The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization- experimental study.

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Abstract

Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) can adequately detect the injury. However, these lethal injuries sometime have been overlooked in current practice. Deep learning algorithms have proven their capabilities in detecting abnormal findings in medical images. The aim of this study is to develop a three-dimensional, unsupervised deep learning algorithm for detecting splenic injury on abdominal CT using a sequential localization and classification approach.The data set was collected in a tertiary trauma center on 600 patients who underwent abdominal CT between 2008 and 2018, half of whom had splenic injuries. The images were split into development and test datasets at a 4:1 ratio. A 2-step deep learning algorithm, including localization and classification models, was constructed to identify the splenic injury. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, PPV, and NPV. Grad-CAM heatmaps from the test set were visually assessed. To validate the algorithm, we also collected images from another hospital to serve as external validation data.A total of 480 patients, 50% of whom had spleen injuries, were included in the development dataset, and the rest were included in the test dataset. All patients underwent contrast-enhanced abdominal CT in the emergency room. The automatic 2-step EfficientNet model detected splenic injury with an AUROC of 0.901 (95% CI:0.836-0.953). At the maximum Youden index, the accuracy, sensitivity, specificity, PPV, and NPV were 0.88, 0.81,0.92, 0.91, and 0.83, respectively. The heatmap identified 96.3% of splenic injury sites in true positive cases. The algorithm achieved a sensitivity of 0.92 for detecting trauma in the external validation cohort, with an acceptable accuracy of 0.80.The deep learning model can identify splenic injury on CT and further application in trauma scenarios is possible.Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.