ArticleJournal of cachexia, sarcopenia and muscle2021
Artificial-intelligence-driven discovery of prognostic biomarker for sarcopenia.
Article in Journal of cachexia, sarcopenia and muscle, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Screening and diagnosis tools for sarcopenia: an overview of systematic reviews.Systematic reviews · 2026Review
- Machine Learning-Based Identification and Validation of PYCR1 and PYGM as Prognostic Biomarkers for Osteosarcoma.Clinical Medicine Insights. Oncology · 2026Article
- Machine Learning Applications and Sarcopenia.Advances in experimental medicine and biology · 2026Review
- Neural Cues and Genomic Clues: NGS Insights into Neurogenic Sarcopenia and Muscle Atrophy.International journal of molecular sciences · 2025Review
- Identification of biomarkers associated with glycolysis and lactation in sarcopenia.Scientific reports · 2025Article
- Cancer therapy and cachexia.The Journal of clinical investigation · 2025Review
- Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model.Journal of functional morphology and kinesiology · 2025Article
- Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI.International journal of molecular sciences · 2025Review
- An Indian Consensus on Sarcopenia: Epidemiology, Etiology, Clinical Impact, Screening, and Therapeutic Approaches.International journal of general medicine · 2025Article
- Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China.Frontiers in public health · 2025Article
- Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2024Review
- Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study.JMIR medical education · 2024Article
- Machine Learning Models to Predict Future Frailty in Community-Dwelling Middle-Aged and Older Adults: The ELSA Cohort Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
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- Biomarker discovery process at binomial decision point (2BDP): Analytical pipeline to construct biomarker panel.Computational and structural biotechnology journal · 2023Article
- Transcriptional Control ofInternational journal of molecular sciences · 2022Article
- Machine learning-featured Secretogranin V is a circulating diagnostic biomarker for pancreatic adenocarcinomas associated with adipopenia.Frontiers in oncology · 2022Article
- Artificial-intelligence-driven discovery of prognostic biomarker for sarcopenia.Journal of cachexia, sarcopenia and muscle · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established.
methodsWe develop an artificial intelligence (AI) diagnosis model of sarcopenia using a published transcriptome dataset comprising patients from multiple ethnicities. For the AI model for sarcopenia diagnosis, we use a transcriptome database comprising 17 339 genes from 118 subjects. Among the 17 339 genes, we select 27 features as the model inputs. For feature selection, we use a random forest, extreme gradient boosting and adaptive boosting. Using the top 27 features, we propose a four-layer deep neural network, named DSnet-v1, for sarcopenia diagnosis.
resultsAmong isolated testing datasets, DSnet-v1 provides high sensitivity (100%), specificity (94.12%), accuracy (95.83%), balanced accuracy (97.06%) and area under receiver operating characteristics (0.99). To extend the number of patient data, we develop a web application (http://sarcopeniaAI.ml/), where the model can be accessed unrestrictedly to diagnose sarcopenia if the transcriptome is available. A focused analysis of the top 27 genes for their differential or co-expression with other genes implied the potential existence of race-specific factors for sarcopenia, suggesting the possibility of identifying causal factors of sarcopenia when a more extended dataset is provided.
conclusionsOur new AI model, DSnet-v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia.
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