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Ishibashi F, Suzuki S. Practical utility of linked color imaging in colonoscopy: Updated literature review. Dig Endosc 2024. [PMID: 39253814 DOI: 10.1111/den.14915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 08/13/2024] [Indexed: 09/11/2024]
Abstract
The remarkable recent developments in image-enhanced endoscopy (IEE) have significantly contributed to the advancement of diagnostic techniques. Linked color imaging (LCI) is an IEE technique in which color differences are expanded by processing image data to enhance short-wavelength narrow-band light. This feature of LCI causes reddish areas to appear redder and whitish areas to appear whiter. Because most colorectal lesions, such as neoplastic and inflammatory lesions, have a reddish tone, LCI is an effective tool for identifying colorectal lesions by clarifying the redder areas and distinguishing them from the surrounding normal mucosa. To date, eight randomized controlled trials have been conducted to evaluate the effectiveness of LCI in identifying colorectal adenomatous lesions. The results of a meta-analysis integrating these studies demonstrated that LCI was superior to white-light endoscopy for detecting colorectal adenomatous lesions. LCI also improves the detection of serrated lesions by enhancing their whiteness. Furthermore, accumulating evidence suggests that LCI is superior to white-light endoscopy for the diagnosis of the colonic mucosa in patients with ulcerative colitis. In this review, based on a comprehensive search of the current literature since the implementation of LCI, the utility of LCI in the detection and diagnosis of colorectal lesions is discussed. Additionally, the latest data, including attempts to combine artificial intelligence and LCI, are presented.
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Affiliation(s)
- Fumiaki Ishibashi
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Chiba, Japan
| | - Sho Suzuki
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Chiba, Japan
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Sugino S, Yoshida N, Guo Z, Zhang R, Inoue K, Hirose R, Dohi O, Itoh Y, Nemoto D, Togashi K, Yamamoto H, Zhu X. Non-polypoid Colorectal Lesions Detection and False Positive Detection by Artificial Intelligence under Blue Laser Imaging and Linked Color Imaging. J Anus Rectum Colon 2024; 8:212-220. [PMID: 39086882 PMCID: PMC11286363 DOI: 10.23922/jarc.2023-070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/28/2024] [Indexed: 08/02/2024] Open
Abstract
Objectives Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS). Methods The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees. Results The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p<0.01), BLI in AI (IEE) (78%: p=0.14), and LCI in trainees (74%: p<0.01). The sensitivity for LCI in AI (IEE) (83%) was significantly higher than that in AI (HQ) (78%: p<0.01). The FPR for LCI (6.5%) in AI (IEE) were significantly lower than that in AI (HQ) (17.3%: p<0.01). Conclusions AI finetuned by appropriate LS detected non-reddish and non-polypoid polyps under LCI.
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Affiliation(s)
- Satoshi Sugino
- Department of Gastroenterology, Asahi University Hospital, Gifu, Japan
| | - Naohisa Yoshida
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Zhe Guo
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Ruiyao Zhang
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
| | - Ken Inoue
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ryohei Hirose
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Osamu Dohi
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yoshito Itoh
- Department of Gastroenterology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Daiki Nemoto
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Kazutomo Togashi
- Department of Coloproctology, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - Hironori Yamamoto
- Department of Gastroenterology, Jichi Medical University, Tochigi, Japan
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Fukushima, Japan
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Desai M, Ausk K, Brannan D, Chhabra R, Chan W, Chiorean M, Gross SA, Girotra M, Haber G, Hogan RB, Jacob B, Jonnalagadda S, Iles-Shih L, Kumar N, Law J, Lee L, Lin O, Mizrahi M, Pacheco P, Parasa S, Phan J, Reeves V, Sethi A, Snell D, Underwood J, Venu N, Visrodia K, Wong A, Winn J, Wright CH, Sharma P. Use of a Novel Artificial Intelligence System Leads to the Detection of Significantly Higher Number of Adenomas During Screening and Surveillance Colonoscopy: Results From a Large, Prospective, US Multicenter, Randomized Clinical Trial. Am J Gastroenterol 2024; 119:1383-1391. [PMID: 38235741 DOI: 10.14309/ajg.0000000000002664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/14/2023] [Indexed: 01/19/2024]
