Deep learning algorithm applied to plain CT images to identify superior mesenteric artery abnormalities

• Published in: European journal of radiology 2024-04, Vol.173, p.111388-111388, Article 111388
• Main Authors: Mei, Junhao, Yan, Hui, Tang, Zheyu, Piao, Zeyu, Yuan, Yuan, Dou, Yang, Su, Haobo, Hu, Chunfeng, Meng, Mingzhu, Jia, Zhongzhi
• Format: Article
• Language: English
• Subjects: Algorithms; Deep Learning; Humans; Mesenteric Artery, Superior - diagnostic imaging; Plain CT; Retrospective Studies; SMA abnormalities; Superior mesenteric artery; Tomography, X-Ray Computed - methods
• ISSN: 0720-048X; 1872-7727
• DOI: 10.1016/j.ejrad.2024.111388

Atypical presentations, lack of biomarkers, and low sensitivity of plain CT can delay the diagnosis of superior mesenteric artery (SMA) abnormalities, resulting in poor clinical outcomes. Our study aims to develop a deep learning (DL) model for detecting SMA abnormalities in plain CT and evaluate its performance in comparison with a clinical model and radiologist assessment. A total of 1048 patients comprised the internal (474 patients with SMA abnormalities, 474 controls) and external testing (50 patients with SMA abnormalities, 50 controls) cohorts. The internal cohort was divided into the training cohort (n = 776), validation cohort (n = 86), and internal testing cohort (n = 86). A total of 5 You Only Look Once version 8 (YOLOv8)–based DL submodels were developed, and the performance of the optimal submodel was compared with that of a clinical model and of experienced radiologists. Of the submodels, YOLOv8x had the best performance. The area under the curve (AUC) of the YOLOv8x submodel was higher than that of the clinical model (internal test set: 0.990 vs 0.878, P =.002; external test set: 0.967 vs 0.912, P =.140) and that of all radiologists (P