Skip to content Ooops! I can't find the page you're looking for You may not be able to find the page you were after because of: An out-of-date bookmark/favorite A search engine that has an out-of-date listing for us A mis-typed address You might find one of the following links useful: Terms While we only
Deep learning-based artificial intelligence can improve the diagnosis of small bowel obstruction: stratified comparison study and hierarchical Bayesian model – Scientific Reports
- Article
- Open access
- Published:
- Aitaro Takimoto1,
- Yuichiro Hayashi2,
- Kazuki Nishida3,
- Liu Jiahui1,
- Chiyoe Shirota1,
- Masahiro Oda2,
- Akinari Hinoki4,
- Kojiro Suzuki5,
- Kensaku Mori2 &
- …
- Hiroo Uchida1
Scientific Reports (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Subjects
Abstract
Accurate diagnosis of small bowel obstruction (SBO) is critical to patient outcomes, particularly in the emergency department (ED). To enhance diagnostic precision, we developed an artificial intelligence (AI) technology that automatically extracts dilated intestinal segments from contrast-enhanced computed tomography (CT) images. 158 contrast-enhanced CT examinations containing 5,200 annotated images were used for deep learning, and the potential utility of AI in improving SBO diagnosis was subsequently evaluated by residents and surgeons. CT images from 30 patients with suspected SBO in the ED were used as a test set. Seventeen residents and ten surgeons were divided into two groups, one interpreting images with AI support and the other without AI. Participants assessed the presence of SBO and identified the obstruction location, and diagnostic time was recorded. A hierarchical Bayesian model was applied for analysis. The median precision, recall, and Dice score of the AI model were 0.98, 0.63, and 0.77, respectively. For both residents and surgeons, the correct diagnosis rate of the obstruction location was significantly higher in the AI-assisted group compared with the non-AI group (74.1% vs. 56.7% and 88.2% vs. 66.2%, respectively; P < 0.0001 and P = 0.0001). Among residents, AI support significantly improved the diagnosis of obstruction location (odds ratio: 4.20; 95% credible interval: 2.14–8.26) and reduced reading time by 26.84 s per case (95% credible interval: −50.37 to − 2.83). These findings indicate that AI technology is clinically feasible and can improve diagnostic accuracy while reducing the time required for diagnosing bowel obstruction.
Acknowledgements
The authors thank Dr. Kazuhiro Hiramatsu, Dr. Jumpei Shibata and other staff at Toyohashi Municipal Hospital and Aichi Medical University for their cooperation in data collection.
Funding
This work was supported by JSPS KAKENHI (grant number JP24K22377). Aitaro Takimoto was supported by a fellowship of the Nagoya University CIBoG WISE program from MEXT.
Ethics declarations
Competing interests
Akinari Hinoki has a conflict of interest with Hitachi but was not involved in the study’s planning. The impartiality of the study was audited by multiple authors, with no conflicts of interest.
Ethics approval and consent to participate
Nagoya University Hospital Ethics Committee approved this study (Registration ID: 2022 − 0188).
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Takimoto, A., Hayashi, Y., Nishida, K. et al. Deep learning-based artificial intelligence can improve the diagnosis of small bowel obstruction: stratified comparison study and hierarchical Bayesian model. Sci Rep (2026). https://doi.org/10.1038/s41598-026-57999-w
Download citation
-
Received:
-
Accepted:
-
Published:
-
DOI: https://doi.org/10.1038/s41598-026-57999-w
Keywords
Supplementary Material 1
Related Posts
