Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros











Base de dados
Intervalo de ano de publicação
1.
J Invest Dermatol ; 142(1): 97-103, 2022 01.
Artigo em Inglês | MEDLINE | ID: mdl-34265329

RESUMO

Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the United States. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied for histopathological confirmation, thus lowering the specificity of noninvasive BCC diagnosis. Recently, reflectance confocal microscopy, a noninvasive diagnostic technique that can image skin lesions at cellular level resolution, has shown to improve specificity in BCC diagnosis and reduced the number needed to biopsy by 2-3 times. In this study, we developed and evaluated a deep learning-based artificial intelligence model to automatically detect BCC in reflectance confocal microscopy images. The proposed model achieved an area under the curve for the receiver operator characteristic curve of 89.7% (stack level) and 88.3% (lesion level), a performance on par with that of reflectance confocal microscopy experts. Furthermore, the model achieved an area under the curve of 86.1% on a held-out test set from international collaborators, demonstrating the reproducibility and generalizability of the proposed automated diagnostic approach. These results provide a clear indication that the clinical deployment of decision support systems for the detection of BCC in reflectance confocal microscopy images has the potential for optimizing the evaluation and diagnosis of patients with skin cancer.


Assuntos
Carcinoma Basocelular/diagnóstico , Aprendizado Profundo/normas , Neoplasias Cutâneas/diagnóstico , Adulto , Idoso , Idoso de 80 Anos ou mais , Inteligência Artificial , Automação , Biópsia , Dermoscopia/métodos , Feminino , Humanos , Masculino , Microscopia Confocal , Pessoa de Meia-Idade , Modelos Biológicos , Exame Físico , Reprodutibilidade dos Testes
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA