Literature DB >> 33930789

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives.

Miłosz Rozynek1, Iwona Kucybała1, Andrzej Urbanik1, Wadim Wojciechowski2.   

Abstract

Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point, evaluating sarcopenia with imaging methods has been time-consuming and dependent on the skills of the physician. The solution for this problem may be found in artificial intelligence, which may assist radiologists in repetitive tasks such as muscle segmentation and body-composition analysis. The major aim of this review was to find and present the current status and future perspectives of artificial intelligence in the imaging of sarcopenia. We searched the PubMed database to find articles concerning the use of artificial intelligence in diagnostic imaging and especially in body-composition analysis in the context of sarcopenia. We found that artificial-intelligence systems could potentially help with evaluating sarcopenia and better predicting outcomes in a vast range of clinical situations, which could get us closer to the true era of precision medicine.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Body composition; Machine learning; Sarcopenia

Year:  2021        PMID: 33930789     DOI: 10.1016/j.nut.2021.111227

Source DB:  PubMed          Journal:  Nutrition        ISSN: 0899-9007            Impact factor:   4.008


  5 in total

1.  Clinical and economic value of oral nutrition supplements in patients with cancer: a position paper from the Survivorship Care and Nutritional Support Working Group of Alliance Against Cancer.

Authors:  Riccardo Caccialanza; Alessandro Laviano; Cristina Bosetti; Mariateresa Nardi; Valentina Casalone; Lucilla Titta; Roberto Mele; Giovanni De Pergola; Francesco De Lorenzo; Paolo Pedrazzoli
Journal:  Support Care Cancer       Date:  2022-07-06       Impact factor: 3.603

2.  Evaluation of a Fully Automatic Deep Learning-Based Method for the Measurement of Psoas Muscle Area.

Authors:  Dennis Van Erck; Pim Moeskops; Josje D Schoufour; Peter J M Weijs; Wilma J M Scholte Op Reimer; Martijn S Van Mourik; Yvonne C Janmaat; R Nils Planken; Marije Vis; Jan Baan; Robert Hemke; Ivana Išgum; José P Henriques; Bob D De Vos; Ronak Delewi
Journal:  Front Nutr       Date:  2022-05-12

3.  Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer.

Authors:  Thomas Ying; Pablo Borrelli; Lars Edenbrandt; Olof Enqvist; Reza Kaboteh; Elin Trägårdh; Johannes Ulén; Henrik Kjölhede
Journal:  Eur Radiol Exp       Date:  2021-11-19

Review 4.  The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer.

Authors:  Ying-Tzu Huang; Yi-Shan Tsai; Peng-Chan Lin; Yu-Min Yeh; Ya-Ting Hsu; Pei-Ying Wu; Meng-Ru Shen
Journal:  Dis Markers       Date:  2022-03-29       Impact factor: 3.434

Review 5.  A deep look into radiomics.

Authors:  Camilla Scapicchio; Michela Gabelloni; Andrea Barucci; Dania Cioni; Luca Saba; Emanuele Neri
Journal:  Radiol Med       Date:  2021-07-02       Impact factor: 3.469

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.