Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Silva-Clavería, Francisca Serrano, Carmen Matas, Iván Serrano, Amalia Toledo-Pastrana, Tomás Acha, Begoña |
| author_facet | Silva-Clavería, Francisca Serrano, Carmen Matas, Iván Serrano, Amalia Toledo-Pastrana, Tomás Acha, Begoña |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18240 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools Silva-Clavería, Francisca Serrano, Carmen Matas, Iván Serrano, Amalia Toledo-Pastrana, Tomás Acha, Begoña Quantitative Methods Computer Vision and Pattern Recognition Information Retrieval Methodology 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. |
| title | Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Information Retrieval Methodology |
| url | https://arxiv.org/abs/2406.18240 |