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Li L, Yang L, Zhang B, Yan G, Bao Y, Zhu R, Li S, Wang H, Chen M, Jin C, Chen Y, Yu C. Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm. Clin Res Hepatol Gastroenterol 2024; 48:102334. [PMID: 38582328 DOI: 10.1016/j.clinre.2024.102334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 03/20/2024] [Accepted: 04/04/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE. METHODS A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively. RESULTS The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001). CONCLUSIONS CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.
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Affiliation(s)
- Lan Li
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China.
| | - Liping Yang
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Bingling Zhang
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Guofei Yan
- Zhejiang Center for Medical Device Evaluation, Hangzhou, China
| | - Yaqing Bao
- GBA Center for Medical Device Evaluation and Inspection, National Medical Products Administration, Shenzhen, China
| | - Renke Zhu
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Shengjie Li
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Huogen Wang
- Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Ming Chen
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Chaohui Jin
- Zhejiang Herymed Technology Co., Ltd, Hangzhou, China; Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Yishu Chen
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
| | - Chaohui Yu
- Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang 310003, China
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Choi KS, Park D, Kim JS, Cheung DY, Lee BI, Cho YS, Kim JI, Lee S, Lee HH. Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield. Dig Endosc 2024; 36:437-445. [PMID: 37612137 DOI: 10.1111/den.14670] [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: 04/26/2023] [Accepted: 08/20/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model. METHODS Clinical data of patients who received SBCE for suspected small-bowel bleeding at two academic hospitals between February 2018 and July 2020 were retrospectively collected. All SBCE videos read as negative were reanalyzed with the CNN algorithm developed in our previous study. Meaningful findings such as angioectasias and ulcers were finally decided after reviewing CNN-selected images by two gastroenterologists. RESULTS Among 202 SBCE videos, 103 (51.0%) were read as negative by humans. Meaningful findings were detected in 63 (61.2%) of these 103 videos after reanalyzing them with the CNN model. There were 79 red spots or angioectasias in 40 videos and 66 erosions or ulcers in 35 videos. After reanalysis, the diagnosis was changed for 10 (10.3%) patients who had initially negative SBCE results. During a mean follow-up of 16.5 months, rebleeding occurred in 19 (18.4%) patients. The rebleeding rate was 23.6% (13/55) for patients with meaningful findings and 16.1% (5/31) for patients without meaningful findings (P = 0.411). CONCLUSION Our CNN algorithm detected meaningful findings in negative SBCE videos that were missed by humans. The use of deep CNN for SBCE image reading is expected to compensate for human error.
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Affiliation(s)
- Kyung Seok Choi
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - DoGyeom Park
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Jin Su Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dae Young Cheung
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Bo-In Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Young-Seok Cho
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jin Il Kim
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seungchul Lee
- Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, Korea
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Korea
| | - Han Hee Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Fantasia S, Cortegoso Valdivia P, Kayali S, Koulaouzidis G, Pennazio M, Koulaouzidis A. The Role of Capsule Endoscopy in the Diagnosis and Management of Small Bowel Tumors: A Narrative Review. Cancers (Basel) 2024; 16:262. [PMID: 38254753 PMCID: PMC10813471 DOI: 10.3390/cancers16020262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/21/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Small bowel tumors (SBT) are relatively rare, but have had a steadily increasing incidence in the last few decades. Small bowel capsule endoscopy (SBCE) and device-assisted enteroscopy are the main endoscopic techniques for the study of the small bowel, the latter additionally providing sampling and therapeutic options, and hence acting complementary to SBCE in the diagnostic work-up. Although a single diagnostic modality is often insufficient in the setting of SBTs, SBCE is a fundamental tool to drive further management towards a definitive diagnosis. The aim of this paper is to provide a concise narrative review of the role of SBCE in the diagnosis and management of SBTs.
