Artificial intelligence facilitates decision-making in the treatment of lumbar disc herniations

Abstract

Purpose Apart from patients with severe neurological deficits, it is not clear whether surgical or conservative treatment of lumbar disc herniations is superior for the individual patient. We investigated whether deep learning techniques can predict the outcome of patients with lumbar disc herniation after 6 months of treatment. Methods The data of 60 patients were used to train and test a deep learning algorithm with the aim to achieve an accurate prediction of the ODI 6 months after surgery or the start of conservative therapy. We developed an algorithm that predicts the ODI of 6 randomly selected test patients in tenfold cross-validation. Results A 100% accurate prediction of an ODI range could be achieved by dividing the ODI scale into 12% sections. A maximum absolute difference of only 3.4% between individually predicted and actual ODI after 6 months of a given therapy was achieved with our most powerful model. The application of artificial intelligence as shown in this work also allowed to compare the actual patient values after 6 months with the prediction for the alternative therapy, showing deviations up to 18.8%. Conclusion Deep learning in the supervised form applied here can identify patients at an early stage who would benefit from conservative therapy, and on the contrary avoid painful and unnecessary delays for patients who would profit from surgical therapy. In addition, this approach can be used in many other areas of medicine as an effective tool for decision-making when choosing between opposing treatment options, despite small patient groups.

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

“…The reason for the insufficient prediction rate is not the number but the quality of the data [18]. We showed previously that a good prediction is possible even with small groups if the data quality is appropriate [19]. The present study is a preliminary study using prospectively collected registry data generated in daily clinical routine.…”

Assessment of Clinical Decision Support System Efficiency in Spinal Neurosurgery for Personalized Minimally Invasive Technologies Used on Lumbar Spine

“…However, regression models showed the worst efficiency treatment rate for each of clinical outcomes. The implementation of deep learning technology in the study by Wirries et al [ 21 ] contributed to exact prediction of a patient’s functional status by ODI 6 months both after lumbar microdiscectomy and in case of conservative treatment. Using prospective register data (n=635), Siccoli et al [ 22 ] demonstrated a model based on several ML algorithms to enable to provide preoperative planning of the following results: clinical ODI improvement, pain decrease in legs and in spine 12 months after surgery — within the accuracy of 62, 74, and 66%, respectively; reduction of the total number of reoperations — within the accuracy of 69%, and surgery time — within the accuracy of 78%; the reduced length of hospital stay — within the accuracy of 77%.…”

Interlaminar Endoscopic Lumbar Discectomy: A Narrative Review

“…Recent developments in surgical robotic systems, artificial reality, and artificial intelligence (AI) are eyeopening. [32][33][34][35] To date, doctors have been responsible for surgical decisions and surgery, but the outcomes are heterogeneous, probably due to diverse decision-making processes and surgical expertise. 6,13,22,[24][25][26] The expectation for AI and robotic systems is precision medicine, and with the development of new technology, decisionmaking and surgical procedures could be replaced by AI and robots in the near future.…”

“…6,13,22,[24][25][26] The expectation for AI and robotic systems is precision medicine, and with the development of new technology, decisionmaking and surgical procedures could be replaced by AI and robots in the near future. [33][34][35] We cannot stop evolution, but we can work toward systematic, reproducible, reliable, and precise surgical decisions and skills with AI and robotics.…”