Table 5

Deep learning is emerging as a transformative tool for chronic pain research. In this systematic review, we evaluated deep learning applications across diverse data modalities (neuroimaging, electrophysiological signals, motion capture, and wearable sensors) for chronic pain diagnosis, classification, and prognostication. A comprehensive search of seven electronic databases identified 22 eligible studies using convolutional neural networks, recurrent neural networks, and hybrid models. Study quality was appraised using PROBAST and PRISMA guidelines. While these studies demonstrate the potential to overcome limitations of subjective pain assessments, heterogeneity in methodologies, sample sizes, and evaluation metrics precluded meta-analysis. Key challenges include data imbalance, limited external validation, and variability in preprocessing approaches. Despite these limitations, deep learning generates non-invasive biomarkers and supports precision medicine strategies in chronic pain management, potentially improving clinical decision-making and reducing the societal burden of chronic pain.

深度学习正成为慢性疼痛研究领域的变革性工具。本项系统综述中，我们针对慢性疼痛的诊断、分类与预后预测，评估了深度学习在多种数据模态（Data Modalities）中的应用，涉及的具体数据类型包括神经影像学（Neuroimaging）、电生理信号（Electrophysiological Signals）、动作捕捉（Motion Capture）及可穿戴传感器（Wearable Sensors）。我们对7个电子数据库开展全面检索，最终纳入22项采用卷积神经网络（Convolutional Neural Networks）、循环神经网络（Recurrent Neural Networks）及混合模型（Hybrid Models）的合格研究。研究质量采用预测模型偏倚风险评估工具（PROBAST）与系统综述与Meta分析优先报告条目（PRISMA）指南进行评价。尽管此类研究展现出弥补主观疼痛评估局限性的潜力，但由于研究方法、样本量与评估指标存在异质性，无法开展元分析（Meta-analysis）。核心挑战包括数据不平衡、外部验证不足以及预处理方法的差异性。尽管存在上述局限性，深度学习仍可生成非侵入性生物标志物，并为慢性疼痛管理提供精准医学策略，有望改善临床决策并减轻慢性疼痛带来的社会负担。