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Affiliation(s)
- Stefano Fantasia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Pablo Cortegoso Valdivia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
| | - Stefano Kayali
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - George Koulaouzidis
- Department of Biochemical Sciences, Pomeranian Medical University, 70204 Szczecin, Poland;
| | - Marco Pennazio
- University Division of Gastroenterology, City of Health and Science University Hospital, University of Turin, 10126 Turin, Italy;
| | - Anastasios Koulaouzidis
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark;
- Department of Gastroenterology, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70204 Szczecin, Poland
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Xie X, Xiao YF, Zhao XY, Li JJ, Yang QQ, Peng X, Nie XB, Zhou JY, Zhao YB, Yang H, Liu X, Liu E, Chen YY, Zhou YY, Fan CQ, Bai JY, Lin H, Koulaouzidis A, Yang SM. Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review. JAMA Netw Open 2022; 5:e2221992. [PMID: 35834249 PMCID: PMC9284338 DOI: 10.1001/jamanetworkopen.2022.21992] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
IMPORTANCE Reading small bowel capsule endoscopy (SBCE) videos is a tedious task for clinicians, and a new method should be applied to solve the situation. OBJECTIVES To develop and evaluate the performance of a convolutional neural network algorithm for SBCE video review in real-life clinical care. DESIGN, SETTING, AND PARTICIPANTS In this multicenter, retrospective diagnostic study, a deep learning neural network (SmartScan) was trained and validated for the SBCE video review. A total of 2927 SBCE examinations from 29 medical centers were used to train SmartScan to detect 17 types of CE structured terminology (CEST) findings from January 1, 2019, to June 30, 2020. SmartScan was later validated with conventional reading (CR) and SmartScan-assisted reading (SSAR) in 2898 SBCE examinations collected from 22 medical centers. Data analysis was performed from January 25 to December 31, 2021. EXPOSURE An artificial intelligence-based tool for interpreting clinical images of SBCE. MAIN OUTCOMES AND MEASURES The detection rate and efficiency of CEST findings detected by SSAR and CR were compared. RESULTS A total of 5825 SBCE examinations were retrospectively collected; 2898 examinations (1765 male participants [60.9%]; mean [SD] age, 49.8 [15.5] years) were included in the validation phase. From a total of 6084 CEST-classified SB findings, SSAR detected 5834 findings (95.9%; 95% CI, 95.4%-96.4%), significantly higher than CR, which detected 4630 findings (76.1%; 95% CI, 75.0%-77.2%). SmartScan-assisted reading achieved a higher per-patient detection rate (79.3% [2298 of 2898]) for CEST findings compared with CR (70.7% [2048 of 2298]; 95% CI, 69.0%-72.3%). With SSAR, the mean (SD) number of images (per SBCE video) requiring review was reduced to 779.2 (337.2) compared with 27 910.8 (12 882.9) with CR, for a mean (SD) reduction rate of 96.1% (4.3%). The mean (SD) reading time with SSAR was shortened to 5.4 (1.5) minutes compared with CR (51.4 [11.6] minutes), for a mean (SD) reduction rate of 89.3% (3.1%). CONCLUSIONS AND RELEVANCE This study suggests that a convolutional neural network-based algorithm is associated with an increased detection rate of SBCE findings and reduced SBCE video reading time.
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Affiliation(s)
- Xia Xie
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Yu-Feng Xiao
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Xiao-Yan Zhao
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Jian-Jun Li
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Qiang-Qiang Yang
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Xue Peng
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Xu-Biao Nie
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Jian-Yun Zhou
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Yong-Bing Zhao
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Huan Yang
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Xi Liu
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - En Liu
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Yu-Yang Chen
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Yuan-Yuan Zhou
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Chao-Qiang Fan
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Jian-Ying Bai
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
| | - Hui Lin
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
- Department of Epidemiology, the Third Military Medical University, Chongqing, China
| | | | - Shi-Ming Yang
- Department of Gastroenterology, The Second Affiliated Hospital, the Third Military Medical University, Chongqing, China
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Koulaouzidis A, Marlicz W, Koulaouzidis G. Den lille Havfrue for the gut. Endosc Int Open 2022; 10:E293. [PMID: 35433213 PMCID: PMC9010085 DOI: 10.1055/a-1785-4672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
Affiliation(s)
- Anastasios Koulaouzidis
- Department of Medicine, OUH Svendborg Sygehus, Svendborg, Denmark,Department of Clinical Research, University of Southern Denmark (SDU), Odense, Denmark,Surgical Research Unit, OUH, Odense, Denmark,Department of Social Medicine and Public Health, Pomeranian Medical University (PMU), Szczecin, Poland
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Koulaouzidis A, Dabos K, Toth E. Diving method or simply…water-immersion small-bowel capsule endoscopy. Gastrointest Endosc 2021; 94:878-879. [PMID: 34530981 DOI: 10.1016/j.gie.2021.05.044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 05/29/2021] [Indexed: 02/08/2023]
Affiliation(s)
| | | | - Ervin Toth
- Skåne University Hospital, Lund University, Malmö, Sweden
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