Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with...

With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients. A total of 425 AIS patients who underwent posterior spinal fixation were collected. Variables such as age, gender, preoperative and final follow-up horizontal and vertical coordinate vectors, and screw positioning data were preprocessed by parameterizing image data and transforming various data types into a unified, continuous high-dimensional feature space. Four deep learning models were designed, including Multi-Layer Perceptron model, Encoder-Decoder model, CNN-LSTM Attention model and Deep FM model. For the implementation of deep learning, 70% of the data was adopted for training and 30% for evaluation. The mean square error (MSE), mean absolute error (MAE) and curve fitting between the predicted and corresponding real postoperative spinal coordinates of the test set were adopted to validate and compare the efficacy of the DL models. A total of 425 patients with an average age of 14.60 ± 2.08 years, including 77 males and 348 females, were enrolled in this study. The Lenke type 1 and 5 AIS patients accounted for the majority of the included patients. The results showed that the Multi-Layer Perceptron model achieved the best performance among the four DL models, with a mean square error of 2.77 × 10^-5 and an average absolute error of 0.00350 on the validation set. Moreover, the results predicted by the Multi-Layer Perceptron model closely matched the actual coordinate positions on the original postoperative images of patients with Lenke type 1 and AIS patients. Deep learning models can provide alternative and effective decision-making support for AIS patients undergoing surgery. Regarding the learning curve and data volume, the optimal DL models should be adjusted and refined to meet future demands.