Publications by authors named "Hou-Shian Lin"
Article Synopsis
- Splenic injuries are common but often overlooked in blunt abdominal trauma; a study aimed to create a deep learning algorithm to detect these injuries using CT scans.
- The dataset consisted of 600 patient scans from a trauma center, and a two-step deep learning model was developed to accurately localize and classify splenic injuries.
- The resulting algorithm demonstrated strong performance with an AUROC of 0.901 and high accuracy, sensitivity, and specificity, successfully identifying 96.3% of splenic injury sites in positive cases.
Objective: To identify the feasibility and efficiency of deep convolutional neural networks (DCNNs) in the detection of ankle fractures and to explore ensemble strategies that applied multiple projections of radiographs.Ankle radiographs (AXRs) are the primary tool used to diagnose ankle fractures. Applying DCNN algorithms on AXRs can potentially improve the diagnostic accuracy and efficiency of detecting ankle fractures.
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