Clinical benchmark dataset for AI accuracy analysis: quantifying radiographic annotation of pelvic tilt

• Published in: Scientific data Vol. 11; no. 1; pp. 1162 - 8
• Main Authors: Chai, Yuan, Boudali, A. Mounir, Maes, Vincent, Walter, William L.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/575; 692/698/1671/63; 692/700/1421/2770; Accuracy; Anatomic Landmarks; Annotations; Artificial Intelligence; Benchmarking; Benchmarks; Data Descriptor; Datasets; Humanities and Social Sciences; Humans; Localization; multidisciplinary; Pelvis - diagnostic imaging; Radiography; Retrospective Studies; Science; Science (multidisciplinary)

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 pres...