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MST-AI: Skin Color Estimation in Skin Cancer Datasets.

Vahid Khalkhali1, Hayan Lee2,3,4, Joseph Nguyen2

  • 1Electrical and Computer Engineering Department, Temple University, Philadelphia, PA 19122, USA.

Journal of Imaging
|July 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces MST-AI, a novel method to detect skin tones in large datasets, addressing AI bias in skin cancer diagnosis for diverse populations. It enables more equitable AI development for early detection.

Keywords:
Monk Skin Tone (MST) scaleartificial intelligence (AI)bias reductionskin cancer detectionskin color detection

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Area of Science:

  • Artificial Intelligence
  • Dermatology
  • Medical Imaging

Background:

  • AI models for skin cancer diagnosis lack skin color data, impacting accuracy for non-white individuals.
  • Existing skin tone scales like Fitzpatrick may introduce bias.
  • Addressing skin color disparities is crucial for equitable AI in healthcare.

Purpose of the Study:

  • To propose MST-AI, a novel method for detecting skin color in large image datasets.
  • To enable the development of unbiased AI models for skin cancer diagnosis.
  • To improve the accuracy of AI diagnostic tools across diverse skin tones.

Main Methods:

  • Utilized convolutional neural networks for image processing tasks like lesion removal and segmentation.
  • Employed a Variational Bayesian Gaussian Mixture Model (VB-GMM) to model normal skin tones.
  • Compared predicted skin tone distributions against Monk Skin Tone (MST) scale probability distribution functions (PDFs) using Kullback-Leibler Divergence (KLD).

Main Results:

  • MST-AI demonstrated superior performance validated against manual annotations.
  • Achieved high correlation coefficients: Kendall's Tau (0.68), Spearman's Rho (0.69).
  • Achieved a perfect Normalized Discounted Cumulative Gain (NDGC) of 1.00, indicating excellent ranking accuracy.

Conclusions:

  • MST-AI effectively detects skin color in large datasets, using the less biased Monk Skin Tone (MST) scale.
  • This method addresses critical skin color imbalances in datasets like ISIC.
  • Paves the way for developing equitable and accurate AI tools for early skin cancer detection across all populations.