate colonoscopy quality. Several well-researched quality metrics for polyp detection have been implemented into clinical practice, chief among these the adenoma detection rate. Less data are available on quality indicators for polyp removal, which currently include complete resection rates and skills assessment tools. This review summarizes the available literature on quality indicators for the detection and removal of colorectal polyps, as well as interventions to improve them.
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Affiliation(s)
- Angela Y Lam
- Department of Gastroenterology, Kaiser Permanente San Francisco Medical Center, 2350 Geary Boulevard, San Francisco, CA 94115, USA
| | - Anna M Duloy
- Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Center, 1635 Aurora Court, Aurora, CO 80045, USA
| | - Rajesh N Keswani
- Division of Gastroenterology and Hepatology, Northwestern University Feinberg School of Medicine, 676 North Street, Clair, Suite 1400, Chicago, IL 60611, USA.
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207
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Stidham RW, Takenaka K. Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice? Gastroenterology 2022; 162:1493-1506. [PMID: 34995537 PMCID: PMC8997186 DOI: 10.1053/j.gastro.2021.12.238] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/02/2021] [Accepted: 12/06/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) has arrived and it will directly impact how we assess, monitor, and manage inflammatory bowel disease (IBD). Advances in the machine learning methodologies that power AI have produced astounding results for replicating expert judgment and predicting clinical outcomes, particularly in the analysis of imaging. This review will cover general concepts for AI in IBD, with descriptions of common machine learning methods, including decision trees and neural networks. Applications of AI in IBD will cover recent achievements in endoscopic image interpretation and scoring, new capabilities for cross-sectional image analysis, natural language processing for automated understanding of clinical text, and progress in AI-powered clinical decision support tools. In addition to detailing current evidence supporting the capabilities of AI for replicating expert clinical judgment, speculative commentary on how AI may advance concepts of disease activity assessment, care pathways, and pathophysiologic mechanisms of IBD will be addressed.
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Affiliation(s)
- Ryan W. Stidham
- Division of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, Michigan, USA,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kento Takenaka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
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208
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Larsen SLV, Mori Y. Artificial intelligence in colonoscopy: A review on the current status. DEN OPEN 2022; 2:e109. [PMID: 35873511 PMCID: PMC9302306 DOI: 10.1002/deo2.109] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/14/2022] [Accepted: 02/17/2022] [Indexed: 12/30/2022]
Abstract
Artificial intelligence has become an increasingly hot topic in the last several years, and it has also gained its way into the medical field. In recent years, the application of artificial intelligence in the gastroenterology field has been of increasing interest, particularly in the colonoscopy setting. Novel technologies such as deep neural networks have enabled real‐time computer‐aided polyp detection and diagnosis during colonoscopy. This might lead to increased performance of endoscopists as well as potentially reducing the costs of unnecessary polypectomies of hyperplastic polyps. Newly published prospective trials studying computer‐aided detection showed that the assistance of artificial intelligence significantly increased the detection of polyps and non‐advanced adenomas approximately by 10%, while three tandem randomized control trials proved that the adenoma miss rate was significantly reduced (e.g., 13.8% vs. 36.7% in one Japanese multicenter trial). Promising results have also been shown in prospective single‐arm trials on computer‐aided polyp diagnosis, but the evidence is insufficient to reach a conclusion.
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Affiliation(s)
- Solveig Linnea Veen Larsen
- Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo Oslo Norway.,Department of Transplantation Medicine, Oslo University Hospital University of Oslo Oslo Norway
| | - Yuichi Mori
- Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo Oslo Norway.,Department of Transplantation Medicine, Oslo University Hospital University of Oslo Oslo Norway.,Digestive Disease Center, Showa University Northern Yokohama Hospital Kanagawa Japan
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209
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Repici A, Spadaccini M, Antonelli G, Correale L, Maselli R, Galtieri PA, Pellegatta G, Capogreco A, Milluzzo SM, Lollo G, Di Paolo D, Badalamenti M, Ferrara E, Fugazza A, Carrara S, Anderloni A, Rondonotti E, Amato A, De Gottardi A, Spada C, Radaelli F, Savevski V, Wallace MB, Sharma P, Rösch T, Hassan C. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut 2022; 71:757-765. [PMID: 34187845 DOI: 10.1136/gutjnl-2021-324471] [Citation(s) in RCA: 93] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 06/21/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIMS Artificial intelligence has been shown to increase adenoma detection rate (ADR) as the main surrogate outcome parameter of colonoscopy quality. To which extent this effect may be related to physician experience is not known. We performed a randomised trial with colonoscopists in their qualification period (AID-2) and compared these data with a previously published randomised trial in expert endoscopists (AID-1). METHODS In this prospective, randomised controlled non-inferiority trial (AID-2), 10 non-expert endoscopists (<2000 colonoscopies) performed screening/surveillance/diagnostic colonoscopies in consecutive 40-80 year-old subjects using high-definition colonoscopy with or without a real-time deep-learning computer-aided detection (CADe) (GI Genius, Medtronic). The primary outcome was ADR in both groups with histology of resected lesions as reference. In a post-hoc analysis, data from this randomised controlled trial (RCT) were compared with data from the previous AID-1 RCT involving six experienced endoscopists in an otherwise similar setting. RESULTS In 660 patients (62.3±10 years; men/women: 330/330) with equal distribution of study parameters, overall ADR was higher in the CADe than in the control group (53.3% vs 44.5%; relative risk (RR): 1.22; 95% CI: 1.04 to 1.40; p<0.01 for non-inferiority and p=0.02 for superiority). Similar increases were seen in adenoma numbers per colonoscopy and in small and distal lesions. No differences were observed with regards to detection of non-neoplastic lesions. When pooling these data with those from the AID-1 study, use of CADe (RR 1.29; 95% CI: 1.16 to 1.42) and colonoscopy indication, but not the level of examiner experience (RR 1.02; 95% CI: 0.89 to 1.16) were associated with ADR differences in a multivariate analysis. CONCLUSIONS In less experienced examiners, CADe assistance during colonoscopy increased ADR and a number of related polyp parameters as compared with the control group. Experience appears to play a minor role as determining factor for ADR. TRIAL REGISTRATION NUMBER NCT:04260321.
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Affiliation(s)
- Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Milan, Italy .,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale Nuovo Regina Margherita, Roma, Italy.,Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy
| | - Loredana Correale
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Roberta Maselli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | | | - Gaia Pellegatta
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Antonio Capogreco
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | | | - Gianluca Lollo
- Department of Gastroenterology and Hepatology, Università della Svizzera Italiana, Lugano, Switzerland
| | - Dhanai Di Paolo
- Division of Digestive Endoscopy and Gastroenterology, Valduce Hospital, Como, Italy
| | - Matteo Badalamenti
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Elisa Ferrara
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Alessandro Fugazza
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Silvia Carrara
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Andrea Anderloni
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Emanuele Rondonotti
- Division of Digestive Endoscopy and Gastroenterology, Valduce Hospital, Como, Italy
| | - Arnaldo Amato
- Division of Digestive Endoscopy and Gastroenterology, Valduce Hospital, Como, Italy
| | - Andrea De Gottardi
- Department of Gastroenterology and Hepatology, Università della Svizzera Italiana, Lugano, Switzerland
| | - Cristiano Spada
- Digestive Endoscopy Unit, Poliambulanza Brescia Hospital, Brescia, Lombardia, Italy
| | - Franco Radaelli
- Division of Digestive Endoscopy and Gastroenterology, Valduce Hospital, Como, Italy
| | - Victor Savevski
- Artificial Intelligence Research, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | | | - Prateek Sharma
- University of Kansas, Kansas City, Kansas, USA.,Endoscopy unit, University of Kansas city, Kansas city, Kansas, USA
| | - Thomas Rösch
- Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Cesare Hassan
- Gastroenterology and Digestive Endoscopy Unit, Ospedale Nuovo Regina Margherita, Roma, Italy
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210
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Nagao S, Tani Y, Shibata J, Tsuji Y, Tada T, Ishihara R, Fujishiro M. Implementation of artificial intelligence in upper gastrointestinal endoscopy. DEN OPEN 2022; 2:e72. [PMID: 35873509 PMCID: PMC9302271 DOI: 10.1002/deo2.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/11/2021] [Accepted: 10/16/2021] [Indexed: 12/24/2022]
Abstract
The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image-enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research.
