Carmen Serrano1, Rafael Boloix-Tortosa1, Tomás Gómez-Cía2, Begoña Acha3. 1. Servicio de Cirugía Plástica y Grandes Quemados, Hospitales U. Virgen del Rocío, Seville, Spain. 2. Departamento de Teoría de la Señal y Comunicaciones, University of Seville, Spain. 3. Servicio de Cirugía Plástica y Grandes Quemados, Hospitales U. Virgen del Rocío, Seville, Spain. Electronic address: cserrano@us.es.
Abstract
PURPOSE: In this paper an automatic system to diagnose burn depths based on colour digital photographs is presented. JUSTIFICATION: There is a low success rate in the determination of burn depth for inexperienced surgeons (around 50%), which rises to the range from 64 to 76% for experienced surgeons. In order to establish the first treatment, which is crucial for the patient evolution, the determination of the burn depth is one of the main steps. As the cost of maintaining a Burn Unit is very high, it would be desirable to have an automatic system to give a first assessment in local medical centres or at the emergency, where there is a lack of specialists. METHOD: To this aim a psychophysical experiment to determine the physical characteristics that physicians employ to diagnose a burn depth is described. A Multidimensional Scaling Analysis (MDS) is then applied to the data obtained from the experiment in order to identify these physical features. Subsequently, these characteristics are translated into mathematical features. Finally, via a classifier (Support Vector Machine) and a feature selection method, the discriminant power of these mathematical features to distinguish among burn depths is analysed, and the subset of features that better estimates the burn depth is selected. RESULTS: A success rate of 79.73% was obtained when burns were classified as those which needed grafts and those which did not. CONCLUSIONS: Results validate the ability of the features extracted from the psychophysical experiment to classify burns into their depths.
PURPOSE: In this paper an automatic system to diagnose burn depths based on colour digital photographs is presented. JUSTIFICATION: There is a low success rate in the determination of burn depth for inexperienced surgeons (around 50%), which rises to the range from 64 to 76% for experienced surgeons. In order to establish the first treatment, which is crucial for the patient evolution, the determination of the burn depth is one of the main steps. As the cost of maintaining a Burn Unit is very high, it would be desirable to have an automatic system to give a first assessment in local medical centres or at the emergency, where there is a lack of specialists. METHOD: To this aim a psychophysical experiment to determine the physical characteristics that physicians employ to diagnose a burn depth is described. A Multidimensional Scaling Analysis (MDS) is then applied to the data obtained from the experiment in order to identify these physical features. Subsequently, these characteristics are translated into mathematical features. Finally, via a classifier (Support Vector Machine) and a feature selection method, the discriminant power of these mathematical features to distinguish among burn depths is analysed, and the subset of features that better estimates the burn depth is selected. RESULTS: A success rate of 79.73% was obtained when burns were classified as those which needed grafts and those which did not. CONCLUSIONS: Results validate the ability of the features extracted from the psychophysical experiment to classify burns into their depths.
Authors: Carolina Maria Costa de Oliveira Souza; Clayton Fernandes de Souza; Bassam Felipe Mogharbel; Ana Carolina Irioda; Celia Regina Cavichiolo Franco; Maria Rita Sierakowski; Katherine Athayde Teixeira de Carvalho Journal: Int J Nanomedicine Date: 2021-02-05
Authors: Che Wei Chang; Feipei Lai; Mesakh Christian; Yu Chun Chen; Ching Hsu; Yo Shen Chen; Dun Hao Chang; Tyng Luen Roan; Yen Che Yu Journal: JMIR Med Inform Date: 2021-12-02