ArticleRadiology2020
Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs.
Open access · greenAbstract readValidation Study
In one paragraph
Article in Radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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41citing papers in PubMed, 1 pooled it
9.1field-weighted citation impact, top 1% of its field
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.
- Pooled it
- Automated assessment of coronal lower extremity alignment on long-leg radiographs using a deep-learning model: validation, efficiency gains, and superiority of an edge-based tibial joint-line definition.BMC musculoskeletal disorders · 2026Article
- AI-based knee osteoarthritis progression prediction: a comprehensive global bibliometric and hotspot evolution analysis (2010-2025).Knee surgery & related research · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Automated classification of shoulder radiology focusing on cuff tear arthropathy and glenoid erosion using AI.BMC musculoskeletal disorders · 2026Article
- Development and validation of a deep learning model for automatic severity grading of hip osteoarthritis: a multi-center study.Annals of medicine · 2025Article
- Comprehensive Evaluation of Facet Joints Osteoarthritis Radiological Features on Lumbar CT: A Multitask Deep Learning Approach.JOR spine · 2025Article
- Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2025Review
- Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Article
- Article
- The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty.Journal of personalized medicine · 2025Review
- Context-Aware Dual-Task Deep Network for Concurrent Bone Segmentation and Clinical Assessment to Enhance Shoulder Arthroplasty Preoperative planning.IEEE open journal of engineering in medicine and biology · 2025Article
- Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study.BMC musculoskeletal disorders · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- Artificial intelligence can be used in the identification and classification of shoulder osteoarthritis and avascular necrosis on plain radiographs: a training study of 7,139 radiograph sets.Acta orthopaedica · 2024Article
- AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.Radiology. Artificial intelligence · 2024Article
- Article
- Three-Dimensional Reconstruction of Computed Tomography Imaging Is Not Reliable in Assessing Acetabular Rim Osteophytes or Acetabular Rim Pathology in Patients With Femoroacetabular Impingement.Arthroscopy, sports medicine, and rehabilitation · 2024Article
- Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study.JMIR formative research · 2023Article
- Convolutional neural networks on risk stratification of patients with suspected coronary artery disease undergoing coronary computed tomography angiography.The international journal of cardiovascular imaging · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Claudio E von SchackyFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0002-1401-0934
Jae Ho SohnFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0002-6733-7551
Felix LiuFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0001-8288-2817
Eugene OzhinskyFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0001-8993-7905
Pia M JungmannFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0003-2198-3678
Lorenzo NardoFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0002-0266-2708
Magdalena PosadzyFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0002-6194-377X
Sarah C ForemanFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0001-9140-0162
Michael C NevittFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0002-6988-7530
Thomas M LinkFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).ORCID 0000-0002-2850-8143
Valentina PedoiaFrom the Department of Radiology and Biomedical Imaging (C.E.v.S., J.H.S., E.O., P.M.J., M.P., S.C.F., T.M.L., V.P.) and Department of Epidemiology and Biostatistics (F.L., M.C.N.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107; Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany (C.E.v.S., S.C.F.); Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (P.M.J.); and Department of Radiology, University of California Davis Health, Sacramento, Calif (L.N.).
University of California, San Francisco · USTechnical University of Munich · DEUniversity of Freiburg · DE
Funding
Structural, Biochemical and Functional Connectivity in Osteoarthritis using Quantitative Magnetic Resonance Imaging and Skeletal BiomechanicsR01AR069006 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA, SOUZA, RICHARD BRIAN · 2016 to 2025
$7.2MSkeletal Biology and Biomechanics (SBB) CoreP30AR075055 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Wenhan Chang · 2019 to 2026
$7.1MBiomedical Imaging for Clinician ScientistsT32EB001631 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI THOMAS M LINK · 2005 to 2026
$5.6MImpact of Weight loss on Knee Joint Biochemical and Structural DegenerationR01AR064771 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LINK, THOMAS M · 2014 to 2019
$1.6MDeep Learning for Characterizing Knee Joint Degeneration Predicting Progression of Osteoarthritis and Total Knee ReplacementR61AR073552 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA, PEDOIA, VALENTINA · 2018 to 2019
$871kMultidimensional MRI-based Non-Euclidean Deep Learning to Study OsteoarthritisR00AR070902 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PEDOIA, VALENTINA · 2018 to 2020
$747kNIAMS NIH HHS P30 AR075055NIAMS NIH HHS R00 AR070902NIAMS NIH HHS R01 AR064771NIAMS NIH HHS R61 AR073552NIBIB NIH HHS T32 EB001631
Abstract
Background A multitask deep learning model might be useful in large epidemiologic studies wherein detailed structural assessment of osteoarthritis still relies on expert radiologists' readings. The potential of such a model in clinical routine should be investigated. Purpose To develop a multitask deep learning model for grading radiographic hip osteoarthritis features on radiographs and compare its performance to that of attending-level radiologists. Materials and Methods This retrospective study analyzed hip joints seen on weight-bearing anterior-posterior pelvic radiographs from participants in the Osteoarthritis Initiative (OAI). Participants were recruited from February 2004 to May 2006 for baseline measurements, and follow-up was performed 48 months later. Femoral osteophytes (FOs), acetabular osteophytes (AOs), and joint-space narrowing (JSN) were graded as absent, mild, moderate, or severe according to the Osteoarthritis Research Society International atlas. Subchondral sclerosis and subchondral cysts were graded as present or absent. The participants were split at 80% (
Indexed as
Deep LearningModels, TheoreticalRadiographyAgedFemaleHumansMaleMiddle AgedOsteoarthritis, HipRetrospective StudiesSeverity of Illness Index
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