| Title |
Accuracy of a smartphone-based artificial intelligence application for classification of melanomas, melanocytic nevi, and seborrheic keratoses / |
| Authors |
Liutkus, Jokubas ; Kriukas, Arturas ; Stragyte, Dominyka ; Mazeika, Erikas ; Raudonis, Vidas ; Galetzka, Wolfgang ; Stang, Andreas ; Valiukeviciene, Skaidra |
| DOI |
10.3390/diagnostics13132139 |
| Full Text |
|
| Is Part of |
Diagnostics.. Basel : MDPI. 2023, vol. 13, iss. 13, art. no. 2139, p. 1-14.. ISSN 2075-4418 |
| Keywords [eng] |
artificial intelligence ; dermatoscopy ; melanoma ; nevus ; seborrheic keratosis ; smartphone |
| Abstract [eng] |
Current artificial intelligence algorithms can classify melanomas at a level equivalent to that of experienced dermatologists. The objective of this study was to assess the accuracy of a smartphone-based "You Only Look Once" neural network model for the classification of melanomas, melanocytic nevi, and seborrheic keratoses. The algorithm was trained using 59,090 dermatoscopic images. Testing was performed on histologically confirmed lesions: 32 melanomas, 35 melanocytic nevi, and 33 seborrheic keratoses. The results of the algorithm's decisions were compared with those of two skilled dermatologists and five beginners in dermatoscopy. The algorithm's sensitivity and specificity for melanomas were 0.88 (0.71-0.96) and 0.87 (0.76-0.94), respectively. The algorithm surpassed the beginner dermatologists, who achieved a sensitivity of 0.83 (0.77-0.87). For melanocytic nevi, the algorithm outclassed each group of dermatologists, attaining a sensitivity of 0.77 (0.60-0.90). The algorithm's sensitivity for seborrheic keratoses was 0.52 (0.34-0.69). The smartphone-based "You Only Look Once" neural network model achieved a high sensitivity and specificity in the classification of melanomas and melanocytic nevi with an accuracy similar to that of skilled dermatologists. However, a bigger dataset is required in order to increase the algorithm's sensitivity for seborrheic keratoses. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2023 |
| CC license |
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