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Affiliation(s)
- Sayaka Nagao
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
- Department of Endoscopy and Endoscopic SurgeryGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Yasuhiro Tani
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Junichi Shibata
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
| | - Yosuke Tsuji
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Tomohiro Tada
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
- AI Medical Service Inc.TokyoJapan
- Department of Surgical OncologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Ryu Ishihara
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Mitsuhiro Fujishiro
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
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211
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Ikematsu H, Murano T, Shinmura K. Detection of colorectal lesions during colonoscopy. DEN OPEN 2022; 2:e68. [PMID: 35310752 PMCID: PMC8828173 DOI: 10.1002/deo2.68] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/11/2022]
Abstract
Owing to its high mortality rate, the prevention of colorectal cancer is of particular importance. The resection of colorectal polyps is reported to drastically reduce colorectal cancer mortality, and examination by endoscopists who had a high adenoma detection rate was found to lower the risk of colorectal cancer, highlighting the importance of identifying lesions. Various devices, imaging techniques, and diagnostic tools aimed at reducing the rate of missed lesions have therefore been developed to improve detection. The distal attachments and devices for improving the endoscopic view angle are intended to help avoid missing blind spots such as folds and flexures in the colon, whereas the imaging techniques represented by image‐enhanced endoscopy contribute to improving lesion visibility. Recent advances in artificial intelligence‐supported detection systems are expected to supplement an endoscopist's eye through the instant diagnosis of the lesions displayed on the monitor. In this review, we provide an outline of each tool and assess its impact on the reduction in the incidence of missed colorectal polyps by summarizing previous clinical research and meta‐analyses. Although useful, the many devices, image‐enhanced endoscopy, and artificial intelligence tools exhibited various limitations. Integrating these tools can improve their shortcomings. Combining artificial intelligence‐based diagnoses with wide‐angle image‐enhanced endoscopy may be particularly useful. Thus, we hope that such tools will be available in the near future.
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Affiliation(s)
- Hiroaki Ikematsu
- Division of Science and Technology for Endoscopy Exploratory Oncology Research & Clinical Trial Center National Cancer Center Chiba Japan.,Department of Gastroenterology and Endoscopy National Cancer Center Hospital East Chiba Japan
| | - Tatsuro Murano
- Department of Gastroenterology and Endoscopy National Cancer Center Hospital East Chiba Japan
| | - Kensuke Shinmura
- Department of Gastroenterology and Endoscopy National Cancer Center Hospital East Chiba Japan
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212
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Spadaccini M, Marco AD, Franchellucci G, Sharma P, Hassan C, Repici A. Discovering the first US FDA-approved computer-aided polyp detection system. Future Oncol 2022; 18:1405-1412. [PMID: 35081745 DOI: 10.2217/fon-2021-1135] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is the third most common cancer worldwide. Because of the slow progression of the precancerous precursors, an efficient endoscopic surveillance strategy may be expected. It seems that around one-fourth of colorectal malignancies are still missed during colonoscopy. Several endoscopic technologies have been introduced, without radical changes. Interest in the development of artificial intelligence applications in the medical field has grown in the past decade. Artificial intelligence can help to highlight a specific region of interest that needs closer examination for the identification of polyps. The aim of this review is to report the first clinical experiences with the first US FDA-approved, real-time, deep-learning, computer-aided detection system (GI Genius™, Medtronic).
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro De Marco
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Gianluca Franchellucci
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology & Hepatology, Kansas City, MO 66045, USA
| | - Cesare Hassan
- Nuovo Regina Margherita Hospital, Digestive Endoscopy Unit, Rome, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
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213
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Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health 2022; 4:e436-e444. [DOI: 10.1016/s2589-7500(22)00042-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/28/2022] [Accepted: 03/01/2022] [Indexed: 02/07/2023]
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214
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Troya J, Fitting D, Brand M, Sudarevic B, Kather JN, Meining A, Hann A. The influence of computer-aided polyp detection systems on reaction time for polyp detection and eye gaze. Endoscopy 2022; 54:1009-1014. [PMID: 35158384 PMCID: PMC9500006 DOI: 10.1055/a-1770-7353] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Multiple computer-aided systems for polyp detection (CADe) have been introduced into clinical practice, with an unclear effect on examiner behavior. This study aimed to measure the influence of a CADe system on reaction time, mucosa misinterpretation, and changes in visual gaze pattern. METHODS Participants with variable levels of colonoscopy experience viewed video sequences (n = 29) while eye movement was tracked. Using a crossover design, videos were presented in two assessments, with and without CADe support. Reaction time for polyp detection and eye-tracking metrics were evaluated. RESULTS 21 participants performed 1218 experiments. CADe was significantly faster in detecting polyps compared with participants (median 1.16 seconds [99 %CI 0.40-3.43] vs. 2.97 seconds [99 %CI 2.53-3.77], respectively). However, the reaction time of participants when using CADe (median 2.90 seconds [99 %CI 2.55-3.38]) was similar to that without CADe. CADe increased misinterpretation of normal mucosa and reduced the eye travel distance. CONCLUSIONS Results confirm that CADe systems detect polyps faster than humans. However, use of CADe did not improve human reaction times. It increased misinterpretation of normal mucosa and decreased the eye travel distance. Possible consequences of these findings might be prolonged examination time and deskilling.
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Affiliation(s)
- Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Daniel Fitting
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Markus Brand
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Boban Sudarevic
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | | | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
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215
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Van Berkel N, Opie J, Ahmad OF, Lovat L, Stoyanov D, Blandford A. Initial Responses to False Positives in AI-Supported Continuous Interactions: A Colonoscopy Case Study. ACM T INTERACT INTEL 2022. [DOI: 10.1145/3480247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
The use of artificial intelligence (AI) in clinical support systems is increasing. In this article, we focus on AI support for continuous interaction scenarios. A thorough understanding of end-user behaviour during these continuous human-AI interactions, in which user input is sustained over time and during which AI suggestions can appear at any time, is still missing. We present a controlled lab study involving 21 endoscopists and an AI colonoscopy support system. Using a custom-developed application and an off-the-shelf videogame controller, we record participants’ navigation behaviour and clinical assessment across 14 endoscopic videos. Each video is manually annotated to mimic an AI recommendation, being either true positive or false positive in nature. We find that time between AI recommendation and clinical assessment is significantly longer for incorrect assessments. Further, the type of medical content displayed significantly affects decision time. Finally, we discover that the participant’s clinical role plays a large part in the perception of clinical AI support systems. Our study presents a realistic assessment of the effects of imperfect and continuous AI support in a clinical scenario.
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Affiliation(s)
- Niels Van Berkel
- Aalborg University, Denmark and University College London, London, United Kingdom
| | - Jeremy Opie
- University College London, London, United Kingdom
| | | | - Laurence Lovat
- University College London Hospitals, London, United Kingdom
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216
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Rajan V, Srinath H, Bong CYS, Cichowski A, Young CJ, Hewett PJ. Software Analysis of Colonoscopy Videos Enhances Teaching and Quality Metrics. Cureus 2022; 14:e23039. [PMID: 35464512 PMCID: PMC9001872 DOI: 10.7759/cureus.23039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2022] [Indexed: 11/29/2022] Open
Abstract
Purpose Machine learning algorithms were hypothesized as being able to predict the quality of colonoscopy luminal images. This is to enhance training and quality indicators in endoscopy. Methods A separate study involving a randomized controlled trial of capped vs. un-capped colonoscopies provided the colonoscopy videos for this study. Videos were analyzed with an algorithm devised by the Australian Institute for Machine Learning. The image analysis validated focus measure, steerable filters-based metrics (SFIL), was used to assess luminal visualization quality and was compared with two independent clinician assessments (C1 and C2). Goodman and Kruskal's gamma (G) measure was used to assess rank correlation data using IBM SPSS Statistics for Windows, version 25.0 (IBM Corp., Armonk, NY). Results A total of 500 random colonoscopy video clips were extracted and analyzed, 88 being excluded. SFIL scores matched with C1 in 45% and C2 in 42% of cases, respectively. There was a significant correlation between SFIL and C1 (G = 0.644, p < 0.005) and SFIL and C2 (G = 0.734, p < 0.005). Conclusion This study demonstrates that machine learning algorithms can recognize the quality of luminal visualization during colonoscopy. We intend to apply this in the future to enhance colonoscopy training and as a metric for quality assessment.
