ArticleRadiology. Artificial intelligence2023
A Deep Learning Algorithm for Automatic 3D Segmentation of Rotator Cuff Muscle and Fat from Clinical MRI Scans.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Pooled it
- Deep Learning-Based Scapular Morphology Assessment Pipeline for Glenoid Segmentation and Landmark Localization.Journal of imaging informatics in medicine · 2026Article
- Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis.Journal of advanced research · 2026Article
- Hierarchical Multimodal Fusion of Multi-Sequence MRI and Clinical Metadata for the Classification of Rotator Cuff Tears.Journal of clinical medicine · 2026Article
- Intra- and inter-rater reliability of skeletal muscle mass MRI assessment among adults: a systematic review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial intelligence revolution in shoulder magnetic resonance imaging: current evidence and future directions for rotator cuff diagnosis.Clinics in shoulder and elbow · 2026Article
- Deep learning-based assessment of paraspinal muscle degeneration and its relationships to muscle function and disability outcomes in chronic low back pain: a prospective study.European radiology · 2026Article
- Lower-Body Muscle Volumes Can Explain Half of the Variance in Sprint Speed Between Collegiate American Football Players.Scandinavian journal of medicine & science in sports · 2026Article
- Quantitative fat-fraction analysis of the rotator cuff muscles on clinical sagittal and coronal T1-weighted MRI using deep learning algorithms.Scientific reports · 2026Article
- Magnetic resonance imaging-based fat infiltration grading improves reparability prediction in massive rotator cuff tears.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2026Article
- Patient-Level Classification of Rotator Cuff Tears on Shoulder MRI Using an Explainable Vision Transformer Framework.Journal of clinical medicine · 2026Article
- Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes.Radiology advances · 2026Article
- Automated 3D segmentation of rotator cuff muscle and fat from longitudinal CT for shoulder arthroplasty evaluation.Skeletal radiology · 2026Article
- Advances in Shoulder Pain Imaging: A Narrative Review of Current Practice and Emerging Trends.Current medical imaging · 2025Article
- Trustworthy deep learning for the automated quantification of the fatty infiltration of the rotator cuff muscles using magnetic resonance imaging.JSES international · 2025Article
- Developing a three-dimensional convolutional neural network for automated full-volume multi-tissue segmentation of the shoulder with comparisons to Goutallier classification and partial volume muscle quality analysis.Journal of shoulder and elbow surgery · 2025Article
- XComposition: multimodal deep learning model to measure body composition using chest radiographs and clinical data.Radiology advances · 2025Article
- Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models.Diagnostics (Basel, Switzerland) · 2025Review
- Integrating Artificial Intelligence in Orthopedic Care: Advancements in Bone Care and Future Directions.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The authors aimed to develop and validate an automated artificial intelligence (AI) algorithm for three-dimensional (3D) segmentation of all four rotator cuff (RC) muscles to quantify intramuscular fat infiltration (FI) and individual muscle volume. The dataset included retrospectively collected RC MRI scans in 232 patients (63 with normal RCs, 169 with RC tears). A two-stage AI model was developed to segment all RC muscles and their FI in each stage. For comparison, single-stage and Otsu filtering models were created. Using the two-stage model, segmentation performance demonstrated high Dice scores (mean, 0.92 ± 0.14 [SD]), low volume errors (mean, 5.72% ± 9.23), and low FI errors (mean, 1.54% ± 2.79) when validated in 30 scans. There was a significant correlation between the 3D FI in the RC tear scans with a Goutallier grade (ρ = 0.53,
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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.