Application and Evaluation of Vision-LSTM Modeling in Diagnostic Ultrasound Imaging of TI-RADS category 4b thyroid nodules

Background ：Accurate differentiation of TI-RADS category 4b thyroid nodules remains a critical clinical challenge, with 20-50% malignancy risk necessitating invasive biopsies. Existing diagnostic methods heavily rely on physician expertise, leading to inconsistencies in resource-limited settings.To develop and validate a Vision-LSTM model for ultrasound-based diagnosis of TI-RADS 4b nodules, integrating spatiotemporal feature analysis to mimic clinicians’ dynamic decision-making. Methods ：Retrospective analysis of 401 pathologically confirmed TI-RADS 4b nodules (188 malignant, 213 benign) was performed. The Vision-LSTM model, combining LSTM layers for temporal dynamics and convolutional networks for spatial features, was trained on 7:3 split data. Performance was compared against junior (AUC=0.624) and senior physicians (AUC=0.787) using ROC analysis, Delong test, and precision-recall metrics. Results ：The Vision-LSTM model significantly outperformed the junior practitioners and slightly outperformed the senior practitioners in terms of diagnostic accuracy and AUC values.The AI model was able to consistently identify complex features in ultrasound images and output consistent and accurate diagnostic results, demonstrating a high degree of accuracy and reliability. Conclusion ：This study demonstrates that the Vision-LSTM model significantly improves diagnostic consistency for TI-RADS 4b nodules, offering a clinically deployable tool to reduce healthcare disparities. Future work will focus on multi-center validation and real-time integration with ultrasound systems.