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217
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Schrader C, Wallstabe I, Schiefke I. Künstliche Intelligenz in der Vorsorgekoloskopie. COLOPROCTOLOGY 2022. [DOI: 10.1007/s00053-022-00593-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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218
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Schmitz R, Werner R, Repici A, Bisschops R, Meining A, Zornow M, Messmann H, Hassan C, Sharma P, Rösch T. Artificial intelligence in GI endoscopy: stumbling blocks, gold standards and the role of endoscopy societies. Gut 2022; 71:451-454. [PMID: 33479051 DOI: 10.1136/gutjnl-2020-323115] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 02/06/2023]
Affiliation(s)
- Rüdiger Schmitz
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Rene Werner
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alessandro Repici
- Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.,Humanitas University, Department of Biomedical Sciences, Milan, Italy
| | - Raf Bisschops
- Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium
| | - Alexander Meining
- Department of Gastroenterology, University of Würzburg, Würzburg, Germany
| | - Michael Zornow
- Chair for Public and European Law, University of Göttingen, Göttingen, Germany
| | - Helmut Messmann
- Department of Gastroenterology, Universitätsklinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, Veterans Affairs Medical Center and University of Kansas, Lawrence, Kansas, USA
| | - Thomas Rösch
- Interdisciplinary Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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219
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Li JW, Wang LM, Ang TL. Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications. Singapore Med J 2022; 63:118-124. [PMID: 35509251 PMCID: PMC9251247 DOI: 10.11622/smedj.2022044] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
| | - Lai Mun Wang
- Pathology Section, Department of Laboratory Medicine, Changi General Hospital, Singapore
- SingHealth Duke-NUS Pathology Academic Clinical Programme, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
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220
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Effect of artificial intelligence-aided colonoscopy for adenoma and polyp detection: a meta-analysis of randomized clinical trials. Int J Colorectal Dis 2022; 37:495-506. [PMID: 34762157 DOI: 10.1007/s00384-021-04062-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/29/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND This meta-analysis aimed to determine whether artificial intelligence (AI) improves colonoscopy outcome metrics i.e. adenoma detection rate (ADR) and polyp detection rate (PDR). METHODS Two authors independently searched Web of Science, PubMed, Science Direct, and Cochrane Library to find all published research before July 2021 that has compared AI-aided colonoscopy with routine colonoscopy (RC) for detection of adenoma and polyp. RESULTS This meta-analysis included 10 RCTs with 6629 individuals in AI-aided (n = 3300) and routine (n = 3329) groups. The results showed that both ADR (RR, 1.43; P < 0.001) and PDR (RR, 1.44; P < 0.001) using AI-aided endoscopy were significantly greater when compared with RC. The adenomas detected per colonoscopy (APC) (WMD, 0.25; P = 0.009), polyps detected per colonoscopy (PPC) (WMD, 0.52; P < 0.001), and sessile serrated lesions detected per colonoscopy (SSLPC) (RR, 1.53; P < 0.001) were significantly higher in the AI-aided group compared with the RC group. Subgroup analysis based on size, location, and shape of adenomas and polyps demonstrated that, except for in the cecum and pedunculated adenomas or polyps, the AI-aided groups of the other subgroups are more advantageous. Withdrawal time was longer in the AI-aided group when biopsies were included, while withdrawal time excluding biopsy time showed no significant difference. CONCLUSIONS AI-aided polyp detection system significantly increases lesion detection rate. In addition, lesion detection by AI is hardly affected by factors such as size, location, and shape.
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221
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Marques KF, Marques AF, Lopes MA, Beraldo RF, Lima TB, Sassaki LY. Artificial intelligence in colorectal cancer screening in patients with inflammatory bowel disease. Artif Intell Gastrointest Endosc 2022; 3:1-8. [DOI: 10.37126/aige.v3.i1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/14/2022] [Accepted: 02/24/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a branch of computer science that develops intelligent machines. In recent years, medicine has been contemplated with this recent modality to aid in the diagnosis of diseases in several specialties, including gastroenterology and gastrointestinal endoscopy. This new technology has superior ability to perform tasks mimicking human behavior and can identify possible pathological alterations, such as pre-malignant lesions and dysplasia, precursor lesions of colorectal cancer (CRC), and support medical decision-making. CRC is among the three most prevalent cancer types, and the second most common cause of cancer-related deaths worldwide; in addition, it is a leading cause of death in patients with inflammatory bowel disease (IBD). Patients with IBD tend to have greater inflammatory cell activity in the intestinal mucosa, which can favor cell proliferation and CRC development. AI can contribute to the detection of pre-neoplastic lesions in patients at risk of CRC development, such as those with extensive IBD or when additional CRC risk factors, such as smoking, are present. In fact, AI systems could improve all aspects of care related to both the detection of pre-malignant and malignant lesions and the screening of patients with IBD. In this review, we aimed to show the benefits and innovations of AI in the screening of CRC in patients with IBD. The promising applications of AI have the potential to revolutionize clinical practice and gastrointestinal endoscopy, especially in at-risk patients, such as those with IBD.
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Affiliation(s)
- Kêmily Fuentes Marques
- Curso de Medicina, Faculdades de Dracena, Fundação Dracenense de Educação e Cultura, Rua Bahia, 332, Dracena, SP, 17900-000, São Paulo, Brasil
| | - Alana Fuentes Marques
- Curso de Medicina, Faculdades de Dracena, Fundação Dracenense de Educação e Cultura, Rua Bahia, 332, Dracena, SP, 17900-000, São Paulo, Brasil
| | - Marina Amorim Lopes
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
| | - Rodrigo Fedatto Beraldo
- Curso de Medicina, Faculdades de Dracena, Fundação Dracenense de Educação e Cultura, Rua Bahia, 332, Dracena, SP, 17900-000, São Paulo, Brasil
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
| | - Talles Bazeia Lima
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
| | - Ligia Yukie Sassaki
- Department of Internal Medicine, São Paulo State University, Medical School, Botucatu, Brazil
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222
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Chen Y, Qu L, Guo L. How can we improve our operation? Endoscopy 2022; 54:222. [PMID: 35086156 DOI: 10.1055/a-1690-6518] [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: 02/08/2023]
Affiliation(s)
- Yonghao Chen
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, Sichuan University, Chengdu, China
| | - Lang Qu
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Linjie Guo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
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223
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Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc 2022; 95:269-280.e6. [PMID: 34547254 DOI: 10.1016/j.gie.2021.09.017] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE. METHODS Endoscopic images were retrospectively obtained from Renmin Hospital of Wuhan University (RHWU) for the development, validation, and internal test of the system. Additional external tests were conducted in 5 other hospitals to evaluate the robustness. Stored videos from RHWU were used for assessing and comparing the performance of ENDOANGEL-LD with that of experts. Prospective consecutive patients undergoing upper endoscopy were enrolled from May 6, 2021 to August 2, 2021 in RHWU to assess clinical practice applicability. RESULTS Over 10,000 patients undergoing upper endoscopy were enrolled in this study. The sensitivities were 96.9% and 95.6% for detecting gastric lesions and 92.9% and 91.7% for diagnosing neoplasms in internal and external patients, respectively. In 100 videos, ENDOANGEL-LD achieved superior sensitivity and negative predictive value for detecting gastric neoplasms from that of experts (100% vs 85.5% ± 3.4% [P = .003] and 100% vs 86.4% ± 2.8% [P = .002], respectively). In 2010 prospective consecutive patients, ENDOANGEL-LD achieved a sensitivity of 92.8% for detecting gastric lesions with 3.04 ± 3.04 false positives per gastroscopy and a sensitivity of 91.8% and specificity of 92.4% for diagnosing neoplasms. CONCLUSIONS Our results show that ENDOANGEL-LD has great potential for assisting endoscopists in screening gastric lesions and suspicious neoplasms in clinical work. (Clinical trial registration number: ChiCTR2100045963.).
