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Back pain is one of the most common chronic diseases worldwide, with nearly a quarter of the world’s population suffering from chronic low back pain (1). In a study on the US veteran population, the annualized prevalence rate of patients with a diagnosis of lower back pain increased by 4.8% per year, compared to 3.8% for depression and 4.1% for hypertension (2). Like other chronic illnesses, back pain has profound effect on daily life with impairment in activities and decrease in quality of life. This leads to further hospital and primary care physician visits with an estimated $200 billion being spent annually on the management of back pain (3). In terms of diagnosing back pain, the first step is distinguishing mechanical pain from neuropathic pain. Most cases of back pain are mechanical in nature and respond to rest, ice, physical therapy and activity modification (4). Other conservative management modalities include acetaminophen, nonsteroidal antiinflammatory drugs (NSAIDs), gabapentinoids, opioids and muscle relaxants. A further subgroup of mechanical back pain includes degenerative back pain, which includes osteoarthritis of the spine such as facet joint, sacroiliac joint, spinal stenosis and degenerative disc disease. In the older adult population, most of the low back pain has no definitive pathology and thus is classified as non-specific back pain. Panta et al. mention that in older population, disk degeneration is more prevalent; however, it is less likely to be the pain source as compared to in younger adults (5). This paper explores the risk factors for low back pain in terms of radiologic data and applies artificial intelligence modeling to decipher the associations. Original Article

Prediction of low back pain using artificial intelligence modeling