Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review
Xia Xie, Yu-Feng Xiao, Xiao-Yan Zhao, Jian-Jun Li, Qiang-Qiang Yang, Xue Peng, Xu-Biao Nie, Jian-Yun Zhou, Yong-Bing Zhao, Huan Yang, Xi Liu, En Liu, Yu-Yang Chen, Yuan-Yuan Zhou, Chao-Qiang Fan, Jian-Ying Bai, Hui Lin, Anastasios Koulaouzidis, Shi-Ming Yang
JAMA Netw Open . 2022 Jul 1;5(7):e2221992. doi: 10.1001/jamanetworkopen.2022.21992.
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.
Read Full Article Here: https://doi.org/10.1001/jamanetworkopen.2022.21992