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Zhu Y, Zhang DF, Wu HL, Fu PY, Feng L, Zhuang K, Geng ZH, Li KK, Zhang XH, Zhu BQ, Qin WZ, Lin SL, Zhang Z, Chen TY, Huang Y, Xu XY, Liu JZ, Wang S, Zhang W, Li QL, Zhou PH. Improving bowel preparation for colonoscopy with a smartphone application driven by artificial intelligence. NPJ Digit Med 2023; 6:41. [PMID: 36918730 PMCID: PMC10011797 DOI: 10.1038/s41746-023-00786-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/23/2023] [Indexed: 03/16/2023] Open
Abstract
Optimal bowel preparation is a prerequisite for a successful colonoscopy; however, the rate of inadequate bowel preparation remains relatively high. In this study, we establish a smartphone app that assesses patient bowel preparation using an artificial intelligence (AI)-based prediction system trained on labeled photographs of feces in the toilet and evaluate its impact on bowel preparation quality in colonoscopy outpatients. We conduct a prospective, single-masked, multicenter randomized clinical trial, enrolling outpatients who own a smartphone and are scheduled for a colonoscopy. We screen 578 eligible patients and randomize 524 in a 1:1 ratio to the control or AI-driven app group for bowel preparation. The study endpoints are the percentage of patients with adequate bowel preparation and the total BBPS score, compliance with dietary restrictions and purgative instructions, polyp detection rate, and adenoma detection rate (secondary). The prediction system has an accuracy of 95.15%, a specificity of 97.25%, and an area under the curve of 0.98 in the test dataset. In the full analysis set (n = 500), adequate preparation is significantly higher in the AI-driven app group (88.54 vs. 65.59%; P < 0.001). The mean BBPS score is 6.74 ± 1.25 in the AI-driven app group and 5.97 ± 1.81 in the control group (P < 0.001). The rates of compliance with dietary restrictions (93.68 vs. 83.81%, P = 0.001) and purgative instructions (96.05 vs. 84.62%, P < 0.001) are significantly higher in the AI-driven app group, as is the rate of additional purgative intake (26.88 vs. 17.41%, P = 0.011). Thus, our AI-driven smartphone app significantly improves the quality of bowel preparation and patient compliance.
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Affiliation(s)
- Yan Zhu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Dan-Feng Zhang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Hui-Li Wu
- Department of Gastroenterology, Zhengzhou Central Hospital, Henan, China
| | - Pei-Yao Fu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Li Feng
- Endoscopy Center, Central Hospital of Minhang District, Shanghai, China
| | - Kun Zhuang
- Department of Gastroenterology, Xian Central Hospital, Shaanxi, China
| | - Zi-Han Geng
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Kun-Kun Li
- Department of Gastroenterology, Zhengzhou Central Hospital, Henan, China
| | - Xiao-Hong Zhang
- Endoscopy Center, Central Hospital of Minhang District, Shanghai, China
| | - Bo-Qun Zhu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Wen-Zheng Qin
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Sheng-Li Lin
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Zhen Zhang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Tian-Yin Chen
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Yuan Huang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Xiao-Yue Xu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Jing-Zheng Liu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Shuo Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Wei Zhang
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Quan-Lin Li
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China. .,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China.
| | - Ping-Hong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China. .,Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China.
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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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Dray X, Toth E, de Lange T, Koulaouzidis A. Artificial intelligence, capsule endoscopy, databases, and the Sword of Damocles. Endosc Int Open 2021; 9:E1754-E1755. [PMID: 34790540 PMCID: PMC8589560 DOI: 10.1055/a-1521-4882] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Xavier Dray
- Sorbonne University, Centre for Digestive Endoscopy, Hôpital Saint Antoine, APHP, Paris, France
| | - Ervin Toth
- Skane University Hospitals, Endoscopy Unit, Department of Gastroenterology, Malmo, Sweden
| | - Thomas de Lange
- Sahlgrenska University Hospital-Molndal, Medical Department, Gothenburg, Sweden
| | - Anastasio Koulaouzidis
- Pomeranian Medical University in Szczecin, Department of Social Medicine & Public Health, Faculty of Health Sciences, Zchodniopomorskie, Poland
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