ReviewSkeletal radiology2020
Current applications and future directions of deep learning in musculoskeletal radiology.
Review in Skeletal radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 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
58 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Changes in shoulder range of motion following arthroscopic rotator cuff repair: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Role of radiomics in staging liver fibrosis: a meta-analysis.BMC medical imaging · 2024Pooled it
- Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Pooled it
- Deep Learning-Based Multi-Class Pediatric Wrist Fracture Subtype Classification: A Pilot Study Comparing Convolutional Neural Network Architectures.Journal of imaging · 2026Article
- 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
- Advanced MRI Sequences for Structural Lesion Assessment in Sacroiliitis.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning-based Detection of Hip Anatomical Landmarks in X-ray Images.The archives of bone and joint surgery · 2026Article
- Estimation of time-to-total knee replacement surgery with multimodal modeling and artificial intelligence.Computers in biology and medicine · 2025Article
- AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model.Journal of biomedical physics & engineering · 2025Article
- Current Update on DWI-MRI and Its Radiomics in Liver Fibrosis-A Review of the Literature.Tomography (Ann Arbor, Mich.) · 2025Review
- Estimation of Trabecular Bone Volume with Dual-Echo Ultrashort Echo Time (UTE) Magnetic Resonance Imaging (MRI) Significantly Correlates with High-Resolution Computed Tomography (CT).Journal of imaging · 2025Article
- ChatGPT's diagnostic performance based on textual vs. visual information compared to radiologists' diagnostic performance in musculoskeletal radiology.European radiology · 2025Article
- Comparison of an AI-driven planning tool and manual radiographic measurements in total knee arthroplasty.Computational and structural biotechnology journal · 2025Article
- Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images.Sensors (Basel, Switzerland) · 2024Article
- Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine.La Radiologia medica · 2024Article
- Deep learning for synovial volume segmentation of the first carpometacarpal joint in osteoarthritis patients.Osteoarthritis imaging · 2024Article
- Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique.PloS one · 2024Article
- Knowledge mapping and bibliometric analysis of medical knee magnetic resonance imaging for knee osteoarthritis (2004-2023).Frontiers in surgery · 2024Article
- Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays.Journal of medical signals and sensors · 2024Article
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of artificial intelligence that is ideally suited to solving image-based problems. There are an increasing number of musculoskeletal applications of deep learning, which can be conceptually divided into the categories of lesion detection, classification, segmentation, and non-interpretive tasks. Numerous examples of deep learning achieving expert-level performance in specific tasks in all four categories have been demonstrated in the past few years, although comprehensive interpretation of imaging examinations has not yet been achieved. It is important for the practicing musculoskeletal radiologist to understand the current scope of deep learning as it relates to musculoskeletal radiology. Interest in deep learning from researchers, radiology leadership, and industry continues to increase, and it is likely that these developments will impact the daily practice of musculoskeletal radiology in the near future.
Indexed as
Identifiers
31377836What 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.