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Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.
Marchetti, Michael A; Liopyris, Konstantinos; Dusza, Stephen W; Codella, Noel C F; Gutman, David A; Helba, Brian; Kalloo, Aadi; Halpern, Allan C.
Afiliação
  • Marchetti MA; Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York. Electronic address: marchetm@mskcc.org.
  • Liopyris K; Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Dusza SW; Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Codella NCF; IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New York.
  • Gutman DA; Department of Neurology, Emory University School of Medicine, Atlanta, Georgia; Department of Psychiatry, Emory University School of Medicine, Atlanta, Georgia; Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.
  • Helba B; Kitware Inc, Clifton Park, New York.
  • Kalloo A; Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Halpern AC; Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
J Am Acad Dermatol ; 82(3): 622-627, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31306724
ABSTRACT

BACKGROUND:

Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.

OBJECTIVE:

To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma.

METHODS:

In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level.

RESULTS:

The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%.

LIMITATIONS:

Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata.

CONCLUSION:

Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.
Assuntos
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Interpretação de Imagem Assistida por Computador / Dermoscopia / Aprendizado Profundo / Melanoma Limite: Humanos País/Região como assunto: América do Norte / América do Sul / Ásia / Colômbia / Europa Idioma: Inglês Revista: J Am Acad Dermatol Ano de publicação: 2020 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Interpretação de Imagem Assistida por Computador / Dermoscopia / Aprendizado Profundo / Melanoma Limite: Humanos País/Região como assunto: América do Norte / América do Sul / Ásia / Colômbia / Europa Idioma: Inglês Revista: J Am Acad Dermatol Ano de publicação: 2020 Tipo de documento: Artigo