Low back pain (LBP) and sciatica may require surgical therapy when they are symptomatic of severe pain. However, there is no effective measures to evaluate the surgical outcomes in advance. This work combined elements of Eastern medicine and machine learning, and developed a preoperative assessment tool to predict the prognosis of lumbar spinal surgery in LBP and sciatica patients. Standard operative assessments, traditional Chinese medicine body constitution assessments, planned surgical approach, and vowel pronunciation recordings were collected and stored in different modalities. Our work provides insights into leveraging modality combinations, multimodals, and fusion strategies. The interpretability of models and correlations between modalities were also inspected. Based on the recruited 105 patients, we found that combining standard operative assessments, body constitution assessments, and planned surgical approach achieved the best performance in 0.81 accuracy. Our approach is effective and can be widely applied in general practice due to simplicity and effective.

翻译：下背痛（Low back pain, LBP）及坐骨神经痛患者在出现剧烈疼痛症状时可能需要接受手术治疗。然而，目前尚无有效手段可预先评估手术结局。本研究融合东方医学与机器学习方法，开发了一套术前评估工具，用于预测LBP及坐骨神经痛患者腰椎手术的预后。研究收集了标准化手术评估、中医体质评估、计划手术入路及元音发音录音等不同模态的数据。本工作揭示了模态组合、多模态学习及融合策略的潜在价值，并对模型可解释性及模态间相关性进行了分析。基于纳入的105例患者数据，研究发现标准化手术评估、体质评估与计划手术入路三者组合可实现最佳性能，准确率达0.81。该方法有效且因简单实用而适用于临床广泛推广。