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224
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Zorzi M, Hassan C, Battagello J, Antonelli G, Pantalena M, Bulighin G, Alicante S, Meggiato T, Rosa-Rizzotto E, Iacopini F, Luigiano C, Monica F, Arrigoni A, Germanà B, Valiante F, Mallardi B, Senore C, Grazzini G, Mantellini P. Adenoma detection by Endocuff-assisted versus standard colonoscopy in an organized screening program: the "ItaVision" randomized controlled trial. Endoscopy 2022; 54:138-147. [PMID: 33524994 DOI: 10.1055/a-1379-6868] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The Endocuff Vision device (Arc Medical Design Ltd., Leeds, UK) has been shown to increase mucosal exposure, and consequently adenoma detection rate (ADR), during colonoscopy. This nationwide multicenter study assessed possible benefits and harms of using Endocuff Vision in a fecal immunochemical test (FIT)-based screening program. METHODS Patients undergoing colonoscopy after a FIT-positive test were randomized 1:1 to undergo Endocuff-assisted colonoscopy or standard colonoscopy, stratified by sex, age, and screening history. Primary outcome was ADR. Secondary outcomes were ADR stratified by endoscopists' ADR, advanced ADR (AADR), adenomas per colonoscopy (APC), withdrawal time, and adverse events. RESULTS 1866 patients were enrolled across 13 centers. After exclusions, 1813 (mean age 60.1 years; male 53.8 %) were randomized (908 Endocuff Vision, 905 standard colonoscopy). ADR was significantly higher in the Endocuff Vision arm (47.8 % vs. 40.8 %; relative risk [RR] 1.17, 95 % confidence interval [CI] 1.06-1.30), with no differences between arms regarding size or morphology. When stratifying for endoscopists' ADR, only low detectors (ADR < 33.3 %) showed a statistically significant ADR increase (Endocuff Vision 41.1 % [95 %CI 35.7-46.7] vs. standard colonoscopy 26.0 % [95 %CI 21.3-31.4]). AADR (24.8 % vs. 20.5 %, RR 1.21, 95 %CI 1.02-1.43) and APC (0.94 vs. 0.77; P = 0.001) were higher in the Endocuff Vision arm. Withdrawal time and adverse events were similar between arms. CONCLUSION Endocuff Vision increased ADR in a FIT-based screening program by improving examination of the whole colonic mucosa. Utility was highest among endoscopists with a low ADR.
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Affiliation(s)
- Manuel Zorzi
- Veneto Tumor Registry, Azienda Zero, Padova, Italy
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | | | - Giulio Antonelli
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy.,Department of Translational and Precision Medicine, "Sapienza" University of Rome, Italy.,Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli (N.O.C.), ASL Roma 6, Ariccia, Rome, Italy
| | - Maurizio Pantalena
- Gastroenterology Unit, Cazzavillan Hospital, ULSS 8 Berica, Arzignano, Italy
| | - Gianmarco Bulighin
- Gastroenterology and Digestive Endoscopy Unit, Fracastoro Hospital, ULSS 9 Scaligera, San Bonifacio, Italy
| | - Saverio Alicante
- Gastroenterology Department, ASST-Crema, Maggiore Hospital, Crema, Italy
| | - Tamara Meggiato
- Department of Gastroenterology, Rovigo General Hospital, ULSS 5 Polesana, Rovigo, Italy
| | - Erik Rosa-Rizzotto
- Gastroenterology Unit, St. Anthony Hospital, Azienda Ospedale-Università, Padua, Italy
| | - Federico Iacopini
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli (N.O.C.), ASL Roma 6, Ariccia, Rome, Italy
| | - Carmelo Luigiano
- Unit of Digestive Endoscopy, ASST Santi Paolo e Carlo, Milan, Italy
| | - Fabio Monica
- Gastroenterology and Digestive Endoscopy Unit, Cattinara University Hospital, Trieste, Italy
| | - Arrigo Arrigoni
- Gastroenterology Unit, University Hospital Città della Salute e della Scienza, Turin, Italy
| | - Bastianello Germanà
- Gastroenterology and Digestive Endoscopy Unit, San Martino Hospital, ULSS 1 Dolomiti, Belluno, Italy
| | - Flavio Valiante
- Gastroenterology and Digestive Endoscopy Unit, Santa Maria del Prato Hospital, ULSS 1 Dolomiti, Feltre, Italy
| | - Beatrice Mallardi
- Screening Unit, Institute for Cancer Research, Prevention and Oncological Network (ISPRO), Florence, Italy
| | - Carlo Senore
- Epidemiology and Screening Unit - CPO, University Hospital Città della Salute e della Scienza, Turin, Italy
| | - Grazia Grazzini
- Screening Unit, Institute for Cancer Research, Prevention and Oncological Network (ISPRO), Florence, Italy
| | - Paola Mantellini
- Screening Unit, Institute for Cancer Research, Prevention and Oncological Network (ISPRO), Florence, Italy
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225
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Pech O, Zippelius C. Reply to Chen et al. Endoscopy 2022; 54:223. [PMID: 35086157 DOI: 10.1055/a-1722-2963] [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: 12/10/2022]
Affiliation(s)
- Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Carolin Zippelius
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
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226
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Hann A, Meining A. Artificial Intelligence in Endoscopy. Visc Med 2022; 37:471-475. [PMID: 35083312 DOI: 10.1159/000519407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/30/2021] [Indexed: 12/23/2022] Open
Abstract
Background Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized. Summary In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed. Key Messages The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.
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Affiliation(s)
- Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, Gastroenterology, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, Gastroenterology, University Hospital Würzburg, Würzburg, Germany
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Artificial Endoscopy and Inflammatory Bowel Disease: Welcome to the Future. J Clin Med 2022; 11:jcm11030569. [PMID: 35160021 PMCID: PMC8836846 DOI: 10.3390/jcm11030569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is assuming an increasingly important and central role in several medical fields. Its application in endoscopy provides a powerful tool supporting human experiences in the detection, characterization, and classification of gastrointestinal lesions. Lately, the potential of AI technology has been emerging in the field of inflammatory bowel disease (IBD), where the current cornerstone is the treat-to-target strategy. A sensible and specific tool able to overcome human limitations, such as AI, could represent a great ally and guide precision medicine decisions. Here we reviewed the available literature on the endoscopic applications of AI in order to properly describe the current state-of-the-art and identify the research gaps in IBD at the dawn of 2022.
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228
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Pan H, Cai M, Liao Q, Jiang Y, Liu Y, Zhuang X, Yu Y. Artificial Intelligence-Aid Colonoscopy Vs. Conventional Colonoscopy for Polyp and Adenoma Detection: A Systematic Review of 7 Discordant Meta-Analyses. Front Med (Lausanne) 2022; 8:775604. [PMID: 35096870 PMCID: PMC8792899 DOI: 10.3389/fmed.2021.775604] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/20/2021] [Indexed: 12/16/2022] Open
Abstract
Objectives: Multiple meta-analyses which investigated the comparative efficacy and safety of artificial intelligence (AI)-aid colonoscopy (AIC) vs. conventional colonoscopy (CC) in the detection of polyp and adenoma have been published. However, a definitive conclusion has not yet been generated. This systematic review selected from discordant meta-analyses to draw a definitive conclusion about whether AIC is better than CC for the detection of polyp and adenoma. Methods: We comprehensively searched potentially eligible literature in PubMed, Embase, Cochrane library, and China National Knowledgement Infrastructure (CNKI) databases from their inceptions until to April 2021. Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to assess the methodological quality. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to assess the reporting quality. Two investigators independently used the Jadad decision algorithm to select high-quality meta-analyses which summarized the best available evidence. Results: Seven meta-analyses met our selection criteria finally. AMSTAR score ranged from 8 to 10, and PRISMA score ranged from 23 to 26. According to the Jadad decision algorithm, two high-quality meta-analyses were selected. These two meta-analyses suggested that AIC was superior to CC for colonoscopy outcomes, especially for polyp detection rate (PDR) and adenoma detection rate (ADR). Conclusion: Based on the best available evidence, we conclude that AIC should be preferentially selected for the route screening of colorectal lesions because it has potential value of increasing the polyp and adenoma detection. However, the continued improvement of AIC in differentiating the shape and pathology of colorectal lesions is needed.