Abstract
INTRODUCTION Adenoma per colonoscopy (APC) has recently been proposed as a quality measure for colonoscopy. We evaluated the impact of a novel artificial intelligence (AI) system, compared with standard high-definition colonoscopy, for APC measurement. METHODS This was a US-based, multicenter, prospective randomized trial examining a novel AI detection system (EW10-EC02) that enables a real-time colorectal polyp detection enabled with the colonoscope (CAD-EYE). Eligible average-risk subjects (45 years or older) undergoing screening or surveillance colonoscopy were randomized to undergo either CAD-EYE-assisted colonoscopy (CAC) or conventional colonoscopy (CC). Modified intention-to-treat analysis was performed for all patients who completed colonoscopy with the primary outcome of APC. Secondary outcomes included positive predictive value (total number of adenomas divided by total polyps removed) and adenoma detection rate. RESULTS In modified intention-to-treat analysis, of 1,031 subjects (age: 59.1 ± 9.8 years; 49.9% male), 510 underwent CAC vs 523 underwent CC with no significant differences in age, gender, ethnicity, or colonoscopy indication between the 2 groups. CAC led to a significantly higher APC compared with CC: 0.99 ± 1.6 vs 0.85 ± 1.5, P = 0.02, incidence rate ratio 1.17 (1.03-1.33, P = 0.02) with no significant difference in the withdrawal time: 11.28 ± 4.59 minutes vs 10.8 ± 4.81 minutes; P = 0.11 between the 2 groups. Difference in positive predictive value of a polyp being an adenoma among CAC and CC was less than 10% threshold established: 48.6% vs 54%, 95% CI -9.56% to -1.48%. There were no significant differences in adenoma detection rate (46.9% vs 42.8%), advanced adenoma (6.5% vs 6.3%), sessile serrated lesion detection rate (12.9% vs 10.1%), and polyp detection rate (63.9% vs 59.3%) between the 2 groups. There was a higher polyp per colonoscopy with CAC compared with CC: 1.68 ± 2.1 vs 1.33 ± 1.8 (incidence rate ratio 1.27; 1.15-1.4; P < 0.01). DISCUSSION Use of a novel AI detection system showed to a significantly higher number of adenomas per colonoscopy compared with conventional high-definition colonoscopy without any increase in colonoscopy withdrawal time, thus supporting the use of AI-assisted colonoscopy to improve colonoscopy quality ( ClinicalTrials.gov NCT04979962).
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Affiliation(s)
- Madhav Desai
- Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, Missouri, USA
| | - Karlee Ausk
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Donald Brannan
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Rajiv Chhabra
- Department of Gastroenterology, Saint Luke's Hospital of Kansas City, Kansas City, Missouri, USA
| | - Walter Chan
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Michael Chiorean
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Seth A Gross
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - Mohit Girotra
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Gregory Haber
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - Reed B Hogan
- GI Associates and Endoscopy Center, Jackson, Mississippi, USA
| | - Bobby Jacob
- Gastroenterology, Largo Medical Center, Largo, Florida, USA
| | - Sreeni Jonnalagadda
- Department of Gastroenterology, Saint Luke's Hospital of Kansas City, Kansas City, Missouri, USA
| | - Lulu Iles-Shih
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Navin Kumar
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Joanna Law
- Gastroenterology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Linda Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Otto Lin
- Gastroenterology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Meir Mizrahi
- Gastroenterology, Largo Medical Center, Largo, Florida, USA
| | - Paulo Pacheco
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - Sravanthi Parasa
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Jennifer Phan
- Departement of Gastroenterology, Keck Medicine University of Southern California, Los Angeles, California, USA
| | - Vonda Reeves
- GI Associates and Endoscopy Center, Jackson, Mississippi, USA
| | - Amrita Sethi
- Department of Gastroenterology, Columbia University Irving Medical Center, New York, New York, USA
