Artificial Intelligence in the Non-Invasive Detection of Melanoma
Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/14/12/1602 |
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| author | Banu İsmail Mendi Kivanc Kose Lauren Fleshner Richard Adam Bijan Safai Banu Farabi Mehmet Fatih Atak |
| author_facet | Banu İsmail Mendi Kivanc Kose Lauren Fleshner Richard Adam Bijan Safai Banu Farabi Mehmet Fatih Atak |
| author_sort | Banu İsmail Mendi |
| collection | DOAJ |
| description | Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices. |
| format | Article |
| id | doaj-art-71f5a4f53ead4c5a8d2317ca7dfd4ab8 |
| institution | DOAJ |
| issn | 2075-1729 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-71f5a4f53ead4c5a8d2317ca7dfd4ab82025-08-20T02:57:14ZengMDPI AGLife2075-17292024-12-011412160210.3390/life14121602Artificial Intelligence in the Non-Invasive Detection of MelanomaBanu İsmail Mendi0Kivanc Kose1Lauren Fleshner2Richard Adam3Bijan Safai4Banu Farabi5Mehmet Fatih Atak6Department of Dermatology, Niğde Ömer Halisdemir University, Niğde 51000, TurkeyDermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USASchool of Medicine, New York Medical College, Valhalla, NY 10595, USADermatology Department, NYC Health + Hospital/Metropolitan, New York, NY 10029, USASkin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.https://www.mdpi.com/2075-1729/14/12/1602artificial intelligencealgorithmsmelanomaskin cancerdermoscopynon-invasive skin imaging |
| spellingShingle | Banu İsmail Mendi Kivanc Kose Lauren Fleshner Richard Adam Bijan Safai Banu Farabi Mehmet Fatih Atak Artificial Intelligence in the Non-Invasive Detection of Melanoma Life artificial intelligence algorithms melanoma skin cancer dermoscopy non-invasive skin imaging |
| title | Artificial Intelligence in the Non-Invasive Detection of Melanoma |
| title_full | Artificial Intelligence in the Non-Invasive Detection of Melanoma |
| title_fullStr | Artificial Intelligence in the Non-Invasive Detection of Melanoma |
| title_full_unstemmed | Artificial Intelligence in the Non-Invasive Detection of Melanoma |
| title_short | Artificial Intelligence in the Non-Invasive Detection of Melanoma |
| title_sort | artificial intelligence in the non invasive detection of melanoma |
| topic | artificial intelligence algorithms melanoma skin cancer dermoscopy non-invasive skin imaging |
| url | https://www.mdpi.com/2075-1729/14/12/1602 |
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