Use of Artificial Intelligence for the Development of Predictive Model to Help in Decision-Making for Patients with Degenerative Lumbar Spine Disease - Purohit, Gaurav; Choudhary, Madhur; Sinha, V. D. (Asian Journal of Neurosurgery, 2022)
Context The aim of the study was to develop a prognostic model using artificial intelligence for patients undergoing lumbar spine surgery for degenerative spine disease for change in pain, functional status, and patient satisfaction based on preoperative variables included in following categories—sociodemographic, clinical, and radiological.Methods and Materials A prospective cohort of 180 patients with lumbar degenerative spine disease was included and divided into three classes of management—conservative, decompressive surgery, and decompression with fixation. Preoperative variables, change in outcome measures (visual analog scale—VAS, Modified Oswestry Disability Index—MODI, and Neurogenic Claudication Outcome Score—NCOS), and type of management were assessed using Machine Learning models. These were used for creating a predictive tool for deciding the type of management that a patient should undergo to achieve the best results. Multivariate logistic regression was also used to identify prognostic factors of significance.Results The area under the curve (AUC) was calculated from the receiver-operating characteristic (ROC) analysis to evaluate the discrimination capability of various machine learning models. Random Forest Classifier gave the best ROC-AUC score in all three classes (0.863 for VAS, 0.831 for MODI, and 0.869 for NCOS), and the macroaverage AUC score was found to be 0.842 suggesting moderate discriminatory power. A graphical user interface (GUI) tool was built using the machine learning algorithm thus defined to take input details of patients and predict change in outcome measures.Conclusion This study demonstrates that machine learning can be used as a tool to help tailor the decision-making process for a patient to achieve the best outcome. The GUI tool helps to incorporate the study results into active decision-making.
Keywords: neurosurgery; arificial intelligence; degenerative lumbar spine disease; machine learning; Random Forest Classifier; lumbar canal stenosis