| | - David Snell
- Gastroenterology, New York University Langone Health, New York, New York, USA
| | - James Underwood
- GI Associates and Endoscopy Center, Jackson, Mississippi, USA
| | - Nanda Venu
- Gastroenterology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Kavel Visrodia
- Department of Gastroenterology, Columbia University Irving Medical Center, New York, New York, USA
| | - Alina Wong
- Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA
| | - Jessica Winn
- Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, Missouri, USA
| | | | - Prateek Sharma
- Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, Missouri, USA
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Kobayashi R, Yoshida N, Tomita Y, Hashimoto H, Inoue K, Hirose R, Dohi O, Inada Y, Murakami T, Morimoto Y, Zhu X, Itoh Y. Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets. J Anus Rectum Colon 2024; 8:61-69. [PMID: 38689788 PMCID: PMC11056537 DOI: 10.23922/jarc.2023-041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/22/2023] [Indexed: 05/02/2024] Open
Abstract
Objectives Detailed superiority of CAD EYE (Fujifilm, Tokyo, Japan), an artificial intelligence for polyp detection/diagnosis, compared to endoscopists is not well examined. We examined endoscopist's ability using movie sets of colorectal lesions which were detected and diagnosed by CAD EYE accurately. Methods Consecutive lesions of ≤10 mm were examined live by CAD EYE from March-June 2022 in our institution. Short unique movie sets of each lesion with and without CAD EYE were recorded simultaneously using two recorders for detection under white light imaging (WLI) and linked color imaging (LCI) and diagnosis under blue laser/light imaging (BLI). Excluding inappropriate movies, 100 lesions detected and diagnosed with CAD EYE accurately were evaluated. Movies without CAD EYE were evaluated first by three trainees and three experts. Subsequently, movies with CAD EYE were examined. The rates of accurate detection and diagnosis were evaluated for both movie sets. Results Among 100 lesions (mean size: 4.7±2.6 mm; 67 neoplastic/33 hyperplastic), mean accurate detection rates of movies without or with CAD EYE were 78.7%/96.7% under WLI (p<0.01) and 91.3%/97.3% under LCI (p<0.01) for trainees and 85.3%/99.0% under WLI (p<0.01) and 92.6%/99.3% under LCI (p<0.01) for experts. Mean accurate diagnosis rates of movies without or with CAD EYE for BLI were 85.3%/100% for trainees (p<0.01) and 92.3%/100% for experts (p<0.01), respectively. The significant risk factors of not-detected lesions for trainees were right-sided, hyperplastic, not-reddish, in the corner, halation, and inadequate bowel preparation. Conclusions Unique movie sets with and without CAD EYE could suggest it's efficacy for lesion detection/diagnosis.
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Affiliation(s)
- Reo Kobayashi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Naohisa Yoshida
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yuri Tomita
- Department of Gastroenterology, Kosekai Takeda Hosptal, Kyoto, Japan
| | - Hikaru Hashimoto
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ken Inoue
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ryohei Hirose
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Osamu Dohi
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yutaka Inada
- Department of Gastroenterology, Kyoto First Red Cross Hospital, Kyoto, Japan
| | - Takaaki Murakami
- Department of Gastroenterology, Aiseikai Yamashina Hospital, Kyoto, Japan
| | - Yasutaka Morimoto
- Department of Gastroenterology, Kyoto Saiseikai Hospital, Kyoto, Japan
| | - Xin Zhu
- Graduate School of Computer Science and Engineering, The University of Aizu, Fukushima, Japan
| | - Yoshito Itoh
- Department of Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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van der Zander QEW, Schreuder RM, Thijssen A, Kusters CHJ, Dehghani N, Scheeve T, Winkens B, van der Ende - van Loon MCM, de With PHN, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems. Artif Intell Gastrointest Endosc 2024; 5:90574. [DOI: 10.37126/aige.v5.i1.90574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/11/2024] [Accepted: 02/02/2024] [Indexed: 02/20/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.
AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.
METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.
RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.
CONCLUSION Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.
