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arXiv · 2406.18240

Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools

Francisca Silva-Clavería·Carmen Serrano·Iván Matas·Amalia Serrano·Tomás Toledo-Pastrana·Begoña Acha
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Abstract

Background: The existence of different basal cell carcinoma (BCC) clinical criteria cannot be objectively validated. An adequate ground-truth is needed to train an artificial intelligence (AI) tool that explains the BCC diagnosis by providing its dermoscopic features. Objectives: To determine the consensus among dermatologists on dermoscopic criteria of 204 BCC. To analyze the performance of an AI tool when the ground-truth is inferred. Methods: A single center, diagnostic and prospective study was conducted to analyze the agreement in dermoscopic criteria by four dermatologists and then derive a reference standard. 1434 dermoscopic images have been used, that were taken by a primary health physician, sent via teledermatology, and diagnosed by a dermatologist. They were randomly selected from the teledermatology platform (2019-2021). 204 of them were tested with an AI tool; the remainder trained it. The performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists was analyzed using McNemar's test and Hamming distance. Results: Dermatologists achieve perfect agreement in the diagnosis of BCC (Fleiss-Kappa=0.9079), and a high correlation with the biopsy (PPV=0.9670). However, there is low agreement in detecting some dermoscopic criteria. Statistical differences were found in the performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists. Conclusions: Care should be taken when training an AI tool to determine the BCC patterns present in a lesion. Ground-truth should be established from multiple dermatologists.

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Francisca Silva-Clavería, Carmen Serrano, Iván Matas, Amalia Serrano, Tomás Toledo-Pastrana, Begoña Acha. 2024-06-26. Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools. https://arxiv.org/abs/2406.18240

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