Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study

Dika, Emi; Curti, Nico; Giampieri, Enrico; Veronesi, Giulia; Misciali, Cosimo; Ricci, Costantino; Castellani, Gastone; Patrizi, Annalisa; Marcelli, Emanuela

doi:10.1016/j.prp.2022.154014

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Using this method, CNNs can find regions of interest to flag for potential melanoma diagnoses. One example of this is Dika et al, 6 wherein they compared melanoma region-of-interest (ROI) detection by CNNs versus expert dermatopathologists; the network and pathologists agreed 94% of the time. When compared with nevi identification, pathologists and deep learning networks agreed 100% of the time.…”

Section: Discussionmentioning

confidence: 99%

“…In the research setting, AI has shown utility as a tool for dermatopathologists diagnosing melanoma, highlighting a potential adjunct for improving patient outcomes. [6][7][8][9][10][11][12][13] As such, this review aims to explore AI-dermatopathology applications in melanoma differential diagnostics, prognosis prediction, and related personalized medicine decision-making. Frequently used AI terminology is defined in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence in Melanoma Dermatopathology: A Review of Literature

Neimy,

Helmy,

Snyder

et al. 2023

The American Journal of Dermatopathology

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Pathology serves as a promising field to integrate artificial intelligence into clinical practice as a powerful screening tool. Melanoma is a common skin cancer with high mortality and morbidity, requiring timely and accurate histopathologic diagnosis. This review explores applications of artificial intelligence in melanoma dermatopathology, including differential diagnostics, prognosis prediction, and personalized medicine decision-making.

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Section: Discussionmentioning

confidence: 99%