The potential of using artificial intelligence to improve skin cancer diagnoses in Hawai‘i’s multiethnic population
| Autor: | Christopher Lum, Kevin Cassel, John A. Shepherd, Janira M. Navarro Sanchez, Shane Y.P.K. Spencer, Terrilea Burnett, Mark L. Willingham |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Cancer Research Skin Neoplasms Adolescent Dermatology Article Hawaii Young Adult Artificial Intelligence medicine Humans Combined result Medical diagnosis Aged integumentary system business.industry Melanoma Cancer Middle Aged medicine.disease Multiethnic population Oncology Ethnic and Racial Minorities Female Artificial intelligence Skin cancer business Skin imaging |
| Zdroj: | Melanoma Res |
| ISSN: | 0960-8931 |
| DOI: | 10.1097/cmr.0000000000000779 |
| Popis: | Skin cancer remains the most commonly diagnosed cancer in the USA with more than 1 million new cases each year. Melanomas account for about 1% of all skin cancers and most skin cancer deaths. Multiethnic individuals whose skin is pigmented underestimate their risk for skin cancers and melanomas and may delay seeking a diagnosis. The use of artificial intelligence may help improve the diagnostic precision of dermatologists/physicians to identify malignant lesions. To validate our artificial intelligence's efficiency in distinguishing between images, we utilized 50 images obtained from our International Skin Imaging Collaboration dataset (n = 25) and pathologically confirmed lesions (n = 25). We compared the ability of our artificial intelligence to visually diagnose these 50 skin cancer lesions with a panel of three dermatologists. The artificial intelligence model better differentiated between melanoma vs. nonmelanoma with an area under the curve of 0.948. The three-panel member dermatologists correctly diagnosed a similar number of images (n = 35) as the artificial intelligence program (n = 34). Fleiss' kappa (ĸ) score for the raters and artificial intelligence indicated fair (0.247) agreement. However, the combined result of the dermatologists panel with the artificial intelligence assessments correctly identified 100% of the images from the test data set. Our artificial intelligence platform was able to utilize visual images to discriminate melanoma from nonmelanoma, using de-identified images. The combined results of the artificial intelligence with those of the dermatologists support the use of artificial intelligence as an efficient lesion assessment strategy to reduce time and expense in diagnoses to reduce delays in treatment. |
| Databáze: | OpenAIRE |
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