WEDNESDAY, Sept. 17, 2025 (HealthDay News) -- A convolutional neural network (CNN) model performs similarly to dermatologists for discriminating between well versus moderately and poorly differentiated cutaneous squamous cell carcinomas (cSCCs), according to a study to be published in the December issue of the Journal of the American Academy of Dermatology International.

Victor Liang, M.D., from Sahlgrenska Academy in Gothenburg, Sweden, and colleagues examined how a CNN performed in discriminating between well versus moderately and poorly differentiated cSCCs in a retrospective study. A de novo CNN was trained on 1,829 clinical close-up images of cSCCs: 1,254 well-differentiated and 575 moderately or poorly differentiated. The images were randomized into training, validation, and test sets (1,329, 200, and 300 images, respectively). Performance of the CNN was compared to combined assessment of seven independent readers (one senior resident and six board-certified dermatologists with four to 25 years of experience).

The researchers found that the CNN model yielded an area under the receiver operating characteristic curve of 0.69 compared with 0.70 for the combined dermatologists' assessment. In assessment of cSCC differentiation, the interobserver agreement was moderate (κ = 0.44). For clinical features, interobserver agreement ranged from fair to substantial. In moderately or poorly differentiated tumors, ulceration and flat surface topography were more common, with odds ratios of 2.34 and 2.94, respectively.

"The model we've developed needs further refinement and testing, but the way forward is clear -- AI [artificial intelligence] should be integrated where it actually adds value to decision-making processes within health care," lead author Sam Polesie, M.D., Ph.D., also from Sahlgrenska Academy, said in a statement.

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