Evidence map›Paper›PMID 41114865›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Lower-limb muscle mass quantification and whole-body muscle loss detection using preoperative computed tomography images in patients with hip disease.

Kono Sotaro, Keisuke Uemura, Mazen Soufi, Ryosuke Nishimura, Takuma Miyamoto, Ryo Higuchi, Hirokazu Mae, Kazuma Takashima, Yoshito Otake, Yasuhito Tanaka and 4 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. 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

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

1 citing paper in PubMed.

  1. Review
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

14 authors.

Kono SotaroDepartment of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID https://orcid.org/0009-0008-8393-2781
Keisuke UemuraDepartment of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan. surmountjp@gmail.com.ORCID http://orcid.org/0000-0002-9245-1743
Mazen SoufiDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.ORCID https://orcid.org/0000-0003-0819-966X
Ryosuke NishimuraDepartment of Orthopaedic Surgery, Ehime University Graduate School of Medicine, Toon, Ehime, Japan.
Takuma MiyamotoDepartment of Orthopaedic Surgery, Nara Medical University Graduate School of Medicine, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0002-8504-2706
Ryo HiguchiDepartment of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID https://orcid.org/0009-0007-6766-8696
Hirokazu MaeDepartment of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID https://orcid.org/0009-0000-4617-0409
Kazuma TakashimaDepartment of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID https://orcid.org/0009-0008-4376-8075
Yoshito OtakeDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan.ORCID https://orcid.org/0000-0003-1291-9316
Yasuhito TanakaDepartment of Orthopaedic Surgery, Nara Medical University Graduate School of Medicine, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0002-2300-611X
Masaki TakaoDepartment of Orthopaedic Surgery, Ehime University Graduate School of Medicine, Toon, Ehime, Japan.ORCID https://orcid.org/0000-0002-5626-9477
Nobuhiko SuganoDepartment of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.ORCID https://orcid.org/0000-0002-5305-3179
Seiji OkadaDepartment of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.ORCID https://orcid.org/0000-0002-5107-8209
Hidetoshi HamadaDepartment of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.ORCID https://orcid.org/0000-0003-3652-9400

Funding

Japan Agency for Medical Research and Development JP25hma322015Japanese Orthopaedic Association 2023-2Japan Society for the Promotion of Science 21K18080
6 · The paper itself

Abstract

purposeThis study developed a method for assessing lower-limb lean mass measured by dual-energy X-ray absorptiometry (DXA-LM

methodsThis retrospective study enrolled 227 patients who underwent hip surgery at two institutions. A deep neural network (DNN)-based method was employed in segmenting lower-limb CT images taken for surgical planning, and the CT-MM was calculated using two different density conversion methods: CT-MM1 (CT-MM calculated using the conventional method) and CT-MM2 (CT-MM calculated using the method by Aubrey et al.). Both CT-MMs were correlated with DXA-LM

resultsIn 222 cases that were successfully automatically analyzed, strong correlations were observed between CT-MM1 and DXA-LM

conclusionCT-MMs were strongly correlated with DXA-LM

Indexed as

Lower ExtremityMuscle, SkeletalSarcopeniaTomography, X-Ray ComputedAbsorptiometry, PhotonAgedAged, 80 and overFemaleHumansMaleMiddle AgedPreoperative CareRetrospective StudiesArtificial intelligenceDeep neural networkMuscle massOpportunistic screeningSarcopenia

Identifiers

PMID41114865

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.