## 1 Introduction  
Diagnostic tests for lumbar myopathy include physical examination, magnetic resonance imaging (MRI), and electromyography. However, objectively measuring the degree of radiating pain is difficult in routine physical examinations [1], and MRI, computed tomography (CT), or electromyography alone may be insufficient diagnostic tools [2].  
According to previous studies, MRI is a good diagnostic tool for lumbar stenosis but is insufficient to objectively predict the degree of radiating pain [3]. Electromyography is a valuable method for diagnosing the presence of radiating pain [4]. However, it is an invasive procedure, as needles are inserted into the patient's body, causing pain during the examination.  
Digital infrared thermographic imaging (DITI) can noninvasively demonstrate functional changes in body temperature at a low cost and has a short examination time. Hypothermia or hyperthermia is induced at the lesion site and can be objectively observed [5]. DITI can visualize changes in the skin temperature, especially in patients with radiating pain [6], thus helping clinicians determine a treatment plan for herinated discs [7]. Additionally, if DITI characteristics change according to the severity of radiating pain, an objective judgment index for subjective pain can be provided.  
Research on artificial intelligence has been conducted to improve accuracy or support diagnosis and treatment in various medical fields (radiology [8], ultrasound [9], and pathology [10]). Therefore, the machine-learning (ML) method used in previous studies was applied to DTI data derived from patients with lumbosacral radiculopathy. As the most frequently affected lumbar discs are L3/4, L4/5, and L5/S1 [11; 12], patients with these radiculopathies were included in our study. We aimed to classify the two classes of patient-reported pain severity for modulating severe and intractable pain first, followed by moderate pain.  
To the best of our knowledge, this study is a significant contribution to the field of pain management and diagnosis. The application of ML to classify pain severity using DTI data is a novel approach that has the potential to improve pain assessment accuracy and optimize treatment plans. This study's focus on lumbosacral radiculopathy is also noteworthy, as this is a common and often debilitating condition that can significantly impact a patient's quality of life. One of the main advantages of using ML in pain assessment and diagnosis is that it can enable local medical personnel, who may not be spine specialists, to develop treatment plans with conservative interventions. This can be particularly beneficial in areas where access to specialized medical care is limited, and patients may have to rely on general practitioners or non-specialists for pain management. ML can provide a reliable and objective tool for these medical professionals to accurately assess pain severity and develop effective treatment plans. In addition to its potential clinical applications, we also applied an ML model calibration for general applications. Model calibration is a critical step in the development of ML models, as it ensures that the model's predictions are accurate and reliable. This is particularly important in the medical field, where inaccurate predictions can have serious consequences for patient health and well-being. This study provides valuable insights into the potential applications of thermography and ML in pain assessment and management.