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Affiliation(s)
- Hui Pan
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
| | - Mingyan Cai
- Endoscopy Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qi Liao
- Department of Gastroenterology, Shanghai Jiangong Hospital, Shanghai, China
| | - Yong Jiang
- Department of Surgery, Shanghai Jiangong Hospital, Shanghai, China
| | - Yige Liu
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
| | - Xiaolong Zhuang
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
| | - Ying Yu
- Department of Endoscopy, Shanghai Jiangong Hospital, Shanghai, China
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229
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Zhuang H, Bao A, Tan Y, Wang H, Xie Q, Qiu M, Xiong W, Liao F. Application and prospect of artificial intelligence in digestive endoscopy. Expert Rev Gastroenterol Hepatol 2022; 16:21-31. [PMID: 34937459 DOI: 10.1080/17474124.2022.2020646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION With the progress of science and technology, artificial intelligence represented by deep learning has gradually begun to be applied in the medical field. Artificial intelligence has been applied to benign gastrointestinal lesions, tumors, early cancer, inflammatory bowel disease, gallbladder, pancreas, and other diseases. This review summarizes the latest research results on artificial intelligence in digestive endoscopy and discusses the prospect of artificial intelligence in digestive system diseases. AREAS COVERED We retrieved relevant documents on artificial intelligence in digestive tract diseases from PubMed and Medline. This review elaborates on the knowledge of computer-aided diagnosis in digestive endoscopy. EXPERT OPINION Artificial intelligence significantly improves diagnostic accuracy, reduces physicians' workload, and provides a shred of evidence for clinical diagnosis and treatment. Shortly, artificial intelligence will have high application value in the field of medicine.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Anyu Bao
- Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yulin Tan
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hanyu Wang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qingfang Xie
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Meiqi Qiu
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wanli Xiong
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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230
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Self-attention Based High Order Sequence Features of Dynamic Functional Connectivity Networks with rs-fMRI for Brain Disease Classification. ARTIF INTELL 2022. [DOI: 10.1007/978-3-031-20500-2_51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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231
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Glissen Brown JR, Waljee AK, Mori Y, Sharma P, Berzin TM. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc 2022; 34:4-12. [PMID: 33715244 DOI: 10.1111/den.13974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Akbar K Waljee
- Division of Gastroenterology, University of Michigan Health System, University of Michigan, Ann Arbor, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
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232
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Artificial intelligence: A promising frontier in bladder cancer diagnosis and outcome prediction. Crit Rev Oncol Hematol 2022; 171:103601. [DOI: 10.1016/j.critrevonc.2022.103601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 02/07/2023] Open
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233
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Abstract
Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.
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Affiliation(s)
- Chaoran Yu
- Department of General Surgery, Shanghai Ninth People’ Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 People’s Republic of China
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430030 People’s Republic of China
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234
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Kader R, Baggaley RF, Hussein M, Ahmad OF, Patel N, Corbett G, Dolwani S, Stoyanov D, Lovat LB. Survey on the perceptions of UK gastroenterologists and endoscopists to artificial intelligence. Frontline Gastroenterol 2022; 13:423-429. [PMID: 36046492 PMCID: PMC9380773 DOI: 10.1136/flgastro-2021-101994] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/21/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND AIMS With the potential integration of artificial intelligence (AI) into clinical practice, it is essential to understand end users' perception of this novel technology. The aim of this study, which was endorsed by the British Society of Gastroenterology (BSG), was to evaluate the UK gastroenterology and endoscopy communities' views on AI. METHODS An online survey was developed and disseminated to gastroenterologists and endoscopists across the UK. RESULTS One hundred four participants completed the survey. Quality improvement in endoscopy (97%) and better endoscopic diagnosis (92%) were perceived as the most beneficial applications of AI to clinical practice. The most significant challenges were accountability for incorrect diagnoses (85%) and potential bias of algorithms (82%). A lack of guidelines (92%) was identified as the greatest barrier to adopting AI in routine clinical practice. Participants identified real-time endoscopic image diagnosis (95%) as a research priority for AI, while the most perceived significant barriers to AI research were funding (82%) and the availability of annotated data (76%). Participants consider the priorities for the BSG AI Task Force to be identifying research priorities (96%), guidelines for adopting AI devices in clinical practice (93%) and supporting the delivery of multicentre clinical trials (91%). CONCLUSION This survey has identified views from the UK gastroenterology and endoscopy community regarding AI in clinical practice and research, and identified priorities for the newly formed BSG AI Task Force.
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Affiliation(s)
- Rawen Kader
- Division of Surgery and Interventional Sciences, University College London, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
- Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Rebecca F Baggaley
- Department of Respiratory Infections, University of Leicester, Leicester, UK
| | - Mohamed Hussein
- Division of Surgery and Interventional Sciences, University College London, London, UK
- Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
- Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Nisha Patel
- Department of Gastroenterology, Imperial College Healthcare NHS Trust, London, UK
| | - Gareth Corbett
- Department of Gastroenterology, Addenbrooke's Hospital, Cambridge, UK
| | - Sunil Dolwani
- Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK
| | - Danail Stoyanov
- Division of Surgery and Interventional Sciences, University College London, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
| | - Laurence B Lovat
- Division of Surgery and Interventional Sciences, University College London, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
- Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, London, UK
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235
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Soons E, Rath T, Hazewinkel Y, van Dop WA, Esposito D, Testoni PA, Siersema PD. Real-time colorectal polyp detection using a novel computer-aided detection system (CADe): a feasibility study. Int J Colorectal Dis 2022; 37:2219-2228. [PMID: 36163514 PMCID: PMC9560918 DOI: 10.1007/s00384-022-04258-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/18/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND AIMS Colonoscopy aims to early detect and remove precancerous colorectal polyps, thereby preventing development of colorectal cancer (CRC). Recently, computer-aided detection (CADe) systems have been developed to assist endoscopists in polyp detection during colonoscopy. The aim of this study was to investigate feasibility and safety of a novel CADe system during real-time colonoscopy in three European tertiary referral centers. METHODS Ninety patients undergoing colonoscopy assisted by a real-time CADe system (DISCOVERY; Pentax Medical, Tokyo, Japan) were prospectively included. The CADe system was turned on only at withdrawal, and its output was displayed on secondary monitor. To study feasibility, inspection time, polyp detection rate (PDR), adenoma detection rate (ADR), sessile serrated lesion (SSL) detection rate (SDR), and the number of false positives were recorded. To study safety, (severe) adverse events ((S)AEs) were collected. Additionally, user friendliness was rated from 1 (worst) to 10 (best) by endoscopists. RESULTS Mean inspection time was 10.8 ± 4.3 min, while PDR was 55.6%, ADR 28.9%, and SDR 11.1%. The CADe system users estimated that < 20 false positives occurred in 81 colonoscopy procedures (90%). No (S)AEs related to the CADe system were observed during the 30-day follow-up period. User friendliness was rated as good, with a median score of 8/10. CONCLUSION Colonoscopy with this novel CADe system in a real-time setting was feasible and safe. Although PDR and SDR were high compared to previous studies with other CADe systems, future randomized controlled trials are needed to confirm these detection rates. The high SDR is of particular interest since interval CRC has been suggested to develop frequently through the serrated neoplasia pathway. CLINICAL TRIAL REGISTRATION The study was registered in the Dutch Trial Register (reference number: NL8788).
