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Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

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Published on: September 26, 2019

Evaluation of atopic dermatitis severity using artificial intelligence.

Aga Maulana¹, Teuku R Noviandy¹, Rivansyah Suhendra²

¹Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia.

|March 7, 2024

Summary

Deep learning models can automate atopic dermatitis severity scoring. ResNet50 achieved 89.8% accuracy, aiding dermatologists in precise patient assessments.

Keywords:

Atopic dermatitis convolutional neural network deep learning image classification severity scoring

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Atopic dermatitis is a common, chronic inflammatory skin condition.
Accurate severity assessment is challenging.
Early and precise diagnosis is crucial for effective patient management.

Purpose of the Study:

To evaluate deep learning models for automated atopic dermatitis severity scoring.
To assess the performance of various convolutional neural network (CNN) architectures.
To utilize a dataset of Indonesian individuals with atopic dermatitis.

Main Methods:

Collected clinical images from 250 patients.
Utilized five pretrained CNN architectures: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0.
Evaluated models using accuracy, precision, sensitivity, specificity, and F1-score.

Main Results:

ResNet50 demonstrated the highest performance.
ResNet50 achieved 89.8% accuracy, 90.00% precision, 89.80% sensitivity, 96.60% specificity, and 89.85% F1-score.
The study highlights the potential of AI in dermatological diagnostics.

Conclusions:

Deep learning models show promise for automated atopic dermatitis severity scoring.
ResNet50 is a proficient model for this task.
AI tools can assist dermatologists in improving patient care and outcomes.