Evidence map›Paper›PMID 42170046›Full record

ArticleProgress in rehabilitation medicine2026

Skeletal Muscle Mass Estimation from Lower Leg Digital Images Using Machine Learning.

Tomohiko Nagano, Hiroo Matsuse, Yoshio Takano, Ryuki Hashida, Junichi Kawakami, Osamu Taguchi, Kenta Murotani, Shigeaki Matsuo, Koji Hiraoka

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PubMed Full text at PMC Publisher
In one paragraph

Article in Progress in rehabilitation medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

9 authors.

Tomohiko NaganoGraduate School of Medicine, Kurume University, Kurume, Japan.
Hiroo MatsuseDivision of Rehabilitation, Kurume University Hospital, Kurume, Japan.
Yoshio TakanoOsaka University of Arts Junior College, Osaka, Japan.
Ryuki HashidaDivision of Rehabilitation, Kurume University Hospital, Kurume, Japan.
Junichi KawakamiDepartment of Anatomy, Nippon Dental University School of Life Dentistry at Niigata, Niigata, Japan.
Osamu TaguchiDepartment of Rehabilitation, Kokura Rehabilitation Hospital, Kitakyushu, Japan.
Kenta MurotaniBiostatistics Center, Kurume University, Kurume, Japan.
Shigeaki MatsuoDepartment of Mechanical Systems Engineering, Kurume Institute of Technology, Kurume, Japan.
Koji HiraokaDepartment of Orthopaedic Surgery, Kurume University School of Medicine, Kurume, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: In the current study, a convolutional neural network (CNN) model for estimating the skeletal muscle index (SMI) from lower leg digital images was developed, and its predictive performance was evaluated. Methods: One hundred healthy adults participated in the study; 50 men and 50 women (median age 37.5 years, interquartile range 28-44 years). Digital images of the non-dominant lower leg were obtained from lateral and posterior views. A CNN model was trained to estimate SMI after applying data augmentation and Canny edge detection. Predictive accuracy was evaluated using fivefold cross-validation (80% training, 20% testing). Results: The mean SMI was 7.0 ± 1.1 kg/m Conclusions: The CNN model accurately estimated SMI from lower leg digital images, with prediction errors below 5%. This simple, noninvasive method could serve as a practical tool for screening skeletal muscle mass.

Indexed as

convolutional neural networklower leg imagessarcopenia

Identifiers

PMID42170046
PMCPMC13187861

What OpenQuestion holds

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Registered trials

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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.