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Affiliation(s)
- E. Soons
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - T. Rath
- Department of Internal Medicine 1, Division of Gastroenterology, Friedrich-Alexander-University, Ludwig Demling Endoscopy Center of Excellence, Erlangen Nuernberg, Germany
| | - Y. Hazewinkel
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - W. A. van Dop
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
| | - D. Esposito
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Scientific Institute San Raffaele, Milan, Italy
| | - P. A. Testoni
- Gastroenterology and Gastrointestinal Endoscopy Unit, Vita-Salute San Raffaele University, Scientific Institute San Raffaele, Milan, Italy
| | - P. D. Siersema
- Department of Gastroenterology and Hepatology, Radboud Institute for Health Sciences, Radboud University Medical Center, 9101, 6500 HB Nijmegen, the Netherlands
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Almario CV, Shergill J, Oh J. Measuring and Improving Quality of Colonoscopy for Colorectal Cancer Screening. TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY 2022; 24:269-283. [PMID: 36778081 PMCID: PMC9910391 DOI: 10.1016/j.tige.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Colorectal cancer (CRC) is largely preventable, yet it remains a major public health issue as it is the third most common and deadly malignancy in the United States. While there are many ways to screen for CRC, colonoscopy remains the gold standard as it is the only test that is both cancer-detecting and cancer-preventing through removal of precancerous polyps. Through identifying and removing neoplastic lesions, colonoscopy reduces CRC incidence by 31%-91% and CRC mortality by 65%-88%. However, colonoscopy is not an infallible test-there is a chance for missed lesions during the exam and there is substantial variation in outcomes among endoscopists. To enhance the quality of colonoscopic exams, and ultimately to improve CRC outcomes, quality indicators have been developed for measuring endoscopists' performance. In this review, we describe the colonoscopic quality indicators and benchmarks recommended by the American Society for Gastrointestinal Endoscopy/American College of Gastroenterology Task Force on Quality in Endoscopy for screening colonoscopies in average-risk individuals. Measuring and monitoring endoscopists' performance on these measures are critical first steps in striving toward conducting high quality exams. We also review the evidence for interventions that aim to improve critical measures including adenoma detection rate, withdrawal time, cecal intubation, and bowel preparation quality. Finally, we provide a preview of the forthcoming Advancing Care for Appropriate Colon Health Merit-Based Incentive Payment System Value Pathway by the Centers for Medicare & Medicaid Services and its potential impact on clinical practice.
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Affiliation(s)
- Christopher V. Almario
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California;,Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California;,Division of Health Services Research, Cedars-Sinai Medical Center, Los Angeles, California;,Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, California;,Division of Informatics, Cedars-Sinai Medical Center, Los Angeles, California;,Cancer Prevention & Control Program, Cedars-Sinai Cancer, Los Angeles, California
| | - Jaspreet Shergill
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Janice Oh
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
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237
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Artificial Intelligence in Gastroenterology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-58080-3_163-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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238
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Okamoto Y, Yoshida S, Izakura S, Katayama D, Michida R, Koide T, Tamaki T, Kamigaichi Y, Tamari H, Shimohara Y, Nishimura T, Inagaki K, Tanaka H, Yamashita K, Sumimoto K, Oka S, Tanaka S. Development of multi-class computer-aided diagnostic systems using the NICE/JNET classifications for colorectal lesions. J Gastroenterol Hepatol 2022; 37:104-110. [PMID: 34478167 DOI: 10.1111/jgh.15682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/22/2021] [Accepted: 08/30/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND AIM Diagnostic support using artificial intelligence may contribute to the equalization of endoscopic diagnosis of colorectal lesions. We developed computer-aided diagnosis (CADx) support system for diagnosing colorectal lesions using the NBI International Colorectal Endoscopic (NICE) classification and the Japan NBI Expert Team (JNET) classification. METHODS Using Residual Network as the classifier and NBI images as training images, we developed a CADx based on the NICE classification (CADx-N) and a CADx based on the JNET classification (CADx-J). For validation, 480 non-magnifying and magnifying NBI images were used for the CADx-N and 320 magnifying NBI images were used for the CADx-J. The diagnostic performance of the CADx-N was evaluated using the magnification rate. RESULTS The accuracy of the CADx-N for Types 1, 2, and 3 was 97.5%, 91.2%, and 93.8%, respectively. The diagnostic performance for each magnification level was good (no statistically significant difference). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the CADx-J were 100%, 96.3%, 82.8%, 100%, and 96.9% for Type 1; 80.3%, 93.7%, 94.1%, 79.2%, and 86.3% for Type 2A; 80.4%, 84.7%, 46.8%, 96.3%, and 84.1% for Type 2B; and 62.5%, 99.6%, 96.8%, 93.8%, and 94.1% for Type 3, respectively. CONCLUSIONS The multi-class CADx systems had good diagnostic performance with both the NICE and JNET classifications and may aid in educating non-expert endoscopists and assist in diagnosing colorectal lesions.
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Affiliation(s)
- Yuki Okamoto
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Shigeto Yoshida
- Department of Gastroenterology, JR Hiroshima Hospital, Hiroshima, Japan
| | - Seiji Izakura
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Daisuke Katayama
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Ryuichi Michida
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Tetsushi Koide
- Research Institute for Nanodevice and Bio Systems, Hiroshima University, Hiroshima, Japan
| | - Toru Tamaki
- Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
| | - Yuki Kamigaichi
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Hirosato Tamari
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Yasutsugu Shimohara
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Tomoyuki Nishimura
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Katsuaki Inagaki
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Hidenori Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Ken Yamashita
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Kyoku Sumimoto
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Shiro Oka
- Department of Gastroenterology and Metabolism, Hiroshima University Hospital, Hiroshima, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
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Strümke I, Hicks SA, Thambawita V, Jha D, Parasa S, Riegler MA, Halvorsen P. Artificial Intelligence in Gastroenterology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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240
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Lux TJ, Banck M, Saßmannshausen Z, Troya J, Krenzer A, Fitting D, Sudarevic B, Zoller WG, Puppe F, Meining A, Hann A. Pilot study of a new freely available computer-aided polyp detection system in clinical practice. Int J Colorectal Dis 2022; 37:1349-1354. [PMID: 35543874 PMCID: PMC9167159 DOI: 10.1007/s00384-022-04178-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/01/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system. METHODS We developed an end-to-end data acquisition and polyp detection system named EndoMind. Examiners of four centers utilizing four different endoscopy processors used EndoMind during their clinical routine. Detected polyps, ADR, time to first detection of a polyp (TFD), and system usability were evaluated (NCT05006092). RESULTS During 41 colonoscopies, EndoMind detected 29 of 29 adenomas in 66 of 66 polyps resulting in an ADR of 41.5%. Median TFD was 130 ms (95%-CI, 80-200 ms) while maintaining a median false positive rate of 2.2% (95%-CI, 1.7-2.8%). The four participating centers rated the system using the System Usability Scale with a median of 96.3 (95%-CI, 70-100). CONCLUSION EndoMind's ability to acquire data, detect polyps in real-time, and high usability score indicate substantial practical value for research and clinical practice. Still, clinical benefit, measured by ADR, has to be determined in a prospective randomized controlled trial.
