Deep Learning With Optical Coherence Tomography for Melanoma Identification and Risk Prediction.

Area of Science:

• Biomedical Imaging
• Artificial Intelligence in Medicine
• Dermatology

Background:

• Malignant melanoma incidence is rising, necessitating advanced diagnostic tools.
• Current noninvasive imaging techniques for melanoma often rely on dermoscopic images.
• Few studies utilize prospective datasets for developing melanoma diagnosis models.

Purpose of the Study:

• To develop and evaluate a convolutional neural network (CNN) for melanoma identification and risk prediction.
• To utilize optical coherence tomography (OCT) imaging for melanoma diagnosis.
• To assess the CNN's performance on longitudinal data from animal models.

Main Methods:

• Development of a CNN model for image analysis.
• Application of the CNN to optical coherence tomography (OCT) images of mice skin.
• Longitudinal testing on four animal models: melanoma, dysplastic nevus, and control groups.

Main Results:

• The CNN achieved high sensitivity (0.99) and specificity (0.98) in classifying melanoma versus healthy tissue.
• The model successfully assigned risk scores based on melanoma probability.
• Accurate classification and risk stratification were demonstrated on prospective datasets.

Conclusions:

• The developed CNN shows significant potential for early melanoma detection and risk stratification using OCT imaging.
• This AI-driven approach may enhance clinical management of melanoma.
• OCT-based CNN models offer a promising avenue for noninvasive skin cancer diagnosis.