Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients

Introduction: Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. Methods: Patients were recruited fo...

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Veröffentlicht in:Journal of cutaneous medicine and surgery 2024-01, Vol.28 (1), p.37-43
Hauptverfasser: Crawford, Madeleine E., Kamali, Kiyana, Dorey, Rachel A., MacIntyre, Olivia C., Cleminson, Kristyna, MacGillivary, Michael L., Green, Peter J., Langley, Richard G., Purdy, Kerri S., DeCoste, Ryan C., Gruchy, Jennette R., Pasternak, Sylvia, Oakley, Amanda, Hull, Peter R.
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container_end_page 43
container_issue 1
container_start_page 37
container_title Journal of cutaneous medicine and surgery
container_volume 28
creator Crawford, Madeleine E.
Kamali, Kiyana
Dorey, Rachel A.
MacIntyre, Olivia C.
Cleminson, Kristyna
MacGillivary, Michael L.
Green, Peter J.
Langley, Richard G.
Purdy, Kerri S.
DeCoste, Ryan C.
Gruchy, Jennette R.
Pasternak, Sylvia
Oakley, Amanda
Hull, Peter R.
description Introduction: Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. Methods: Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. Results: Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI’s ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images. Conclusion: AI, used in this study, may serve as a practical skin cancer screening aid. While it does have technical and diagnostic limitations, its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.
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In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. Methods: Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. Results: Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI’s ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images. Conclusion: AI, used in this study, may serve as a practical skin cancer screening aid. 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In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. Methods: Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. Results: Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI’s ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images. Conclusion: AI, used in this study, may serve as a practical skin cancer screening aid. While it does have technical and diagnostic limitations, its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.</description><subject>Artificial intelligence</subject><subject>Dermatology</subject><subject>Medical screening</subject><subject>Melanoma</subject><subject>Original</subject><subject>Skin cancer</subject><issn>1203-4754</issn><issn>1615-7109</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><recordid>eNp1kV9LHDEUxUNpqVb7AXwpgb74MjZ3MvkzTyKiVlAUXZ9DJnuzRmYTTWaFfvtmWWtbS58SuL9zck8OIXvADgCU-gYt450SXcuhBdlL9Y5sgwTRKGD9-3qv82YNbJFPpTwwxgBE95FscQ1CylZvk9ldCXFBj_IUfHDBjvQ8TjiOYYHRIbWFWnqJo41paemty4hxzc9SGmmI9BZH39ygx5xxTq_tFDBOZZd88HYs-Pnl3CF3pyez4-_NxdXZ-fHRReM6qaaGS4ZSSSv6oZODnzuUANjxwbd20OC5HpjwvdDKSeYsYm_nuh-E7rmyTDu-Qw43vo-rYYlVH6dsR_OYw9LmHybZYP6exHBvFunZ1O9humWsOuy_OOT0tMIymWUorua3EdOqmLZfk53kqqJf36APaZVjzVcpzpVgnYBKwYZyOZWS0b9uA8ysSzP_lFY1X_6M8ar41VIFDjZAsQv8_ez_HX8CLyGe5A</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Crawford, Madeleine E.</creator><creator>Kamali, Kiyana</creator><creator>Dorey, Rachel A.</creator><creator>MacIntyre, Olivia C.</creator><creator>Cleminson, Kristyna</creator><creator>MacGillivary, Michael L.</creator><creator>Green, Peter J.</creator><creator>Langley, Richard G.</creator><creator>Purdy, Kerri S.</creator><creator>DeCoste, Ryan C.</creator><creator>Gruchy, Jennette R.</creator><creator>Pasternak, Sylvia</creator><creator>Oakley, Amanda</creator><creator>Hull, Peter R.</creator><general>SAGE Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>AFRWT</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3896-9373</orcidid><orcidid>https://orcid.org/0000-0001-8400-6714</orcidid><orcidid>https://orcid.org/0000-0001-8339-257X</orcidid></search><sort><creationdate>20240101</creationdate><title>Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients</title><author>Crawford, Madeleine E. ; 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Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of cutaneous medicine and surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Crawford, Madeleine E.</au><au>Kamali, Kiyana</au><au>Dorey, Rachel A.</au><au>MacIntyre, Olivia C.</au><au>Cleminson, Kristyna</au><au>MacGillivary, Michael L.</au><au>Green, Peter J.</au><au>Langley, Richard G.</au><au>Purdy, Kerri S.</au><au>DeCoste, Ryan C.</au><au>Gruchy, Jennette R.</au><au>Pasternak, Sylvia</au><au>Oakley, Amanda</au><au>Hull, Peter R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients</atitle><jtitle>Journal of cutaneous medicine and surgery</jtitle><addtitle>J Cutan Med Surg</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>28</volume><issue>1</issue><spage>37</spage><epage>43</epage><pages>37-43</pages><issn>1203-4754</issn><eissn>1615-7109</eissn><abstract>Introduction: Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. Methods: Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. Results: Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI’s ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images. Conclusion: AI, used in this study, may serve as a practical skin cancer screening aid. 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source SAGE Complete A-Z List
subjects Artificial intelligence
Dermatology
Medical screening
Melanoma
Original
Skin cancer
title Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients
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