Multimodal Deep Learning for Grading Carpal Tunnel Syndrome: A Multicenter Study in China.

Abstract

Rationale and objectives: Ultrasound (US)-based deep learning (DL) models for grading the severity of carpal tunnel syndrome (CTS) are scarce. We aimed to advance CTS grading by developing a joint-DL model integrating clinical information and multimodal US features.

Materials and methods: A retrospective dataset of CTS patients from three hospitals was randomly divided into the training (n=680) and internal validation (n=173) sets. An external validation set was prospectively recruited from another hospital (n=174). To further test the model's generalizability, cross-vendor testing was conducted at three additional hospitals utilizing different US systems in the external validation set 2 (n=224). An US-based model was developed to grade CTS severity utilizing multimodal sonographic features, including cross-sectional area [CSA], echogenicity, longitudinal nerve appearance, and intraneural vascularity. A joint-DL model (CTSGrader) was constructed integrating sonographic features and clinical information. Diagnostic performance of both models was verified based on electrophysiological results. In the validation sets, the better-performing model was compared to two junior and two senior radiologists. Additionally, the radiologists' diagnostic performance with artificial intelligence (AI) assistance was evaluated in external validation sets.

Results: CTSGrader achieved areas under the curve (AUCs) of 0.951, 0.910, and 0.897 in the validation sets. The accuracies of CTSGrader were 0.849, 0.833, and 0.827, which were higher than those of US-based model (all p<.05). It outperformed two junior and one senior radiologists (all p<.05) and was equivalent to 1 senior radiologist (all p>.05). With its assistance, the accuracies of two junior and one senior radiologists were improved (all p<.05).

Conclusion: The joint-DL model (CTSGrader) developed in our study outperformed the single-modality model. The AI-aided strategy suggested its potential to support clinical decision-making for grading CTS severity.