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Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience
The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as b...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407151/ https://www.ncbi.nlm.nih.gov/pubmed/36010322 http://dx.doi.org/10.3390/diagnostics12081972 |
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author | Cazzato, Gerardo Massaro, Alessandro Colagrande, Anna Lettini, Teresa Cicco, Sebastiano Parente, Paola Nacchiero, Eleonora Lospalluti, Lucia Cascardi, Eliano Giudice, Giuseppe Ingravallo, Giuseppe Resta, Leonardo Maiorano, Eugenio Vacca, Angelo |
author_facet | Cazzato, Gerardo Massaro, Alessandro Colagrande, Anna Lettini, Teresa Cicco, Sebastiano Parente, Paola Nacchiero, Eleonora Lospalluti, Lucia Cascardi, Eliano Giudice, Giuseppe Ingravallo, Giuseppe Resta, Leonardo Maiorano, Eugenio Vacca, Angelo |
author_sort | Cazzato, Gerardo |
collection | PubMed |
description | The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma. |
format | Online Article Text |
id | pubmed-9407151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94071512022-08-26 Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience Cazzato, Gerardo Massaro, Alessandro Colagrande, Anna Lettini, Teresa Cicco, Sebastiano Parente, Paola Nacchiero, Eleonora Lospalluti, Lucia Cascardi, Eliano Giudice, Giuseppe Ingravallo, Giuseppe Resta, Leonardo Maiorano, Eugenio Vacca, Angelo Diagnostics (Basel) Article The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma. MDPI 2022-08-15 /pmc/articles/PMC9407151/ /pubmed/36010322 http://dx.doi.org/10.3390/diagnostics12081972 Text en © 2022 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 Lettini, Teresa Cicco, Sebastiano Parente, Paola Nacchiero, Eleonora Lospalluti, Lucia Cascardi, Eliano Giudice, Giuseppe Ingravallo, Giuseppe Resta, Leonardo Maiorano, Eugenio Vacca, Angelo Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience |
title | Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience |
title_full | Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience |
title_fullStr | Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience |
title_full_unstemmed | Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience |
title_short | Dermatopathology of Malignant Melanoma in the Era of Artificial Intelligence: A Single Institutional Experience |
title_sort | dermatopathology of malignant melanoma in the era of artificial intelligence: a single institutional experience |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407151/ https://www.ncbi.nlm.nih.gov/pubmed/36010322 http://dx.doi.org/10.3390/diagnostics12081972 |
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