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Affiliation(s)
- Thomas J. Lux
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Michael Banck
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany ,grid.8379.50000 0001 1958 8658Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Zita Saßmannshausen
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Joel Troya
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Adrian Krenzer
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany ,grid.8379.50000 0001 1958 8658Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Daniel Fitting
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Boban Sudarevic
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany ,grid.459701.e0000 0004 0493 2358Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
| | - Wolfram G. Zoller
- grid.459701.e0000 0004 0493 2358Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
| | - Frank Puppe
- grid.8379.50000 0001 1958 8658Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Alexander Meining
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alexander Hann
- grid.411760.50000 0001 1378 7891Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
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Ishiyama M, Kudo SE, Misawa M, Mori Y, Maeda Y, Ichimasa K, Kudo T, Hayashi T, Wakamura K, Miyachi H, Ishida F, Itoh H, Oda M, Mori K. Impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video). Gastrointest Endosc 2022; 95:155-163. [PMID: 34352255 DOI: 10.1016/j.gie.2021.07.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/22/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists. METHODS This was a propensity score-matched prospective study that took place at a university hospital between July 2020 and December 2020. We recruited patients aged ≥20 years who were scheduled for colonoscopy. Patients with polyposis, inflammatory bowel disease, or incomplete colonoscopy were excluded. We used a regulatory-approved CADe system and conducted a propensity score matching-based comparison of the ADR between patients examined with and without CADe as the primary outcome. RESULTS During the study period, 2261 patients underwent colonoscopy with the CADe system or routine colonoscopy, and 172 patients were excluded in accordance with the exclusion criteria. Thirty endoscopists (9 nonexperts and 21 experts) were involved in this study. Propensity score matching was conducted using 5 factors, resulting in 1836 patients included in the analysis (918 patients in each group). The ADR was significantly higher in the CADe group than in the control group (26.4% vs 19.9%, respectively; relative risk, 1.32; 95% confidence interval, 1.12-1.57); however, there was no significant increase in the advanced neoplasia detection rate (3.7% vs 2.9%, respectively). CONCLUSIONS The use of the CADe system for colonoscopy significantly increased the ADR in a large-scale prospective study including 30 endoscopists (Clinical trial registration number: UMIN000040677.).
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Affiliation(s)
- Misaki Ishiyama
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan; Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Yasuhara Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toyoki Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Fumio Ishida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Nagoya, Japan; Information Strategy Office, Information and Communications, Nagoya University, Nagoya, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
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Chang WY, Chiu HM. Can image-enhanced endoscopy improve adenoma detection rate? Dig Endosc 2022; 34:284-296. [PMID: 34351014 DOI: 10.1111/den.14102] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/26/2021] [Accepted: 08/02/2021] [Indexed: 12/14/2022]
Abstract
An accumulating body of evidence has shown that detection and resection of pre-cancerous adenoma by colonoscopy could effectively prevent colorectal cancer (CRC) and its related mortality. Among various colonoscopy quality indicators, such as cecal intubation rate, withdrawal time, and adenoma detection rate (ADR); ADR is the most important and most closely associated with the subsequent risk of CRC. Image-enhanced endoscopy (IEE), including digital and dye-based IEE, was originally developed to discriminate neoplastic from non-neoplastic lesions but later studies have demonstrated that it can also enhance lesion detection by enhancing the contrast between the lesion and background colonic mucosa. Nevertheless, using IEE in colonoscopy for lesion detection is still not the standard way of practice in the real world. For a better understanding of current IEE modalities, this review introduces and compares the currently available IEE modalities and their efficacy in detecting adenoma from the results of randomized controlled trials or meta-analyses.
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Affiliation(s)
- Wei-Yuan Chang
- Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Han-Mo Chiu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
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Dougherty KE, Melkonian VJ, Montenegro GA. Artificial intelligence in polyp detection - where are we and where are we headed? Artif Intell Gastrointest Endosc 2021; 2:211-219. [DOI: 10.37126/aige.v2.i6.211] [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: 04/27/2021] [Revised: 07/02/2021] [Accepted: 11/18/2021] [Indexed: 02/06/2023] Open
Abstract
The goal of artificial intelligence in colonoscopy is to improve adenoma detection rate and reduce interval colorectal cancer. Artificial intelligence in polyp detection during colonoscopy has evolved tremendously over the last decade mainly due to the implementation of neural networks. Computer aided detection (CADe) utilizing neural networks allows real time detection of polyps and adenomas. Current CADe systems are built in single centers by multidisciplinary teams and have only been utilized in limited clinical research studies. We review the most recent prospective randomized controlled trials here. These randomized control trials, both non-blinded and blinded, demonstrated increase in adenoma and polyp detection rates when endoscopists used CADe systems vs standard high definition colonoscopes. Increase of polyps and adenomas detected were mainly small and sessile in nature. CADe systems were found to be safe with little added time to the overall procedure. Results are promising as more CADe have shown to have ability to increase accuracy and improve quality of colonoscopy. Overall limitations included selection bias as all trials built and utilized different CADe developed at their own institutions, non-blinded arms, and question of external validity.
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Affiliation(s)
- Kristen E Dougherty
- Department of Surgery, St. Louis University Hospital, Saint Louis, MO 63110, United States
| | - Vatche J Melkonian
- Department of Surgery, St. Louis University Hospital, Saint Louis, MO 63110, United States
| | - Grace A Montenegro
- Department of Surgery, St. Louis University Hospital, Saint Louis, MO 63110, United States
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Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021; 27:8103-8122. [PMID: 35068857 PMCID: PMC8704267 DOI: 10.3748/wjg.v27.i47.8103] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/22/2021] [Accepted: 12/08/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.
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Affiliation(s)
- Mahsa Taghiakbari
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo 0450, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama 224-8503, Japan
| | - Daniel von Renteln
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
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Mascarenhas Saraiva M, Ribeiro T, Afonso J, Andrade P, Cardoso P, Ferreira J, Cardoso H, Macedo G. Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia. MEDICINA (KAUNAS, LITHUANIA) 2021; 57:medicina57121378. [PMID: 34946323 PMCID: PMC8706550 DOI: 10.3390/medicina57121378] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/09/2021] [Accepted: 12/16/2021] [Indexed: 02/06/2023]
Abstract
Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (P.A.); (P.C.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Correspondence:
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (P.A.); (P.C.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (P.A.); (P.C.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (P.A.); (P.C.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (P.A.); (P.C.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (P.A.); (P.C.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (J.A.); (P.A.); (P.C.); (H.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
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Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience. Eur J Gastroenterol Hepatol 2021; 33:e662-e669. [PMID: 34034272 PMCID: PMC8734627 DOI: 10.1097/meg.0000000000002209] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AIM The use of artificial intelligence represents an objective approach to increase endoscopist's adenoma detection rate (ADR) and limit interoperator variability. In this study, we evaluated a newly developed deep convolutional neural network (DCNN) for automated detection of colorectal polyps ex vivo as well as in a first in-human trial. METHODS For training of the DCNN, 116 529 colonoscopy images from 278 patients with 788 different polyps were collected. A subset of 10 467 images containing 504 different polyps were manually annotated and treated as the gold standard. An independent set of 45 videos consisting of 15 534 single frames was used for ex vivo performance testing. In vivo real-time detection of colorectal polyps during routine colonoscopy by the DCNN was tested in 42 patients in a back-to-back approach. RESULTS When analyzing the test set of 15 534 single frames, the DCNN's sensitivity and specificity for polyp detection and localization within the frame was 90% and 80%, respectively, with an area under the curve of 0.92. In vivo, baseline polyp detection rate and ADR were 38% and 26% and significantly increased to 50% (P = 0.023) and 36% (P = 0.044), respectively, with the use of the DCNN. Of the 13 additionally with the DCNN detected lesions, the majority were diminutive and flat, among them three sessile serrated adenomas. CONCLUSION This newly developed DCNN enables highly sensitive automated detection of colorectal polyps both ex vivo and during first in-human clinical testing and could potentially increase the detection of colorectal polyps during colonoscopy.
