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dc.contributor.author | Mirón-Mombiela, Rebeca | es_ES |
dc.contributor.author | Ruiz-España, Silvia | es_ES |
dc.contributor.author | Moratal, David | es_ES |
dc.contributor.author | Borrás, Consuelo | es_ES |
dc.date.accessioned | 2024-04-11T10:00:33Z | |
dc.date.available | 2024-04-11T10:00:33Z | |
dc.date.issued | 2023-10 | es_ES |
dc.identifier.issn | 0047-6374 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/203367 | |
dc.description.abstract | [EN] The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 <= AUC <= 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 <= p <= 0.18) and were predictive of death for pre-frail and frail participants (0.001 <= p <= 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach. | es_ES |
dc.description.sponsorship | This work was supported by the following grants: Grant PID2020-113839RB-I00 funded by MCIN/AEI/10.13039/501100011033 to C.B. DM acknowledges financial support from the Conselleria d ' Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2018/021 and AEST/2019/037) . | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113839RB-I00/ES/EFECTO DEL TRATAMIENTO CON VESICULAS EXTRACELULARES DE CELULAS MADRE DE GRASA DE RATONES JOVENES SOBRE PARAMETROS DE ENVEJECIMIENTO Y FRAGILIDAD EN RATONES DE EDAD AVANZADA/ | |
dc.relation | info:eu-repo/grantAgreement/GVA//AEST%2F2018%2F021/ | |
dc.relation | info:eu-repo/grantAgreement/GVA//AEST%2F2019%2F037/ | |
dc.relation.ispartof | Mechanisms of Ageing and Development | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Frailty | es_ES |
dc.subject | Muscle | es_ES |
dc.subject | Ultrasound | es_ES |
dc.subject | Machine-learning | es_ES |
dc.subject | Texture analysis | es_ES |
dc.subject | Image biomarkers | es_ES |
dc.subject.classification | TECNOLOGIA ELECTRONICA | es_ES |
dc.title | Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.mad.2023.111860 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.description.bibliographicCitation | Mirón-Mombiela, R.; Ruiz-España, S.; Moratal, D.; Borrás, C. (2023). Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques. Mechanisms of Ageing and Development. 215. https://doi.org/10.1016/j.mad.2023.111860 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.mad.2023.111860 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 215 | es_ES |
dc.identifier.pmid | 37666473 | es_ES |
dc.relation.pasarela | S\510430 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | |
dc.contributor.funder | Generalitat Valenciana |