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Publications

Journal Article Epublish

PubMed: 40338232 DOI: 10.3390/diagnostics15080988

Automatic Analysis of Ultrasound Images to Estimate Subcutaneous and Visceral Fat and Muscle Tissue in Patients with Suspected Malnutrition.

Diagnostics (Basel) 15(8)

Authors: Antonio Cuesta-Vargas, José María Arjona-Caballero, Gabriel Olveira, Daniel de Luis Román, Diego Bellido-Guerrero, Jose Manuel García-Almeida

Affiliations

  • Clinimetria Research Group, Department of Physiotherapy, Faculty of Health Sciences, Universidad de Málaga, 29071 Malaga, Spain.
  • Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, 29590 Malaga, Spain.
  • Department of Endocrinology and Nutrition, Hospital Regional Universitario de Málaga, 29010 Malaga, Spain.
  • Department of Medicine and Dermatology, Faculty of Medicine, Universidad de Malaga, 29010 Malaga, Spain.
  • Centro de Investigación Biomédica en Red (CIBER) de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 29010 Málaga, Spain.
  • Endocrinology and Nutrition Department, Clinical University Hospital of Valladolid, 47003 Valladolid, Spain.
  • Investigation Centre Endocrinology and Nutrition, Faculty of Medicine, University of Valladolid, 47003 Valladolid, Spain.
  • Department of Endocrinology and Nutrition, Complejo Hospitalario Universitario de Ferrol (CHUF), 15405 Ferrol, Spain.
  • Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, 29590 Malaga, Spain. Department of Medicine and Dermatology, Faculty of Medicine, Universidad de Malaga, 29010 Malaga, Spain. Centro de Investigación Biomédica en Red (CIBER) de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 29010 Málaga, Spain. Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, 29010 Malaga, Spain. Department of Endocrinology and Nutrition, Quironsalud Málaga Hospital, 29009 Malaga, Spain.

Abstract

Malnutrition is a prevalent condition associated with adverse health outcomes, requiring the accurate assessment of muscle composition and fat distribution. This study presents a novel method for the automatic analysis of ultrasound images to estimate subcutaneous and visceral fat, as well as muscle, in patients with suspected malnutrition. The proposed system utilizes computer vision techniques to segment regions of interest (ROIs), calculate relevant variables, and store data for clinical evaluation. Unlike traditional segmentation methods that rely solely on thresholding or pre-defined masks, our method employs an iterative hierarchical approach to refine contour detection and improve localization accuracy. A dataset of abdominal and leg ultrasound images, captured in both longitudinal and transversal planes, was analyzed. Results showed higher precision for longitudinal scans compared to transversal scans, particularly for length-related variables, with the Y-axis Vastus intermediate achieving a precision of 92.87%. However, area-based measurements demonstrated lower precision due to differences between manual adjustments by experts and automatic geometric approximations. These findings highlight the system's potential for clinical use while emphasizing the need for further algorithmic refinements to improve precision in area calculations.

MeSH

Muscles, Malnutrition, Ultrasonography, Machine Learning, Tissues