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Li JW, Chia T, Fock KM, Chong KDW, Wong YJ, Ang TL. Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use. J Gastroenterol Hepatol 2021; 36:3298-3307. [PMID: 34327729 DOI: 10.1111/jgh.15642] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 05/11/2021] [Accepted: 07/22/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIM Artificial intelligence has been extensively studied to assist clinicians in polyp detection, but such systems usually require expansive processing power, making them prohibitively expensive and hindering wide adaption. The current study used a fast object detection algorithm, known as the YOLOv3 algorithm, to achieve real-time polyp detection on a laptop. In addition, we evaluated and classified the causes of false detections to further improve accuracy. METHODS The YOLOv3 algorithm was trained and validated with 6038 and 2571 polyp images, respectively. Videos from live colonoscopies in a tertiary center and those obtained from public databases were used for the training and validation sets. The algorithm was tested on 10 unseen videos from the CVC-Video ClinicDB dataset. Only bounding boxes with an intersection over union area of > 0.3 were considered positive predictions. RESULTS Polyp detection rate in our study was 100%, with the algorithm able to detect every polyp in each video. Sensitivity, specificity, and F1 score were 74.1%, 85.1%, and 83.3, respectively. The algorithm achieved a speed of 61.2 frames per second (fps) on a desktop RTX2070 GPU and 27.2 fps on a laptop GTX2060 GPU. Nearly a quarter of false negatives happened when the polyps were at the corner of an image. Image blurriness accounted for approximately 3% and 9% of false positive and false negative detections, respectively. CONCLUSION The YOLOv3 algorithm can achieve real-time poly detection with high accuracy and speed on a desktop GPU, making it low cost and accessible to most endoscopy centers worldwide.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
| | | | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
| | | | - Yu Jun Wong
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS, Singapore
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Houdeville C, Souchaud M, Leenhardt R, Beaumont H, Benamouzig R, McAlindon M, Grimbert S, Lamarque D, Makins R, Saurin JC, Histace A, Dray X. A multisystem-compatible deep learning-based algorithm for detection and characterization of angiectasias in small-bowel capsule endoscopy. A proof-of-concept study. Dig Liver Dis 2021; 53:1627-1631. [PMID: 34563469 DOI: 10.1016/j.dld.2021.08.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/26/2021] [Accepted: 08/26/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Current artificial intelligence (AI)-based solutions for capsule endoscopy (CE) interpretation are proprietary. We aimed to evaluate an AI solution trained on a specific CE system (Pillcam®, Medtronic) for the detection of angiectasias on images captured by a different proprietary system (MiroCam®, Intromedic). MATERIAL AND METHODS An advanced AI solution (Axaro®, Augmented Endoscopy), previously trained on Pillcam® small bowell images, was evaluated on independent datasets with more than 1200 Pillcam® and MiroCam® still frames (equally distributed, with or without angiectasias). Images were reviewed by experts before and after AI interpretation. RESULTS Sensitivity for the diagnosis of angiectasia was 97.4% with Pillcam® images and 96.1% with Mirocam® images, with specificity of 98.8% and 97.8%, respectively. Performances regarding the delineation of regions of interest and the characterization of angiectasias were similar in both groups (all above 95%). Processing time was significantly shorter with Mirocam® (20.7 ms) than with Pillcam® images (24.6 ms, p<0.0001), possibly related to technical differences between systems. CONCLUSION This proof-of-concept study on still images paves the way for the development of resource-sparing, "universal" CE databases and AI solutions for CE interpretation.
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Affiliation(s)
- Charles Houdeville
- Sorbonne Université, Centre d'Endoscopie Digestive, Hôpital Saint-Antoine, APHP, Paris, France
| | - Marc Souchaud
- ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | - Romain Leenhardt
- Sorbonne Université, Centre d'Endoscopie Digestive, Hôpital Saint-Antoine, APHP, Paris, France; ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | | | - Robert Benamouzig
- Department of Hepato-gastroenterology, Hôpital Avicenne, APHP, Bobigny, France
| | - Mark McAlindon
- Royal Hallamshire Hospital, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Sylvie Grimbert
- Department of Hepato-gastroenterology, Groupe hospitalier Diaconesses Croix Saint Simon, Paris, France
| | - Dominique Lamarque
- Université de Versailles St-Quentin en Yvelines, Service d'Hépato-Gastroentérologie, Hôpital Ambroise Paré, France
| | - Richard Makins
- Cheltenham General Hospital, Cheltenham, Gloucestershire, United Kingdom
| | - Jean-Christophe Saurin
- Department of Hepato-gastroenterology, E. Herriot Hospital, Hospices civils de Lyon, Lyon, France
| | - Aymeric Histace
- ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France
| | - Xavier Dray
- Sorbonne Université, Centre d'Endoscopie Digestive, Hôpital Saint-Antoine, APHP, Paris, France; ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), Cergy, France.
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Hardy NP, Cahill RA. Digital surgery for gastroenterological diseases. World J Gastroenterol 2021; 27:7240-7246. [PMID: 34876786 PMCID: PMC8611203 DOI: 10.3748/wjg.v27.i42.7240] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/27/2021] [Accepted: 10/20/2021] [Indexed: 02/06/2023] Open
Abstract
Advances in machine learning, computer vision and artificial intelligence methods, in combination with those in processing and cloud computing capability, portend the advent of true decision support during interventions in real-time and soon perhaps in automated surgical steps. Such capability, deployed alongside technology intraoperatively, is termed digital surgery and can be delivered without the need for high-end capital robotic investment. An area close to clinical usefulness right now harnesses advances in near infrared endolaparoscopy and fluorescence guidance for tissue characterisation through the use of biophysics-inspired algorithms. This represents a potential synergistic methodology for the deep learning methods currently advancing in ophthalmology, radiology, and recently gastroenterology via colonoscopy. As databanks of more general surgical videos are created, greater analytic insights can be derived across the operative spectrum of gastroenterological disease and operations (including instrumentation and operative step sequencing and recognition, followed over time by surgeon and instrument performance assessment) and linked to value-based outcomes. However, issues of legality, ethics and even morality need consideration, as do the limiting effects of monopolies, cartels and isolated data silos. Furthermore, the role of the surgeon, surgical societies and healthcare institutions in this evolving field needs active deliberation, as the default risks relegation to bystander or passive recipient. This editorial provides insight into this accelerating field by illuminating the near-future and next decade evolutionary steps towards widespread clinical integration for patient and societal benefit.
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Affiliation(s)
- Niall Philip Hardy
- UCD Centre for Precision Surgery, University College Dublin, Dublin D07 Y9AW, Ireland
| | - Ronan Ambrose Cahill
- UCD Centre for Precision Surgery, University College Dublin, Dublin D07 Y9AW, Ireland
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Low DJ, Hong Z, Khan R, Bansal R, Gimpaya N, Grover SC. Automated detection of cecal intubation with variable bowel preparation using a deep convolutional neural network. Endosc Int Open 2021; 9:E1778-E1784. [PMID: 34790545 PMCID: PMC8589561 DOI: 10.1055/a-1546-8266] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 06/04/2021] [Indexed: 12/15/2022] Open
Abstract
Background and study aims Colonoscopy completion reduces post-colonoscopy colorectal cancer. As a result, there have been attempts at implementing artificial intelligence to automate the detection of the appendiceal orifice (AO) for quality assurance. However, the utilization of these algorithms has not been demonstrated in suboptimal conditions, including variable bowel preparation. We present an automated computer-assisted method using a deep convolutional neural network to detect the AO irrespective of bowel preparation. Methods A total of 13,222 images (6,663 AO and 1,322 non-AO) were extracted from 35 colonoscopy videos recorded between 2015 and 2018. The images were labelled with Boston Bowel Preparation Scale scores. A total of 11,900 images were used for training/validation and 1,322 for testing. We developed a convolutional neural network (CNN) with a DenseNet architecture pre-trained on ImageNet as a feature extractor on our data and trained a classifier uniquely tailored for identification of AO and non-AO images using binary cross entropy loss. Results The deep convolutional neural network was able to correctly classify the AO and non-AO images with an accuracy of 94 %. The area under the receiver operating curve of this neural network was 0.98. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 0.96, 0.92, 0.92 and 0.96, respectively. AO detection was > 95 % regardless of BBPS scores, while non-AO detection improved from BBPS 1 score (83.95 %) to BBPS 3 score (98.28 %). Conclusions A deep convolutional neural network was created demonstrating excellent discrimination between AO from non-AO images despite variable bowel preparation. This algorithm will require further testing to ascertain its effectiveness in real-time colonoscopy.
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Affiliation(s)
| | | | - Rishad Khan
- St. Michael’s Hospital, University of Toronto
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