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Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting

Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of...

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Autores principales: Giavina-Bianchi, Mara, de Sousa, Raquel Machado, Paciello, Vitor Zago de Almeida, Vitor, William Gois, Okita, Aline Lissa, Prôa, Renata, Severino, Gian Lucca dos Santos, Schinaid, Anderson Alves, Espírito Santo, Rafael, Machado, Birajara Soares
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457457/
https://www.ncbi.nlm.nih.gov/pubmed/34550970
http://dx.doi.org/10.1371/journal.pone.0257006
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author Giavina-Bianchi, Mara
de Sousa, Raquel Machado
Paciello, Vitor Zago de Almeida
Vitor, William Gois
Okita, Aline Lissa
Prôa, Renata
Severino, Gian Lucca dos Santos
Schinaid, Anderson Alves
Espírito Santo, Rafael
Machado, Birajara Soares
author_facet Giavina-Bianchi, Mara
de Sousa, Raquel Machado
Paciello, Vitor Zago de Almeida
Vitor, William Gois
Okita, Aline Lissa
Prôa, Renata
Severino, Gian Lucca dos Santos
Schinaid, Anderson Alves
Espírito Santo, Rafael
Machado, Birajara Soares
author_sort Giavina-Bianchi, Mara
collection PubMed
description Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists’ diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention.
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spelling pubmed-84574572021-09-23 Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting Giavina-Bianchi, Mara de Sousa, Raquel Machado Paciello, Vitor Zago de Almeida Vitor, William Gois Okita, Aline Lissa Prôa, Renata Severino, Gian Lucca dos Santos Schinaid, Anderson Alves Espírito Santo, Rafael Machado, Birajara Soares PLoS One Research Article Skin cancer is currently the most common type of cancer among Caucasians. The increase in life expectancy, along with new diagnostic tools and treatments for skin cancer, has resulted in unprecedented changes in patient care and has generated a great burden on healthcare systems. Early detection of skin tumors is expected to reduce this burden. Artificial intelligence (AI) algorithms that support skin cancer diagnoses have been shown to perform at least as well as dermatologists’ diagnoses. Recognizing the need for clinically and economically efficient means of diagnosing skin cancers at early stages in the primary care attention, we developed an efficient computer-aided diagnosis (CAD) system to be used by primary care physicians (PCP). Additionally, we developed a smartphone application with a protocol for data acquisition (i.e., photographs, demographic data and short clinical histories) and AI algorithms for clinical and dermoscopic image classification. For each lesion analyzed, a report is generated, showing the image of the suspected lesion and its respective Heat Map; the predicted probability of the suspected lesion being melanoma or malignant; the probable diagnosis based on that probability; and a suggestion on how the lesion should be managed. The accuracy of the dermoscopy model for melanoma was 89.3%, and for the clinical model, 84.7% with 0.91 and 0.89 sensitivity and 0.89 and 0.83 specificity, respectively. Both models achieved an area under the curve (AUC) above 0.9. Our CAD system can screen skin cancers to guide lesion management by PCPs, especially in the contexts where the access to the dermatologist can be difficult or time consuming. Its use can enable risk stratification of lesions and/or patients and dramatically improve timely access to specialist care for those requiring urgent attention. Public Library of Science 2021-09-22 /pmc/articles/PMC8457457/ /pubmed/34550970 http://dx.doi.org/10.1371/journal.pone.0257006 Text en © 2021 Giavina-Bianchi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Giavina-Bianchi, Mara
de Sousa, Raquel Machado
Paciello, Vitor Zago de Almeida
Vitor, William Gois
Okita, Aline Lissa
Prôa, Renata
Severino, Gian Lucca dos Santos
Schinaid, Anderson Alves
Espírito Santo, Rafael
Machado, Birajara Soares
Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting
title Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting
title_full Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting
title_fullStr Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting
title_full_unstemmed Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting
title_short Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting
title_sort implementation of artificial intelligence algorithms for melanoma screening in a primary care setting
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457457/
https://www.ncbi.nlm.nih.gov/pubmed/34550970
http://dx.doi.org/10.1371/journal.pone.0257006
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