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Affiliation(s)
- Quirine Eunice Wennie van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Ramon M Schreuder
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
| | - Ayla Thijssen
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
| | - Carolus H J Kusters
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Nikoo Dehghani
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Thom Scheeve
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, Postbus 616, 6200 MD Maastricht, Netherlands
- School for Public Health and Primary Care, Maastricht University, Maastricht 6200 MD, Netherlands
| | | | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, Netherlands
| | - Ad A M Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht 6202 AZ, Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht 6200 MD, Netherlands
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven 5602 ZA, Netherlands
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Yamaguchi D, Shimoda R, Miyahara K, Yukimoto T, Sakata Y, Takamori A, Mizuta Y, Fujimura Y, Inoue S, Tomonaga M, Ogino Y, Eguchi K, Ikeda K, Tanaka Y, Takedomi H, Hidaka H, Akutagawa T, Tsuruoka N, Noda T, Tsunada S, Esaki M. Impact of an artificial intelligence-aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: Prospective, randomized, multicenter study. Dig Endosc 2024; 36:40-48. [PMID: 37079002 DOI: 10.1111/den.14573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/19/2023] [Indexed: 04/21/2023]
Abstract
OBJECTIVE This study was performed to evaluate whether the use of CAD EYE (Fujifilm, Tokyo, Japan) for colonoscopy improves colonoscopy quality in gastroenterology trainees. METHODS The patients in this multicenter randomized controlled trial were divided into Group A (observation using CAD EYE) and Group B (standard observation). Six trainees performed colonoscopies using a back-to-back method in pairs with gastroenterology experts. The primary end-point was the trainees' adenoma detection rate (ADR), and the secondary end-points were the trainees' adenoma miss rate (AMR) and Assessment of Competency in Endoscopy (ACE) tool scores. Each trainee's learning curve was evaluated using a cumulative sum (CUSUM) control chart. RESULTS We analyzed data for 231 patients (Group A, n = 113; Group B, n = 118). The ADR was not significantly different between the two groups. Group A had a significantly lower AMR (25.6% vs. 38.6%, P = 0.033) and number of missed adenomas per patient (0.5 vs. 0.9, P = 0.004) than Group B. Group A also had significantly higher ACE tool scores for pathology identification (2.26 vs. 2.07, P = 0.030) and interpretation and identification of pathology location (2.18 vs. 2.00, P = 0.038). For the CUSUM learning curve, Group A showed a trend toward a lower number of cases of missed multiple adenomas by the six trainees. CONCLUSION CAD EYE did not improve ADR but decreased the AMR and improved the ability to accurately locate and identify colorectal adenomas. CAD EYE can be assumed to be beneficial for improving colonoscopy quality in gastroenterology trainees. TRIAL REGISTRATION University Hospital Medical Information Network Clinical Trials Registry (UMIN000044031).
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Affiliation(s)
- Daisuke Yamaguchi
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Ryo Shimoda
- Department of Endoscopic Diagnostics and Therapeutics, Saga University Hospital, Saga, Japan
| | - Koichi Miyahara
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Takahiro Yukimoto
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Yasuhisa Sakata
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Ayako Takamori
- Clinical Research Center, Saga University Hospital, Saga, Japan
| | - Yumi Mizuta
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Yutaro Fujimura
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Suma Inoue
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Michito Tomonaga
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Yuya Ogino
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Kohei Eguchi
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Kei Ikeda
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Yuichiro Tanaka
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Hironobu Takedomi
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Hidenori Hidaka
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Takashi Akutagawa
- Department of Endoscopic Diagnostics and Therapeutics, Saga University Hospital, Saga, Japan
| | - Nanae Tsuruoka
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Takahiro Noda
- Department of Internal Medicine, Karatsu Red Cross Hospital, Saga, Japan
| | - Seiji Tsunada
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | - Motohiro Esaki
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
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Ding M, Yan J, Chao G, Zhang S. Application of artificial intelligence in colorectal cancer screening by colonoscopy: Future prospects (Review). Oncol Rep 2023; 50:199. [PMID: 37772392 DOI: 10.3892/or.2023.8636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/07/2023] [Indexed: 09/30/2023] Open
Abstract
Colorectal cancer (CRC) has become a severe global health concern, with the third‑high incidence and second‑high mortality rate of all cancers. The burden of CRC is expected to surge to 60% by 2030. Fortunately, effective early evidence‑based screening could significantly reduce the incidence and mortality of CRC. Colonoscopy is the core screening method for CRC with high popularity and accuracy. Yet, the accuracy of colonoscopy in CRC screening is related to the experience and state of operating physicians. It is challenging to maintain the high CRC diagnostic rate of colonoscopy. Artificial intelligence (AI)‑assisted colonoscopy will compensate for the above shortcomings and improve the accuracy, efficiency, and quality of colonoscopy screening. The unique advantages of AI, such as the continuous advancement of high‑performance computing capabilities and innovative deep‑learning architectures, which hugely impact the control of colorectal cancer morbidity and mortality expectancy, highlight its role in colonoscopy screening.
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Affiliation(s)
- Menglu Ding
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Junbin Yan
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
| | - Guanqun Chao
- Department of General Practice, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, P.R. China
| | - Shuo Zhang
- The Second Affiliated Hospital of Zhejiang Chinese Medical University (The Xin Hua Hospital of Zhejiang Province), Hangzhou, Zhejiang 310000, P.R. China
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8
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Young E, Edwards L, Singh R. The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization. Cancers (Basel) 2023; 15:5126. [PMID: 37958301 PMCID: PMC10647850 DOI: 10.3390/cancers15215126] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.
