Deep learning using computed tomography to identify high-risk patients for acute small bowel obstruction: development and validation of a prediction model : a retrospective cohort study.

Journal: International journal of surgery (London, England)

PMID: 37720936

OBJECTIVE: To build a novel classifier using an optimized 3D-convolutional neural network for predicting high-grade small bowel obstruction (HGSBO).

Seungmin Oh

Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61186, Korea.
Jongbin Ryu

Department of Computer Engineering, Ajou University, Republic of Korea; Department of Artificial Intelligence, Ajou University, Republic of Korea. Electronic address: jongbinryu@ajou.ac.kr.
Ho-Jung Shin

Department of Surgery, Ajou University School of Medicine, Suwon, South Korea.
Jeong Ho Song

Department of Surgery, Ajou University School of Medicine, Suwon, South Korea.
Sang-Yong Son

Department of Surgery, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-shi, Kyeonggi-do, 463-707, South Korea.
Hoon Hur

Department of Surgery, Ajou University School of Medicine, Suwon, South Korea.
Sang-Uk Han

Department of Surgery, Ajou University School of Medicine, Suwon, South Korea.

Artificial Intelligence Deep Learning Humans Intestinal Obstruction Neural Networks, Computer Retrospective Studies Tomography, X-Ray Computed

View on PubMed Access via DOI PubMed (37720936)