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Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology

Malignant melanoma (MM) is the “great mime” of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed...

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Autores principales: Cazzato, Gerardo, Massaro, Alessandro, Colagrande, Anna, Trilli, Irma, Ingravallo, Giuseppe, Casatta, Nadia, Lupo, Carmelo, Ronchi, Andrea, Franco, Renato, Maiorano, Eugenio, Vacca, Angelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378276/
https://www.ncbi.nlm.nih.gov/pubmed/37504312
http://dx.doi.org/10.3390/curroncol30070452
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author Cazzato, Gerardo
Massaro, Alessandro
Colagrande, Anna
Trilli, Irma
Ingravallo, Giuseppe
Casatta, Nadia
Lupo, Carmelo
Ronchi, Andrea
Franco, Renato
Maiorano, Eugenio
Vacca, Angelo
author_facet Cazzato, Gerardo
Massaro, Alessandro
Colagrande, Anna
Trilli, Irma
Ingravallo, Giuseppe
Casatta, Nadia
Lupo, Carmelo
Ronchi, Andrea
Franco, Renato
Maiorano, Eugenio
Vacca, Angelo
author_sort Cazzato, Gerardo
collection PubMed
description Malignant melanoma (MM) is the “great mime” of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process.
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spelling pubmed-103782762023-07-29 Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology Cazzato, Gerardo Massaro, Alessandro Colagrande, Anna Trilli, Irma Ingravallo, Giuseppe Casatta, Nadia Lupo, Carmelo Ronchi, Andrea Franco, Renato Maiorano, Eugenio Vacca, Angelo Curr Oncol Article Malignant melanoma (MM) is the “great mime” of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process. MDPI 2023-06-23 /pmc/articles/PMC10378276/ /pubmed/37504312 http://dx.doi.org/10.3390/curroncol30070452 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cazzato, Gerardo
Massaro, Alessandro
Colagrande, Anna
Trilli, Irma
Ingravallo, Giuseppe
Casatta, Nadia
Lupo, Carmelo
Ronchi, Andrea
Franco, Renato
Maiorano, Eugenio
Vacca, Angelo
Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology
title Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology
title_full Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology
title_fullStr Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology
title_full_unstemmed Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology
title_short Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology
title_sort artificial intelligence applied to a first screening of naevoid melanoma: a new use of fast random forest algorithm in dermatopathology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378276/
https://www.ncbi.nlm.nih.gov/pubmed/37504312
http://dx.doi.org/10.3390/curroncol30070452
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