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Affiliation(s)
- Edward Young
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
| | - Louisa Edwards
- Faculty of Health and Medical Sciences, University of Adelaide, Queen Elizabeth Hospital, Port Rd, Woodville South, SA 5011, Australia
| | - Rajvinder Singh
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
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Nagai M, Suzuki S, Minato Y, Ishibashi F, Mochida K, Ohata K, Morishita T. Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials. Clin Endosc 2023; 56:553-562. [PMID: 37491990 PMCID: PMC10565430 DOI: 10.5946/ce.2023.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/31/2023] [Accepted: 04/09/2023] [Indexed: 07/27/2023] Open
Abstract
Colonoscopy plays an important role in reducing the incidence and mortality of colorectal cancer by detecting adenomas and other precancerous lesions. Image-enhanced endoscopy (IEE) increases lesion visibility by enhancing the microstructure, blood vessels, and mucosal surface color, resulting in the detection of colorectal lesions. In recent years, various IEE techniques have been used in clinical practice, each with its unique characteristics. Numerous studies have reported the effectiveness of IEE in the detection of colorectal lesions. IEEs can be divided into two broad categories according to the nature of the image: images constructed using narrowband wavelength light, such as narrowband imaging and blue laser imaging/blue light imaging, or color images based on white light, such as linked color imaging, texture and color enhancement imaging, and i-scan. Conversely, artificial intelligence (AI) systems, such as computer-aided diagnosis systems, have recently been developed to assist endoscopists in detecting colorectal lesions during colonoscopy. To better understand the features of each IEE, this review presents the effectiveness of each type of IEE and their combination with AI for colorectal lesion detection by referencing the latest research data.
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Affiliation(s)
- Mizuki Nagai
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Chiba, Japan
| | - Sho Suzuki
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Chiba, Japan
| | - Yohei Minato
- Department of Gastrointestinal Endoscopy, NTT Medical Center Tokyo, Tokyo, Japan
| | - Fumiaki Ishibashi
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Chiba, Japan
| | - Kentaro Mochida
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Chiba, Japan
| | - Ken Ohata
- Department of Gastrointestinal Endoscopy, NTT Medical Center Tokyo, Tokyo, Japan
| | - Tetsuo Morishita
- Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Chiba, Japan
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Baumer S, Streicher K, Alqahtani SA, Brookman-Amissah D, Brunner M, Federle C, Muehlenberg K, Pfeifer L, Salzberger A, Schorr W, Zustin J, Pech O. Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center. Endosc Int Open 2023; 11:E818-E828. [PMID: 37727511 PMCID: PMC10506867 DOI: 10.1055/a-2096-2960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/08/2023] [Indexed: 09/21/2023] Open
Abstract
Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. The primary outcome was accuracy of the AI classifying the polyps into "neoplastic" and "non-neoplastic." The secondary outcome was accuracy of the classification by the endoscopists. Sessile serrated lesions were classified as neoplastic. Results We included 156 patients (mean age 65; 57 women) with 262 polyps ≤10 mm. Eighty-four were hyperplastic polyps (32.1%), 158 adenomas (60.3%), seven sessile serrated lesions (2.7%) and 13 other entities (normal/inflammatory colonmucosa, lymphoidic polyp) (4.9%) on histological diagnosis. Sensitivity, specificity and accuracy of AI were 89.70% (95% confidence interval [CI]: 84.02%-93.88%), 75.26% (95% CI: 65.46%-83.46%) and 84.35% (95% CI:79.38%-88.53%), respectively. Sensitivity, specificity and accuracy for less experienced endoscopists (2-5 years of endoscopy) were 95.56% (95% CI: 84.85%-99.46%), 61.54% (95% CI: 40.57%-79.77%) and 83.10% (95% CI: 72.34%-90.95%) and for experienced endoscopists 90.83% (95% CI: 84.19%-95.33%), 71.83% (95% CI: 59.90%-81.87%) and 83.77% (95% CI: 77.76%-88.70%), respectively. Conclusion Accuracy for polyp characterization by a new commercially available AI system is high, but does not fulfill the criteria for a "resect-and-discard" strategy.
