Augmented prediction of vertebral collapse after osteoporotic vertebral compression fractures through parameter-efficient fine-tuning of biomedical foundation models.

Vertebral collapse (VC) following osteoporotic vertebral compression fracture (OVCF) often requires aggressive treatment, necessitating an accurate prediction for early intervention. This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. Among 245 enrolled patients with acute OVCF, data from 200 patients were used for the development dataset, and data from 45 patients were used for the test dataset. To construct an accurate prediction model, we explored two backbone architectures: convolutional neural networks and vision transformers (ViTs), along with various pre-trained weights and fine-tuning methods. Through extensive experiments, we built our model by performing parameter-efficient fine-tuning of a ViT model pre-trained on a large-scale biomedical dataset. Attention rollouts indicated that the contours and internal features of the compressed vertebral body were critical in predicting VC with this model. To further improve the prediction performance of our model, we applied the augmented prediction strategy, which uses multiple MRI frames and achieves a significantly higher area under the curve (AUC). Our findings suggest that employing a biomedical foundation model fine-tuned using a parameter-efficient method, along with augmented prediction, can significantly enhance medical decisions.