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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2021
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oai:doaj.org-article:dcda1b413b5041558f4819a1167e1ff82021-12-02T20:14:18ZImplementation of artificial intelligence algorithms for melanoma screening in a primary care setting.1932-620310.1371/journal.pone.0257006https://doaj.org/article/dcda1b413b5041558f4819a1167e1ff82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257006https://doaj.org/toc/1932-6203Skin 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.Mara Giavina-BianchiRaquel Machado de SousaVitor Zago de Almeida PacielloWilliam Gois VitorAline Lissa OkitaRenata PrôaGian Lucca Dos Santos SeverinoAnderson Alves SchinaidRafael Espírito SantoBirajara Soares MachadoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257006 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Mara Giavina-Bianchi Raquel Machado de Sousa Vitor Zago de Almeida Paciello William Gois Vitor Aline Lissa Okita Renata Prôa Gian Lucca Dos Santos Severino Anderson Alves Schinaid Rafael Espírito Santo Birajara Soares Machado Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting. |
| 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. |
| format |
article |
| author |
Mara Giavina-Bianchi Raquel Machado de Sousa Vitor Zago de Almeida Paciello William Gois Vitor Aline Lissa Okita Renata Prôa Gian Lucca Dos Santos Severino Anderson Alves Schinaid Rafael Espírito Santo Birajara Soares Machado |
| author_facet |
Mara Giavina-Bianchi Raquel Machado de Sousa Vitor Zago de Almeida Paciello William Gois Vitor Aline Lissa Okita Renata Prôa Gian Lucca Dos Santos Severino Anderson Alves Schinaid Rafael Espírito Santo Birajara Soares Machado |
| author_sort |
Mara Giavina-Bianchi |
| title |
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_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_sort |
implementation of artificial intelligence algorithms for melanoma screening in a primary care setting. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/dcda1b413b5041558f4819a1167e1ff8 |
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