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Affiliation(s)
- Sebastian Baumer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Kilian Streicher
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Saleh A. Alqahtani
- Department of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, United States
- Liver Transplant Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Dominic Brookman-Amissah
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Monika Brunner
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Christoph Federle
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Klaus Muehlenberg
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Lukas Pfeifer
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Andrea Salzberger
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Wolfgang Schorr
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
| | - Jozef Zustin
- Private Practice, Histopathology Service Private Practice, Regensburg, Germany
- Gerhard-Domagk-Institute of Pathology, Universitätsklinikum Münster, Munster, Germany
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
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Liao WC, Mukundan A, Sadiaza C, Tsao YM, Huang CW, Wang HC. Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging. BIOMEDICAL OPTICS EXPRESS 2023; 14:4383-4405. [PMID: 37799695 PMCID: PMC10549751 DOI: 10.1364/boe.492635] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 10/07/2023]
Abstract
One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.
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Affiliation(s)
- Wei-Chih Liao
- Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
- Graduate Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Cleorita Sadiaza
- Department of Mechanical Engineering, Far Eastern University, P. Paredes St., Sampaloc, Manila, 1015, Philippines
| | - Yu-Ming Tsao
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung County 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
- Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 2, Minsheng Road, Dalin, Chiayi, 62247, Taiwan
- Director of Technology Development, Hitspectra Intelligent Technology Co., Ltd., 4F., No. 2, Fuxing 4th Rd., Qianzhen Dist., Kaohsiung City 80661, Taiwan
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Dos Santos CEO, Malaman D, Arciniegas Sanmartin ID, Leão ABS, Leão GS, Pereira-Lima JC. Performance of artificial intelligence in the characterization of colorectal lesions. Saudi J Gastroenterol 2023; 29:219-224. [PMID: 37203122 PMCID: PMC10445495 DOI: 10.4103/sjg.sjg_316_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/10/2023] [Indexed: 05/20/2023] Open
Abstract
Background Image-enhanced endoscopy (IEE) has been used in the differentiation between neoplastic and non-neoplastic colorectal lesions through microvasculature analysis. This study aimed to evaluate the computer-aided diagnosis (CADx) mode of the CAD EYE system for the optical diagnosis of colorectal lesions and compare it with the performance of an expert, in addition to evaluating the computer-aided detection (CADe) mode in terms of polyp detection rate (PDR) and adenoma detection rate (ADR). Methods A prospective study was conducted to evaluate the performance of CAD EYE using blue light imaging (BLI), dichotomizing lesions into hyperplastic and neoplastic, and of an expert based on the Japan Narrow-Band Imaging Expert Team (JNET) classification for the characterization of lesions. After white light imaging (WLI) diagnosis, magnification was used on all lesions, which were removed and examined histologically. Diagnostic criteria were evaluated, and PDR and ADR were calculated. Results A total of 110 lesions (80 (72.7%) dysplastic lesions and 30 (27.3%) nondysplastic lesions) were evaluated in 52 patients, with a mean lesion size of 4.3 mm. Artificial intelligence (AI) analysis showed 81.8% accuracy, 76.3% sensitivity, 96.7% specificity, 98.5% positive predictive value (PPV), and 60.4% negative predictive value (NPV). The kappa value was 0.61, and the area under the receiver operating characteristic curve (AUC) was 0.87. Expert analysis showed 93.6% accuracy, 92.5% sensitivity, 96.7% specificity, 98.7% PPV, and 82.9% NPV. The kappa value was 0.85, and the AUC was 0.95. Overall, PDR was 67.6% and ADR was 45.9%. Conclusions The CADx mode showed good accuracy in characterizing colorectal lesions, but the expert assessment was superior in almost all diagnostic criteria. PDR and ADR were high.
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Affiliation(s)
- Carlos E. O. Dos Santos
- Department of Endoscopy, Santa Casa de Caridade Hospital, Bagé, RS, Brazil
- Department of Endoscopy, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Daniele Malaman
- Department of Endoscopy, Santa Casa de Caridade Hospital, Bagé, RS, Brazil
| | | | - Ari B. S. Leão
- Department of Endoscopy, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Gabriel S. Leão
- Department of Endoscopy, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Júlio C. Pereira-Lima
- Department of Gastroenterology and Endoscopy, Santa Casa Hospital, Porto Alegre, RS, Brazil
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Mozaffari J, Amirkhani A, Shokouhi SB. A survey on deep learning models for detection of COVID-19. Neural Comput Appl 2023; 35:1-29. [PMID: 37362568 PMCID: PMC10224665 DOI: 10.1007/s00521-023-08683-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08683-x.
