首个同步记录头姿与眼动的3D医学数据集，助力AI诊断眼源性异常头位。
PoseGaze-AHP: A Knowledge-Based 3D Dataset for AI-Driven Ocular and Postural Diagnosis
- 用大语言模型从文献提取临床数据，结合3D建模生成真实感姿态
- 共生成7920张图像，涵盖多种眼病状态，数据提取准确率达91.92%
- 专为眼源性异常头位设计，适合医疗AI研究者和隐私合规系统开发
诊断眼源性异常头位（AHP）需综合分析头姿与眼球运动，但现有数据集多仅关注单一维度，限制了集成化诊断方法的发展。为此，本文提出PoseGaze-AHP——首个公开的3D数据集，同步捕捉眼源性AHP相关的头姿与眼动信息。通过迭代式提示策略，利用Claude 3.5 Sonnet大语言模型从医学文献中提取结构化临床数据，并采用神经头像（NHA）框架将其转化为3D表示。最终生成7,920张图像，基于两种头面纹理覆盖广泛眼病场景。数据提取整体准确率达91.92%，验证了方法可靠性。该数据集支持高精度、隐私合规的AI辅助诊断工具研发。
原文摘要 · Abstract (English)
Diagnosing ocular-induced abnormal head posture (AHP) requires a comprehensive analysis of both head pose and ocular movements. However, existing datasets focus on these aspects separately, limiting the development of integrated diagnostic approaches and restricting AI-driven advancements in AHP analysis. To address this gap, we introduce PoseGaze-AHP, a novel 3D dataset that synchronously captures head pose and gaze movement information for ocular-induced AHP assessment. Structured clinical data were extracted from medical literature using large language models (LLMs) through an iterative process with the Claude 3.5 Sonnet model, combining stepwise, hierarchical, and complex prompting strategies. The extracted records were systematically imputed and transformed into 3D representations using the Neural Head Avatar (NHA) framework. The dataset includes 7,920 images generated from two head textures, covering a broad spectrum of ocular conditions. The extraction method achieved an overall accuracy of 91.92%, demonstrating its reliability for clinical dataset construction. PoseGaze-AHP is the first publicly available resource tailored for AI-driven ocular-induced AHP diagnosis, supporting the development of accurate and privacy-compliant diagnostic tools.
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