Lipodistrofia generalizada congênita

Filho, Paulo P. Figueiredo; Val, Alexandre Costa; Diamante, Rosângela; Cunha, Cristiane F.; Norton, Rocksane de Carvalho; Lamounier, Joel Alves; Leão, Ennio

doi:10.1590/s0021-75572004000500015

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…It is estimated that in Brazil there are approximately 100 CGL patients being followed up in specialized services, however, not all cases are published 20 , 21 and access to images is very limited. The relatively small number of patients and the wide range of age represent a limitation and a challenge in machine learning.…”

Section: Discussionmentioning

confidence: 99%

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO

Olegario

Neto

Barbosa

et al. 2023

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO

Olegario

Neto

Barbosa

et al. 2023

Sci Rep

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Berardinelli‐Seip Congenital Lipodystrophy

Maldergem¹

2008

Encyclopedia of Genetics, Genomics, Proteomics and Informatics

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Experiência de pessoas que vivem com a Síndrome de Berardinelli-Seip no Nordeste brasileiro

Damasceno

Figueiredo

França

et al. 2018

Ciênc. saúde coletiva

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Resumo O artigo analisa a experiência de pessoas que vivem com a Síndrome de Berardinelli-Seip no Nordeste brasileiro. Este estudo qualitativo foi desenvolvido com onze interlocutores, sendo nove pessoas vivendo com a síndrome e duas mães. Para coligir as informações, utilizaram-se observação participante, caracterização social e entrevistas semiestruturadas. Os dados foram analisados por meio da técnica de codificação temática. Emergiram duas categorias: (1) ‘o segredo é fechar a boca’: gerenciamento da alimentação na vida cotidiana; e (2) ‘ah, é uma travesti?’ Corpo, gênero e masculinização. Concluiu-se que na experiência dos interlocutores seus agenciamentos e criatividade se traduziram em estratégias para gerenciamento da alimentação que integravam gostos, valores, hábitos, prescrições biomédicas e prazeres envolvidos em situações de comensalidade. No que tange à corporeidade, evidenciou-se que as representações e as experiências com o corpo apresentam desigualdades de gênero, na medida em que a mulher passa a ser alvo privilegiado de estigmas, preconceitos e discriminação na vida adulta.

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Lipodistrofia generalizada congênita

Cited by 3 publications

References 15 publications

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO

Berardinelli‐Seip Congenital Lipodystrophy

Experiência de pessoas que vivem com a Síndrome de Berardinelli-Seip no Nordeste brasileiro

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