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Affiliation(s)
- Javad Mozaffari
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Abdollah Amirkhani
- School of Automotive Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Shahriar B. Shokouhi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
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Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study. Endoscopy 2023; 55:14-22. [PMID: 35562098 DOI: 10.1055/a-1852-0330] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Optical diagnosis of colonic polyps is poorly reproducible outside of high volume referral centers. The present study aimed to assess whether real-time artificial intelligence (AI)-assisted optical diagnosis is accurate enough to implement the leave-in-situ strategy for diminutive (≤ 5 mm) rectosigmoid polyps (DRSPs). METHODS Consecutive colonoscopy outpatients with ≥ 1 DRSP were included. DRSPs were categorized as adenomas or nonadenomas by the endoscopists, who had differing expertise in optical diagnosis, with the assistance of a real-time AI system (CAD-EYE). The primary end point was ≥ 90 % negative predictive value (NPV) for adenomatous histology in high confidence AI-assisted optical diagnosis of DRSPs (Preservation and Incorporation of Valuable endoscopic Innovations [PIVI-1] threshold), with histopathology as the reference standard. The agreement between optical- and histology-based post-polypectomy surveillance intervals (≥ 90 %; PIVI-2 threshold) was also calculated according to European Society of Gastrointestinal Endoscopy (ESGE) and United States Multi-Society Task Force (USMSTF) guidelines. RESULTS Overall 596 DRSPs were retrieved for histology in 389 patients; an AI-assisted high confidence optical diagnosis was made in 92.3 %. The NPV of AI-assisted optical diagnosis for DRSPs (PIVI-1) was 91.0 % (95 %CI 87.1 %-93.9 %). The PIVI-2 threshold was met with 97.4 % (95 %CI 95.7 %-98.9 %) and 92.6 % (95 %CI 90.0 %-95.2 %) of patients according to ESGE and USMSTF, respectively. AI-assisted optical diagnosis accuracy was significantly lower for nonexperts (82.3 %, 95 %CI 76.4 %-87.3 %) than for experts (91.9 %, 95 %CI 88.5 %-94.5 %); however, nonexperts quickly approached the performance levels of experts over time. CONCLUSION AI-assisted optical diagnosis matches the required PIVI thresholds. This does not however offset the need for endoscopists' high level confidence and expertise. The AI system seems to be useful, especially for nonexperts.
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Koh FH, Ladlad J, Teo EK, Lin CL, Foo FJ. Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore. Surg Endosc 2023; 37:165-171. [PMID: 35882667 PMCID: PMC9321269 DOI: 10.1007/s00464-022-09470-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/10/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Colonoscopy is a mainstay to detect premalignant neoplastic lesions in the colon. Real-time Artificial Intelligence (AI)-aided colonoscopy purportedly improves the polyp detection rate, especially for small flat lesions. The aim of this study is to evaluate the performance of real-time AI-aided colonoscopy in the detection of colonic polyps. METHODS A prospective single institution cohort study was conducted in Singapore. All real-time AI-aided colonoscopies, regardless of indication, performed by specialist-grade endoscopists were anonymously recorded from July to September 2021 and reviewed by 2 independent authors (FHK, JL). Sustained detection of an area by the program was regarded as a "hit". Histology for the polypectomies were reviewed to determine adenoma detection rate (ADR). Individual endoscopist's performance with AI were compared against their baseline performance without AI endoscopy. RESULTS A total of 24 (82.8%) endoscopists participated with 18 (62.1%) performing ≥ 5 AI-aided colonoscopies. Of the 18, 72.2% (n = 13) were general surgeons. During that 3-months period, 487 "hits" encountered in 298 colonoscopies. Polypectomies were performed for 51.3% and 68.4% of these polypectomies were adenomas on histology. The post-intervention median ADR was 30.4% was higher than the median baseline polypectomy rate of 24.3% (p = 0.02). Of the adenomas excised, 14 (5.6%) were sessile serrated adenomas. Of those who performed ≥ 5 AI-aided colonoscopies, 13 (72.2%) had an improvement of ADR compared to their polypectomy rate before the introduction of AI, of which 2 of them had significant improvement. CONCLUSIONS Real-time AI-aided colonoscopy have the potential to improved ADR even for experienced endoscopists and would therefore, improve the quality of colonoscopy.
