KEYWORDS: Polyps, Optical coherence tomography, In vivo imaging, Deep learning, Visual process modeling, Tumor growth modeling, Error control coding, Resection, Endoscopy, Visualization
We present the development of an optical coherence tomography (OCT) catheter designed for in vivo subsurface imaging during colonoscopy, along with the results of a clinical pilot study involving 36 subjects to assess its ability to characterize colorectal polyps real-time. High-resolution cross-sectional OCT imaging of polyp microsctructure revealed distinct morphological structures that correlated with histological findings, including tubular adenoma, tubulovillous adenoma, sessile serrated polyps, and cancer. To enhance the in vivo diagnostic capabilities, we integrated a Vision Transformer (ViT) based deep learning classifier to differentiate between cancerous and complex benign polyps, and achieved a 100% accuracy for 5 test cases. Our findings suggest that the OCT catheter combined with deep learning complements standard-of-care imaging and has the potential to enhance real-time polyp characterization and improve clinical decision-making.
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