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Tipo: Artigo de Periódico
Título : Identifying congenital generalized lipodystrophy using deep learning‑DEEPLIPO
Autor : Olegario, Natália Bitar da Cunha
Cunha Neto, Joel Sotero da
Barbosa, Paulo Cirillo Souza
Pinheiro, Plácido Rogério
Landim, Pedro Lino Azevêdo
Montenegro, Ana Paula Dias Rangel
Fernandes, Virginia Oliveira
Albuquerque, Victor Hugo Costa de
Duarte, João Batista Furlan
Lima, Grayce Ellen da Cruz Paiva
Montenegro Junior, Renan Magalhães
Fecha de publicación : 2023
Editorial : Scientific Reports
Citación : OLEGARIO, Natália Bitar da Cunha; CUNHA NETO, Joel Sotero da; BARBOSA, Paulo Cirillo Souza; PINHEIRO, Plácido Rogério; LANDIM, Pedro Lino Azevêdo; MONTENEGRO, Ana Paula Dias Rangel; FERNANDES, Virginia Oliveira; ALBUQUERQUE, Victor Hugo Costa de; DUARTE, João Batista Furlan; LIMA, Grayce Ellen da Cruz Paiva; MONTENEGRO JÚNIOR, Renan Magalhães. Identifying congenital generalized lipodystrophy using deep learning‑DEEPLIPO. Scientific Reports, [s.l.], v. 13, p. 2176, 2023.
Abstract: Congenital Generalized Lipodystrophy (CGL) is a rare autosomal recessive disease characterized by near complete absence of functional adipose tissue from birth. CGL diagnosis can be based on clinical data including acromegaloid features, acanthosis nigricans, reduction of total body fat, muscular hypertrophy, and protrusion of the umbilical scar. The identification and knowledge of CGL by the health care professionals is crucial once it is associated with severe and precocious cardiometabolic complications and poor outcome. Image processing by deep learning algorithms have been implemented in medicine and the application into routine clinical practice is feasible. Therefore, the aim of this study was to identify congenital generalized lipodystrophy phenotype using deep learning. A deep learning approach model using convolutional neural network was presented as a detailed experiment with evaluation steps undertaken to test the effectiveness. These experiments were based on CGL patient’s photography database. The dataset consists of two main categories (training and testing) and three subcategories containing photos of patients with CGL, individuals with malnutrition and eutrophic individuals with athletic build. A total of 337 images of individuals of different ages, children and adults were carefully chosen from internet open access database and photographic records of stored images of medical records of a reference center for inherited lipodystrophies. For validation, the dataset was partitioned into four parts, keeping the same proportion of the three subcategories in each part. The fourfold cross-validation technique was applied, using 75% (3 parts) of the data as training and 25% (1 part) as a test. Following the technique, four tests were performed, changing the parts that were used as training and testing until each part was used exactly once as validation data. As a result, a mean accuracy, sensitivity, and specificity were obtained with values of [90.85 ± 2.20%], [90.63 ± 3.53%] and [91.41 ± 1.10%], respectively. In conclusion, this study presented for the first time a deep learning model able to identify congenital generalized lipodystrophy phenotype with excellent accuracy, sensitivity and specificity, possibly being a strategic tool for detecting this disease
URI : http://www.repositorio.ufc.br/handle/riufc/73435
ISSN : 2045-2322
Derechos de acceso: Acesso Aberto
Aparece en las colecciones: DEEL - Artigos publicados em revista científica

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