Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks
Purpose There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors. Methods 66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different t…
Quantitative Analysis of Real-Time Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks: A Preliminary Study
Lumbar sympathetic blocks (LSBs) are commonly performed to treat pain ailments in the lower limbs. LSBs involve injecting local anesthetic around the nerves. The injection is guided by fluoroscopy which is sometimes considered to be insufficiently accurate. The main aim was to analyze the plantar foot skin temperature data acquired while performing LSBs in patients with complex regional pain syndrome (CRPS) affecting the lower limbs. Forty-four LSBs for treating lower limb CRPS in 13 patients were assessed. Pain medicine physicians visualized the infrared thermography (IRT) video in real time and classified the performance depending on the observed thermal changes within the first 4 min. Th…