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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10378276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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