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Low back pain (LBP) and sciatica may require surgical therapy when they aresymptomatic of severe pain. However, there is no effective measures to evaluatethe surgical outcomes in advance. This work combined elements of Easternmedicine and machine learning, and developed a preoperative assessment tool topredict the prognosis of lumbar spinal surgery in LBP and sciatica patients.Standard operative assessments, traditional Chinese medicine body constitutionassessments, planned surgical approach, and vowel pronunciation recordings werecollected and stored in different modalities. Our work provides insights intoleveraging modality combinations, multimodals, and fusion strategies. Theinterpretability of models and correlations between modalities were alsoinspected. Based on the recruited 105 patients, we found that combiningstandard operative assessments, body constitution assessments, and plannedsurgical approach achieved the best performance in 0.81 accuracy. Our approachis effective and can be widely applied in general practice due to simplicityand effective.

Preoperative Prognosis Assessment of Lumbar Spinal Surgery for Low Back Pain and Sciatica Patients based on Multimodalities and Multimodal Learning