Evidence map›Paper›PMID 40782188›Full record

ArticleSkeletal radiology2026

Automated 3D segmentation of rotator cuff muscle and fat from longitudinal CT for shoulder arthroplasty evaluation.

Mingrui Yang, Bong-Jae Jun, Tammy Owings, Nikhil Subhas, Joshua Polster, Carl S Winalski, Jason C Ho, Vahid Entezari, Kathleen A Derwin, Eric T Ricchetti and 1 more

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In one paragraph

Article in Skeletal radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Mingrui YangDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA. yangm@ccf.org.
Bong-Jae JunDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
Tammy OwingsDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
Nikhil SubhasDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
Joshua PolsterDepartment of Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
Carl S WinalskiDepartment of Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
Jason C HoDepartment of Orthopaedics, Cleveland Clinic, Cleveland, OH, USA.
Vahid EntezariDepartment of Orthopaedics, Cleveland Clinic, Cleveland, OH, USA.
Kathleen A DerwinDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
Eric T RicchettiDepartment of Orthopaedics, Cleveland Clinic, Cleveland, OH, USA.
Xiaojuan LiDepartment of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.

Funding

Automated Arthroscopic Partial Meniscectomy Patient Outcome Prediction using Deep LearningK25AR078928 · NIAMS · CLEVELAND CLINIC LERNER COM-CWRU · PI Mingrui Yang · 2022 to 2026
$678k
Identifying the patient, disease, surgical, and implant positional shift factors that predict outcomes following total shoulder arthroplastyR01AR075286 · NIAMS · CLEVELAND CLINIC LERNER COM-CWRU · PI RICCHETTI, ERIC T. · 2020 to 2022
$616k
Cleveland Clinic Artificial Intelligence in MedicineNIAMS NIH HHS K25 AR078928NIAMS NIH HHS K25AR078928NIAMS NIH HHS R01 AR075286NIAMS NIH HHS R01AR075286
6 · The paper itself

Abstract

objectiveTo develop and validate a deep learning model for automated 3D segmentation of rotator cuff muscles on longitudinal CT scans to quantify muscle volume and fat fraction in patients undergoing total shoulder arthroplasty (TSA).

methodsThe proposed segmentation models adopted DeepLabV3 + with ResNet50 as the backbone. The models were trained, validated, and tested on preoperative or minimum 2-year follow-up CT scans from 53 TSA subjects. 3D Dice similarity scores, average symmetric surface distance (ASSD), 95th percentile Hausdorff distance (HD95), and relative absolute volume difference (RAVD) were used to evaluate the model performance on hold-out test sets. The trained models were applied to a cohort of 172 patients to quantify rotator cuff muscle volumes and fat fractions across preoperative and minimum 2- and 5-year follow-ups.

resultsCompared to the ground truth, the models achieved mean Dice of 0.928 and 0.916, mean ASSD of 0.844 mm and 1.028 mm, mean HD95 of 3.071 mm and 4.173 mm, and mean RAVD of 0.025 and 0.068 on the hold-out test sets for the pre-operative and the minimum 2-year follow-up CT scans, respectively.

conclusionThis study developed accurate and reliable deep learning models for automated 3D segmentation of rotator cuff muscles on clinical CT scans in TSA patients. These models substantially reduce the time required for muscle volume and fat fraction analysis and provide a practical tool for investigating how rotator cuff muscle health relates to surgical outcomes. This has the potential to inform patient selection, rehabilitation planning, and surgical decision-making in TSA and RCR.

Indexed as

Adipose TissueArthroplasty, Replacement, ShoulderImaging, Three-DimensionalRotator CuffTomography, X-Ray ComputedAgedDeep LearningFemaleHumansLongitudinal StudiesMaleMiddle AgedAutomated segmentationComputed tomographyDeep learningFat fractionMuscle volumeRotator cuff muscleTotal shoulder arthroplasty

Identifiers

PMID40782188
PMCPMC12338071

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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.