Evidence map›Paper›PMID 37251872›Full record

ArticleHeliyon2023

Can artificial intelligence simplify the screening of muscle mass loss?

Enrico Buccheri, Daniele Dell'Aquila, Marco Russo, Rita Chiaramonte, Giuseppe Musumeci, Michele Vecchio

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
4.4field-weighted citation impact, top 5% of its field
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

28 citing papers in PubMed, 25 citations in OpenAlex.

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  14. Performance of Risk Scores in SARS-CoV-2 Infection: A Retrospective Study.International journal of environmental research and public health · 2025
    Observational
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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

6 authors at 2 institutions in 1 country.

Enrico BuccheriDepartment of Biomedical and Biotechnological Sciences, Section of Pharmacology, University of Catania, 95123 Catania, Italy.
Daniele Dell'AquilaDipartimento di Fisica "Ettore Pancini", University of Naples "Federico II", Napoli, Italy.
Marco RussoDepartment of Physics and Astronomy, University of Catania, Catania, Italy.
Rita ChiaramonteDepartment of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy.
Giuseppe MusumeciDepartment of Biomedical and Biotechnological Sciences, Human, Histology and Movement Science Section, University of Catania, 95123, Catania, Italy.
Michele VecchioDepartment of Biomedical and Biotechnological Sciences, Section of Pharmacology, University of Catania, 95123 Catania, Italy.
University of Catania · ITIstituto Nazionale di Fisica Nucleare, Sezione di Napoli · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sarcopenia is a risk factor for morbidity and preventable mortality in old age, with consequent high costs for the national health system. Its diagnosis requires costly radiological examinations, such as the DEXA, which complicate screening in medical centers with a high prevalence of sarcopenia. Objectives: Developing a nearly zero-cost screening tool to emulate the performance of DEXA in identifying patients with muscle mass loss. This can crucially help the early diagnosis of sarcopenia at large-scale, contributing to reduce its prevalence and related complications with timely treatments. Methods: We exploit cross-sectional data for about 14,500 patients and 38 non-laboratory variables from successive NHANES over 7 years (1999-2006). Data are analyzed through a state-of-the-art artificial intelligence approach based on decision trees. Results: A reduced number of anthropometric parameters allows to predict the outcome of DEXA with AUC between 0.92 and 0.94. The most complex model derived in this paper exploits 6 variables, related to the circumference of key corporal segments and to the evaluation of body fat. It achieves an optimal trade-off sensitivity of 0.89 and a specificity of 0.82. Restricting exclusively to variables related to lower limb, we obtain an even simpler tool with only slightly lower accuracy (AUC 0.88-0.90). Conclusions: Anthropometric data seem to contain the entire informative content of a more complex set of non-laboratory variables, including anamnestic and/or morbidity factors. Compared to previously published screening tools for muscle mass loss, the newly developed models are less complex and achieve a better accuracy. The new results might suggest a possible inversion of the standard diagnostic algorithm of sarcopenia. We conjecture a new diagnostic scheme, which requires a dedicated clinical validation that goes beyond the scope of the present study.

Indexed as

Artificial intelligenceBoost decision treesMuscle mass lossSarcopenia

Identifiers

PMID37251872
PMCPMC10208931
OpenAlexW4376956636

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.