- •Correct clinical diagnoses in skin lesions of face and scalp (FSL) are challenging.
- •Convolutional neural networks (CNN) are increasingly applied for skin diagnostics.
- •We investigated CNN-support for FSL diagnostically ‘unclear’ to dermatologists.
- •Following CNN classifications significantly reduced incorrect management decisions.
The clinical diagnosis of face and scalp lesions (FSL) is challenging due to overlapping features. Dermatologists encountering diagnostically ‘unclear’ lesions may benefit from artificial intelligence support via convolutional neural networks (CNN).
In a web-based classification task, dermatologists (n = 64) diagnosed a convenience sample of 100 FSL as ‘benign’, ‘malignant’, or ‘unclear’ and indicated their management decisions (‘no action’, ‘follow-up’, ‘treatment/excision’). A market-approved CNN (Moleanalyzer-Pro®, FotoFinder Systems, Germany) was applied for binary classifications (benign/malignant) of dermoscopic images.
After reviewing one dermoscopic image per case, dermatologists labelled 562 of 6400 diagnoses (8.8%) as ‘unclear’ and mostly managed these by follow-up examinations (57.3%, n = 322) or excisions (42.5%, n = 239). Management was incorrect in 58.8% of 291 truly malignant cases (171 ‘follow-up’ or ‘no action’) and 43.9% of 271 truly benign cases (119 ‘excision’). Accepting CNN classifications in unclear cases would have reduced false management decisions to 4.1% in truly malignant and 31.7% in truly benign lesions (both p < 0.01). After receiving full case information 239 diagnoses (3.7%) remained ‘unclear’ to dermatologists, now triggering more excisions (72.0%) than follow-up examinations (28.0%). These management decisions were incorrect in 32.8% of 116 truly malignant cases and 76.4% of 123 truly benign cases. Accepting CNN classifications would have reduced false management decisions to 6.9% in truly malignant lesions and to 38.2% in truly benign cases (both p < 0.01).
Dermatologists mostly managed diagnostically ‘unclear’ FSL by treatment/excision or follow-up examination. Following CNN classifications as guidance in unclear cases seems suitable to significantly reduce incorrect decisions.
Abbreviations:95% CI (95% confidence interval), AI (artificial intelligence), AK (actinic keratosis), BCC (basal cell carcinoma), CNN (convolutional neural network), FSL (face and scalp lesion), LM (lentigo maligna), LMM (lentigo maligna melanoma), SK (seborrhoeic keratosis)
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Published online: March 03, 2023
Accepted: February 26, 2023
Received in revised form: February 24, 2023
Received: January 19, 2023
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