Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.
J Dtsch Dermatol Ges
; 21(11): 1329-1337, 2023 11.
Article
en En
| MEDLINE
| ID: mdl-37814387
ABSTRACT
BACKGROUND:
Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS ANDMETHODS:
In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network.RESULTS:
In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established.CONCLUSIONS:
AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Cutáneas
/
Carcinoma Basocelular
/
Carcinoma de Células Escamosas
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Dtsch Dermatol Ges
Asunto de la revista:
DERMATOLOGIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Alemania