Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning.
Hongbo LuoShuying LiYifeng ZengHassam CheemaEbunoluwa OtegbeyeSafee AhmedWilliam C ChapmanMatthew MutchChao ZhouQuing ZhuPublished in: Journal of biophotonics (2022)
Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue assessment and biopsy targeting, with a high optical resolution and an imaging depth of ~1 mm. Recent advances in convolutional neural networks (CNN) have enabled application in ophthalmology, cardiology, and gastroenterology malignancy detection with high sensitivity and specificity. Here, we describe a miniaturized OCT catheter and a residual neural network (ResNet)-based deep learning model manufactured and trained to perform automatic image processing and real-time diagnosis of the OCT images. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~7 μm, and an axial resolution of ~6 μm. A customized ResNet is utilized to classify OCT catheter colorectal images. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.975 is achieved to distinguish between normal and cancerous colorectal tissue images.
Keyphrases
- optical coherence tomography
- deep learning
- convolutional neural network
- ultrasound guided
- optic nerve
- artificial intelligence
- diabetic retinopathy
- neural network
- machine learning
- high resolution
- fine needle aspiration
- single molecule
- endothelial cells
- high grade
- mass spectrometry
- induced pluripotent stem cells
- structural basis
- photodynamic therapy