ReviewJournal of magnetic resonance imaging : JMRI2020
Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease.
Review in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- Changes in shoulder range of motion following arthroscopic rotator cuff repair: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- Comparison of multistage and single-stage framework for automated landmark localization and radiologic measurement: a case of the C1-2 complex on cervical spine lateral radiographs.BMC medical imaging · 2026Article
- Effects of axial malrotation on posterior tibial slope measurement: a digitally reconstructed radiograph study enabling automated quality assessment.Knee surgery & related research · 2026Article
- Development and multicenter external validation of a deep learning model for early screening of thoracic ossification of the ligamentum flavum on routine chest radiographs.Frontiers in radiology · 2026Article
- Technologies to investigate the mechanisms of force-based manipulations: a narrative methods review.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Development and Validation of Transformer- and Convolutional Neural Network-Based Deep Learning Models to Predict Curve Progression in Adolescent Idiopathic Scoliosis.Journal of clinical medicine · 2025Article
- Detection of maxillary sinus pathologies using deep learning algorithms.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.Journal of neurology · 2025Review
- Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- High-Resolution 3D Turbo Spin-Echo Wrist MRI at 7T Accelerated by Compressed Sensing.NMR in biomedicine · 2025Article
- Deep learning models in classifying primary bone tumors and bone infections based on radiographs.NPJ precision oncology · 2025Article
- Artificial intelligence in musculoskeletal applications: a primer for radiologists.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Integration of Artificial Intelligence for Diagnostic Methods in Musculoskeletal Conditions: A Systematic Review.Cureus · 2025Review
- Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence.Scientific reports · 2025Article
- Artificial intelligence in outpatient management: a simulation-based study in a Chinese tertiary hospital.Frontiers in artificial intelligence · 2025Article
- AI-assisted automated interpretation of corneal topography in orthokeratology patients: enhancing diagnostic precision and efficiency.International journal of ophthalmology · 2025Article
- Application and Prospects of Deep Learning Technology in Fracture Diagnosis.Current medical science · 2024Review
- Evaluation of a deep learning software for automated measurements on full-leg standing radiographs.Knee surgery & related research · 2024Article
- Bidirectional dynamic frame prediction network for total-body [EJNMMI physics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep-learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep-learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep-learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep-learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep-learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1607-1619.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.