Artificial‐intelligence‐driven discovery of prognostic biomarker for sarcopenia. Issue 6 (26th October 2021)

• Record Type: Journal Article
• Authors: Chung, Heewon; Jo, Yunju; Ryu, Dongryeol; Jeong, Changwon; Choe, Seong‐Kyu; Lee, Jinseok
• Is Part Of: Journal of cachexia, sarcopenia and muscle. Volume 12:Issue 6(2021)
• Page Start: 2220
• Page End: 2230
• Publication Date: 2021-10-26
• Subjects: Sarcopenia -- Muscle wasting -- Artificial intelligence -- Transcriptome -- Diagnosis; Cachexia -- Periodicals; Muscles -- Aging -- Periodicals; Muscles -- Periodicals; Cachexia; Sarcopenia; Muscles; Cachexia; Muscles; Muscles -- Aging; Periodicals; Periodicals; 616
• DOI: 10.1002/jcsm.12840
• Languages: English
• ISSNs: 2190-5991

Abstract

Abstract: Background: Sarcopenia 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. Methods: We 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. Results: Among 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. Conclusions: Our new AI model, DSnet‐v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia.