Researchers developed a dual-modality imaging system that combines high-resolution structural and chemical analysis with AI to improve skin cancer diagnosis.
Key Details
- 1The system merges line-field confocal optical coherence tomography (LC-OCT) with confocal Raman microspectroscopy.
- 2Over 330 nonmelanoma skin cancer samples were examined in a year-long clinical study.
- 3AI models trained on chemical spectra achieved classification accuracy of 0.95 for basal cell carcinoma and 0.92 for both basal and squamous cell carcinoma.
- 4The approach allows targeted, noninvasive analysis of suspicious skin structures at cellular and molecular levels.
Why It Matters
This innovation promises less invasive, more accurate skin cancer diagnosis by combining structural and biochemical imaging with AI classification, potentially reducing reliance on biopsies and improving patient outcomes.
Source
EurekAlert
Related News
•EurekAlert
AI Model Accurately Predicts Blood Loss Risk in Liposuction
A machine learning model predicts blood loss during high-volume liposuction with 94% accuracy.
•EurekAlert
AI-Driven CT Tool Predicts Cancer Spread in Oropharyngeal Tumors
Researchers have created an AI tool that uses CT imaging to predict the spread risk of oropharyngeal cancer, offering improved treatment stratification.
•EurekAlert
AI Model PRTS Predicts Spatial Transcriptomics From H&E Histology Images
Researchers developed PRTS, a deep learning model that infers single-cell spatial transcriptomics from standard H&E-stained tissue images.