Sydney Musculoskeletal Health and The Kolling Institute, Northern Clinical School, Faculty of Medicine and Health and the Northern Sydney Local Health District, Sydney, NSW, Australia.
University Hospitals Leuven, Department of Orthopedic Surgery, Leuven, Belgium.
Sci Data. 2024 Oct 22;11(1):1162. doi: 10.1038/s41597-024-04003-7.
Radiographic landmark annotation determines patients' anatomical parameters and influences diagnoses. However, challenges arise from ambiguous region-based definitions, human error, and image quality variations, potentially compromising patient care. Additionally, AI landmark localization often presents its predictions in a probability-based heatmap format, which lacks a corresponding clinical standard for accuracy validation. This Data Descriptor presents a clinical benchmark dataset for pelvic tilt landmarks, gathered through a probabilistic approach to measure annotation accuracy within clinical environments. A retrospective analysis of 115 pelvic sagittal radiographs was conducted for annotating pelvic tilt parameters by five annotators, revealing landmark cloud sizes of 6.04 mm-17.90 mm at a 95% dataset threshold, corresponding to 9.51°-16.55° maximum angular disagreement in clinical settings. The outcome provides a quantified point cloud dataset for each landmark corresponding to different probabilities, which enables assessment of directional annotation distribution and parameter-wise impact, providing clinical benchmarks. The data is readily reusable for AI studies analyzing the same landmarks, and the method can be easily replicated for establishing clinical accuracy benchmarks of other landmarks.
影像学标志点标注决定了患者的解剖参数，并影响诊断。然而，基于区域的定义不明确、人为误差和图像质量变化等挑战，可能会影响患者的治疗。此外，人工智能标志点定位通常以基于概率的热图格式呈现其预测结果，这缺乏相应的临床准确性验证标准。本数据描述符提供了一个用于骨盆倾斜标志点的临床基准数据集，该数据集是通过一种概率方法在临床环境中测量标注准确性而收集的。通过对 115 张骨盆矢状面射线照片进行回顾性分析，由五名标注者对骨盆倾斜参数进行标注，在 95%数据集阈值下，标志点云大小为 6.04mm-17.90mm，这对应于临床环境下最大角度差异 9.51°-16.55°。该结果为每个标志点提供了对应不同概率的量化点云数据集，可用于评估定向标注分布和参数影响，为临床提供基准。该数据可方便地重复用于分析相同标志点的人工智能研究，并且可以轻松复制该方法以建立其他标志点的临床准确性基准。
