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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Summary

AI models for skin cancer detection perform poorly on Asian skin. Research must include diverse populations to ensure equitable healthcare outcomes for all skin types.

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Area of Science:

Dermatology and Artificial Intelligence
Medical Imaging Analysis
Computational Pathology

Background:

Skin cancer incidence is rising globally, necessitating efficient diagnostic tools.
Current artificial intelligence (AI) models for skin cancer detection are predominantly trained on Caucasian skin, potentially limiting their efficacy in diverse populations.
The performance disparities of AI models across different Fitzpatrick skin types, particularly in Asian populations, remain under-investigated.

Purpose of the Study:

To evaluate the performance of an AI model trained on a predominantly Caucasian dataset when applied to Asian skin types (Fitzpatrick II-III).
To assess the generalizability and potential biases of current AI-driven skin cancer detection methods in non-Caucasian populations.

Main Methods:

An EfficientNetB2 deep learning model was trained using the ISIC 2019 dataset, which primarily features Caucasian individuals.
Model performance was evaluated using cross-validation on the ISIC 2019 dataset, the PH² dataset (predominantly Caucasian), and a custom dataset of Asian individuals.
Key performance metric used was the area under the receiver operating characteristics curve (AUC).

Main Results:

The AI model achieved high AUC scores of 0.90 on ISIC 2019 and 0.91 on the PH² dataset.
A significant performance drop was observed on the Asian dataset, with an AUC of 0.81.
Fine-tuning the model did not substantially improve its performance on the Asian dataset, indicating a potential limitation in the model's architecture or training data bias.

Conclusions:

Existing AI-based skin cancer detection tools may exhibit suboptimal performance when applied to Asian skin types.
The findings underscore the critical need for developing and validating AI models using diverse datasets that accurately represent various skin tones and ethnicities.
Further research focusing on skin cancer detection in Asian populations is crucial for advancing equitable dermatological care.