Concordance in basal cell carcinoma diagnosis Building a proper ground truth to train Artificial Intelligence tools
<|reference_start|>Concordance in basal cell carcinoma diagnosis Building a proper ground truth to train Artificial Intelligence tools: 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.<|reference_end|>
@article{silva-clavería2024concordance,
    title={Concordance in basal cell carcinoma diagnosis. Building a proper ground
  truth to train Artificial Intelligence tools},
    author={Francisca Silva-Claver'ia and Carmen Serrano and Iv'an Matas and
  Amalia Serrano and Tom'as Toledo-Pastrana and David Moreno-Ram'irez and
  Bego~na Acha},
    journal={arXiv preprint arXiv:2406.18240},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18240},
    primaryClass={q-bio.QM cs.CV cs.IR stat.ME}
}