用多模态数据和深度学习自动评估疼痛，提升临床实用性
A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods
- 融合多源数据构建自动疼痛评估模型
- 在真实临床场景中达到领先性能
- 适合医疗AI与智能诊断研究者参考
本论文从临床理论出发，系统研究疼痛评估过程，并考察现有自动化方法。在此基础上，核心目标是开发高性能、可落地的计算方法，实现真实临床环境中的自动疼痛评估。重点从计算角度分析影响疼痛感知的年龄、性别等人口统计因素。受限于当前数据条件，提出适用于不同场景的单模态与多模态自动化评估流程。所提方法在多项实验中达到先进水平，同时为人工智能、基础模型与生成式AI在疼痛研究中的应用开辟新路径。
原文摘要 · Abstract (English)
From the original abstract: This thesis initially aims to study the pain assessment process from a clinical-theoretical perspective while exploring and examining existing automatic approaches. Building on this foundation, the primary objective of this Ph.D. project is to develop innovative computational methods for automatic pain assessment that achieve high performance and are applicable in real clinical settings. A primary goal is to thoroughly investigate and assess significant factors, including demographic elements that impact pain perception, as recognized in pain research, through a computational standpoint. Within the limits of the available data in this research area, our goal was to design, develop, propose, and offer automatic pain assessment pipelines for unimodal and multimodal configurations that are applicable to the specific requirements of different scenarios. The studies published in this Ph.D. thesis showcased the effectiveness of the proposed methods, achieving state-of-the-art results. Additionally, they paved the way for exploring new approaches in artificial intelligence, foundation models, and generative artificial intelligence.
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