ArticleBioengineering (Basel, Switzerland)2024
Rectus Femoris Muscle Segmentation on Ultrasound Images of Older Adults Using Automatic Segment Anything Model, nnU-Net and U-Net-A Prospective Study of Hong Kong Community Cohort.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Artificial Intelligence for Computer-Aided Detection in Biomedical Applications.Bioengineering (Basel, Switzerland) · 2026Article
- Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Sarcopenia is characterized by a degeneration of muscle mass and strength that incurs impaired mobility, posing grievous impacts on the quality of life and well-being of older adults worldwide. In 2018, a new international consensus was formulated to incorporate ultrasound imaging of the rectus femoris (RF) muscle for early sarcopenia assessment. Nonetheless, current clinical RF muscle identification and delineation procedures are manual, subjective, inaccurate, and challenging. Thus, developing an effective AI-empowered RF segmentation model to streamline downstream sarcopenia assessment is highly desirable. Yet, this area of research readily goes unnoticed compared to other disciplines, and relevant research is desperately wanted, especially in comparison among traditional, classic, and cutting-edge segmentation networks. This study evaluated an emerging Automatic Segment Anything Model (AutoSAM) compared to the U-Net and nnU-Net models for RF segmentation on ultrasound images. We prospectively analyzed ultrasound images of 257 older adults (aged > 65) in a community setting from Hong Kong's District Elderly Community Centers. Three models were developed on a training set (
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What Socratic 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 Socratic graph.