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Affiliation(s)
- Frederick H. Koh
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | - Jasmine Ladlad
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | | | - Eng-Kiong Teo
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Cui-Li Lin
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Fung-Joon Foo
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore ,grid.508163.90000 0004 7665 4668Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, Singapore, Singapore
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Young EJ, Rajandran A, Philpott HL, Sathananthan D, Hoile SF, Singh R. Mucosal imaging in colon polyps: New advances and what the future may hold. World J Gastroenterol 2022; 28:6632-6661. [PMID: 36620337 PMCID: PMC9813932 DOI: 10.3748/wjg.v28.i47.6632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/23/2022] [Accepted: 11/23/2022] [Indexed: 12/19/2022] Open
Abstract
An expanding range of advanced mucosal imaging technologies have been developed with the goal of improving the detection and characterization of lesions in the gastrointestinal tract. Many technologies have targeted colorectal neoplasia given the potential for intervention prior to the development of invasive cancer in the setting of widespread surveillance programs. Improvement in adenoma detection reduces miss rates and prevents interval cancer development. Advanced imaging technologies aim to enhance detection without significantly increasing procedural time. Accurate polyp characterisation guides resection techniques for larger polyps, as well as providing the platform for the “resect and discard” and “do not resect” strategies for small and diminutive polyps. This review aims to collate and summarise the evidence regarding these technologies to guide colonoscopic practice in both interventional and non-interventional endoscopists.
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Affiliation(s)
- Edward John Young
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Arvinf Rajandran
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
| | - Hamish Lachlan Philpott
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Dharshan Sathananthan
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Sophie Fenella Hoile
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
| | - Rajvinder Singh
- Department of Gastroenterology, Lyell McEwin Hospital, Northern Adelaide Local Health Network, Elizabeth Vale 5031, South Australia, Australia
- Faculty of Health and Medical Sciences, University of Adelaide, Adelaide 5000, South Australia, Australia
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Fitting D, Krenzer A, Troya J, Banck M, Sudarevic B, Brand M, Böck W, Zoller WG, Rösch T, Puppe F, Meining A, Hann A. A video based benchmark data set (ENDOTEST) to evaluate computer-aided polyp detection systems. Scand J Gastroenterol 2022; 57:1397-1403. [PMID: 35701020 DOI: 10.1080/00365521.2022.2085059] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND AIMS Computer-aided polyp detection (CADe) may become a standard for polyp detection during colonoscopy. Several systems are already commercially available. We report on a video-based benchmark technique for the first preclinical assessment of such systems before comparative randomized trials are to be undertaken. Additionally, we compare a commercially available CADe system with our newly developed one. METHODS ENDOTEST consisted in the combination of two datasets. The validation dataset contained 48 video-snippets with 22,856 manually annotated images of which 53.2% contained polyps. The performance dataset contained 10 full-length screening colonoscopies with 230,898 manually annotated images of which 15.8% contained a polyp. Assessment parameters were accuracy for polyp detection and time delay to first polyp detection after polyp appearance (FDT). Two CADe systems were assessed: a commercial CADe system (GI-Genius, Medtronic), and a self-developed new system (ENDOMIND). The latter being a convolutional neuronal network trained on 194,983 manually labeled images extracted from colonoscopy videos recorded in mainly six different gastroenterologic practices. RESULTS On the ENDOTEST, both CADe systems detected all polyps in at least one image. The per-frame sensitivity and specificity in full colonoscopies was 48.1% and 93.7%, respectively for GI-Genius; and 54% and 92.7%, respectively for ENDOMIND. Median FDT of ENDOMIND with 217 ms (Inter-Quartile Range(IQR)8-1533) was significantly faster than GI-Genius with 1050 ms (IQR 358-2767, p = 0.003). CONCLUSIONS Our benchmark ENDOTEST may be helpful for preclinical testing of new CADe devices. There seems to be a correlation between a shorter FDT with a higher sensitivity and a lower specificity for polyp detection.
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Affiliation(s)
- Daniel Fitting
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany
| | - Adrian Krenzer
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany.,Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany
| | - Michael Banck
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany.,Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany
| | - Boban Sudarevic
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany.,Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
| | - Markus Brand
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany
| | | | - Wolfram G Zoller
- Department of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany
| | - Thomas Rösch
- Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Frank Puppe
- Artificial Intelligence and Knowledge Systems, Institute for Computer Science, Julius-Maximilians-Universität, Würzburg, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Internal Medicine II, University Hospital Wuerzburg, Würzburg, Germany
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Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy. J Clin Med 2022; 11:jcm11102923. [PMID: 35629049 PMCID: PMC9143862 DOI: 10.3390/jcm11102923] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 12/19/2022] Open
Abstract
The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.
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AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC.
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