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The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review.
Widaatalla, Y; Wolswijk, T; Adan, F; Hillen, L M; Woodruff, H C; Halilaj, I; Ibrahim, A; Lambin, P; Mosterd, K.
Afiliación
  • Widaatalla Y; The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
  • Wolswijk T; Department of Dermatology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands.
  • Adan F; Department of Dermatology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands.
  • Hillen LM; Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands.
  • Woodruff HC; The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
  • Halilaj I; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands.
  • Ibrahim A; The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
  • Lambin P; Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA.
  • Mosterd K; The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
J Eur Acad Dermatol Venereol ; 37(6): 1160-1167, 2023 Jun.
Article en En | MEDLINE | ID: mdl-36785993
Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Cutáneas / Carcinoma Basocelular Tipo de estudio: Diagnostic_studies / Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Eur Acad Dermatol Venereol Asunto de la revista: DERMATOLOGIA / DOENCAS SEXUALMENTE TRANSMISSIVEIS Año: 2023 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Cutáneas / Carcinoma Basocelular Tipo de estudio: Diagnostic_studies / Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Eur Acad Dermatol Venereol Asunto de la revista: DERMATOLOGIA / DOENCAS SEXUALMENTE TRANSMISSIVEIS Año: 2023 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Reino Unido