Clinical benchmark dataset for AI accuracy analysis: quantifying radiographic annotation of pelvic tilt
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...
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Published in | Scientific data Vol. 11; no. 1; pp. 1162 - 8 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
22.10.2024
Nature Publishing Group Nature Portfolio |
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Abstract | 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. |
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AbstractList | 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. 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.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. Abstract 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. |
ArticleNumber | 1162 |
Author | Walter, William L. Chai, Yuan Boudali, A. Mounir Maes, Vincent |
Author_xml | – sequence: 1 givenname: Yuan orcidid: 0000-0001-6977-6155 surname: Chai fullname: Chai, Yuan email: yuanchai95@gmail.com organization: Sydney Musculoskeletal Health and The Kolling Institute, Northern Clinical School, Faculty of Medicine and Health and the Northern Sydney Local Health District – sequence: 2 givenname: A. Mounir surname: Boudali fullname: Boudali, A. Mounir organization: Sydney Musculoskeletal Health and The Kolling Institute, Northern Clinical School, Faculty of Medicine and Health and the Northern Sydney Local Health District – sequence: 3 givenname: Vincent surname: Maes fullname: Maes, Vincent organization: University Hospitals Leuven, Department of Orthopedic Surgery, Department of Orthopedics and Traumatic Surgery, Royal North Shore Hospital – sequence: 4 givenname: William L. surname: Walter fullname: Walter, William L. organization: Sydney Musculoskeletal Health and The Kolling Institute, Northern Clinical School, Faculty of Medicine and Health and the Northern Sydney Local Health District, Department of Orthopedics and Traumatic Surgery, Royal North Shore Hospital, The Orthopaedic Department, St Vincent’s Hospital |
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Snippet | Radiographic landmark annotation determines patients’ anatomical parameters and influences diagnoses. However, challenges arise from ambiguous region-based... Radiographic landmark annotation determines patients' anatomical parameters and influences diagnoses. However, challenges arise from ambiguous region-based... Abstract Radiographic landmark annotation determines patients’ anatomical parameters and influences diagnoses. However, challenges arise from ambiguous... |
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SubjectTerms | 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) |
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Title | Clinical benchmark dataset for AI accuracy analysis: quantifying radiographic annotation of pelvic tilt |
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