Machine Learning-Based Pain Severity Classification of Lumbosacral Radiculopathy Using Infrared Thermal Imaging

Abstract

Pain is subjective and varies among individuals. Doctors determine pain severity based on a patient's self-reported symptoms. In such situations, a language barrier may prevent patients from expressing their pain accurately, which may cause doctors to underestimate their pain degree. Moreover, patients' subjective descriptions of pain can determine their eligibility for secondary benefits, as in the case of compensation for traffic or industrial accidents. Therefore, to perform a multiclass prediction of the severity of lumbar radiculopathy, the authors applied digital infrared thermographic imaging (DITI) to a machine-learning (ML) algorithm. The DITI dataset included data from a healthy population and patients with radiculopathy with herniated lumbar discs at the L3/4, L4/5, and L5/S1 levels. The dataset of 1000 patients was split into training and test datasets in a 7:3 ratio to evaluate the model's performance. For the training dataset, the average accuracy, precision, recall, and F1 score were 0.82, 0.76, 0.72, and 0.74, respectively. For the test dataset, these values were 0.77, 0.71, 0.75, and 0.73, respectively. Applying the ML algorithm to a pain-severity classification using thermographic images will aid in the treatment of lumbosacral radiculopathy and allow providers to monitor the therapeutic effect of interventions through an assessment of physiological evidence.