Diagnosing gangrenous cholecystitis on computed tomography using deep learning: A preliminary study. Issue 1 (20th September 2022) - Okuda, Yoichi; Saida, Tsukasa; Morinaga, Keigo; Ohara, Arisa; Hara, Akihiro; Hashimoto, Shinji; Takahashi, Shinji; Goya, Tomoyuki; Ohkohchi, Nobuhiro
Abstract : Aim: To compare deep learning and experienced physicians in diagnosing gangrenous cholecystitis using computed tomography images and explore the feasibility of diagnostic assistance for acute cholecystitis requiring emergency surgery. Methods: This retrospective study included 25 patients with pathologically confirmed gangrenous cholecystitis and 129 patients with noncomplicated acute cholecystitis who underwent computed tomography between 2016 and 2021 at two institutions. All available computed tomography images at the time of the initial diagnosis were used for the analysis. A deep learning model based on a convolutional neural network was trained using 1, 517 images of 112 patients (18 patients with gangrenous cholecystitis and 94 patients with acute cholecystitis) and tested with 68 images of 42 patients (seven patients with gangrenous cholecystitis and 35 patients with acute cholecystitis). Three blinded, experienced physicians independently interpreted the test images. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were compared between the convolutional neural network and the reviewers. Results: The convolutional neural network (sensitivity, 0.70; 95% confidence interval [CI], 0.44–0.87, specificity, 0.93; 95% CI, 0.88–0.96, accuracy, 0.89; 95% CI, 0.81–0.95, area under the receiver operating characteristic curve, 0.84; 95% CI, 0.68–1.00) had achieved a better diagnostic performance than the reviewers (ex. sensitivity, 0.55; 95% CI, 0.30–0.77, specificity, 0.67; 95% CI, 0.62–0.71, accuracy, 0.65; 95% CI, 0.57–0.72, area under the receiver operating characteristic curve, 0.63; 95% CI, 0.44–0.82; P  = 0.048 for area under the receiver operating characteristic curve versus convolutional neural network). Conclusions: Deep learning had a better diagnostic performance than experienced reviewers in diagnosing gangrenous cholecystitis and has potential applicability for assisting in identifying indications for emergency surgery in the future. Abstract : Differentiating cholecystitis using a CNN.