Prediction of neurological function status in patients with traumatic cervical spinal cord injury based on machine learning

English Keywords:Traumatic cervical spinal cord injury  Machine learning  Neurological function prediction  Integrated technology  ASIA scores

English Abstract:

  【Abstract】 Objectives: To propose a method based on machine learning to predict the neurological functional status of patients with traumatic cervical spinal cord injury(TCSCI). Methods: The clinical data of 180 patients with TCSCI admitted to Shanghai Changzheng Hospital were retrospectively analyzed, including cervical spine MRI images and American Spinal Injury Association(ASIA) scores within 24 hours after injury and the ASIA scores at 1-year follow-up after injury. The 180 patients were randomly divided into a training set of 144 patients and a test set of 36 patients in a ratio of 8∶2. Overall, a new clinical-imaging prediction method was proposed using the two-stage integration concept, which used the ASIA scores and MRI images within 24 hours after TCSCI to achieve a full-feature prediction of the patient′s sensory and motor function one year after injury. In the first stage, models such as GradentBoosting, GaussianNB, KNeighbors, DecisionTree, RandomForest, and support vector classifier were used to independently predict the sensory-motor function recovery of 132 skin nodes and muscle nodes. In the second stage, the optimal model for each feature prediction was screened out through horizontal and vertical comparison of performance indicators, so as to finally achieve the best prognostic prediction of neurological function at 56 light touch and 56 acupuncture skin nodes, and 20 key muscle nodes. After the constructed prediction model is trained and verified, the prediction of the test set is evaluated using accuracy, precision, recall, average precision and F1 score. Results: In terms of the overall performance of this prediction model in predicting sensory-motor function in TCSCI patients 1 year after injury, all models in the test set achieved accuracy≥0.886, recall≥0.845, precision≥0.875, average precision≥0.853, and F1 score≥0.859, demonstrating that the correct prediction ability of each model and the quality and completeness of the actual prediction results were relatively high. Moreover, the two-stage prediction model can optimize the prediction effect of each model on each feature, and the prediction performance is better. Conclusions: This sensory-motor full-feature prediction method can effectively predict the neurological function recovery of TCSCI patients at 1-year follow-up after injury. Its predictive ability is significantly higher than that of a single model, and is expected to provide a useful reference for the personalized diagnosis, treatment and rehabilitation of TCSCI patients.