DeepSarc-US: A Deep Learning Framework for Assessing Sarcopenia Using Ultrasound Images
Bibliographic record
Abstract
Sarcopenia, the age-related loss of skeletal muscle mass, is a core component of frailty that is associated with functional decline and adverse health events in older adults. Unfortunately, the available tools to diagnose sarcopenia are often inaccessible or not user-friendly for clinicians. Point-of-care ultrasound (US) is a promising tool that has been used to image the quadriceps muscle and measure its thickness (QMT) as a diagnostic criterion for sarcopenia. This measurement can be challenging for clinicians, especially when performed at the bedside using handheld systems or phased-array probes not designed for this use case. In this paper, we sought to automate this measurement using deep learning methods to improve its accuracy, reliability, and speed in the hands of untrained clinicians. In the proposed framework, which aids in better training, particularly when limited data are available, convolutional and transformer-based deep learning models with generic or data-driven pre-trained weights were compared. We evaluated regression (QMT as a continuous output in cm) and classification (QMT as an ordinal output in 0.5 cm bins) approaches, and in the latter, activation maps were generated to interpret the anatomical landmarks driving the model predictions. Finally, we evaluated a segmentation approach to derive QMT. The results showed that both transformer-based models and convolutional neural networks benefit from the proposed framework in estimating QMT. Additionally, the activation maps highlighted the interface between the femur bone and the quadriceps muscle as a key anatomical landmark for accurate predictions. The proposed framework is a pivotal step to enable the application of US-based measurement of QMT in large-scale clinical studies seeking to validate its diagnostic performance for sarcopenia, alone or with ancillary criteria assessing muscle quality or strength. We believe that implementing the proposed framework will empower clinicians to conveniently diagnose sarcopenia in clinical settings and accordingly personalize the care of older patients, leading to improved patient outcomes and a more efficient allocation of healthcare resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".