Cutaneous melanoma (CM) incidence has dramatically increased in the last years. Early diagnosis is of paramount importance in terms of prognosis. Artificial Intelligence (AI) tools are being proposed for clinicians and pathologists as an adjunct support in the diagnostic process. We described herein an overview of the most important parameters that a potential AI tool should take into consideration in histopathology to evaluate a skin lesion. First of all, recognition of a melanocytic or non-melanocytic nature. Furthermore, melanocytic lesions should be stratified according to at least four parameters: silhouette and asymmetry; identification and spatial distribution of the cells; mitosis count; presence of ulceration. According to the number of parameters the AI tools might stratify the risk of CM and prioritize the pathologist’s work.

Querzoli, G., Veronesi, G., Corti, B., Nottegar, A., Dika, E. (2023). Basic Elements of Artificial Intelligence Tools in the Diagnosis of Cutaneous Melanoma. CRITICAL REVIEWS IN ONCOGENESIS, 28(3), 37-41 [10.1615/critrevoncog.2023050220].

Basic Elements of Artificial Intelligence Tools in the Diagnosis of Cutaneous Melanoma

Querzoli, Giulia;Dika, Emi
2023

Abstract

Cutaneous melanoma (CM) incidence has dramatically increased in the last years. Early diagnosis is of paramount importance in terms of prognosis. Artificial Intelligence (AI) tools are being proposed for clinicians and pathologists as an adjunct support in the diagnostic process. We described herein an overview of the most important parameters that a potential AI tool should take into consideration in histopathology to evaluate a skin lesion. First of all, recognition of a melanocytic or non-melanocytic nature. Furthermore, melanocytic lesions should be stratified according to at least four parameters: silhouette and asymmetry; identification and spatial distribution of the cells; mitosis count; presence of ulceration. According to the number of parameters the AI tools might stratify the risk of CM and prioritize the pathologist’s work.
2023
Querzoli, G., Veronesi, G., Corti, B., Nottegar, A., Dika, E. (2023). Basic Elements of Artificial Intelligence Tools in the Diagnosis of Cutaneous Melanoma. CRITICAL REVIEWS IN ONCOGENESIS, 28(3), 37-41 [10.1615/critrevoncog.2023050220].
Querzoli, Giulia; Veronesi, Giulia; Corti, Barbara; Nottegar, Alessia; Dika, Emi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1004785
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