Development of artificial intelligence methods for optical coherence tomography based medical diagnostic applications

Primary challenges with early disease diagnosis are the lack of high-quality data and complexity of available data. Early diagnosis of both Atherosclerosis and AD, which effect hundreds of millions, is still limited by these two factors. Atherosclerosis is largely diagnosed decades after its onset with imaging lacking the resolution to directly identify critical disease components like vulnerable plaque with thin fibrous caps over a fibroatheroma (TCFA). Changes in the brain due to AD may begin 20 years before onset of disease symptoms and current diagnoses. Current diagnosis relies on low quality, late stage biomarkers like memory or motor function loss. In this work we have developed and used artificial intelligence methods to diagnose early onset of heart disease, cancer and Alzheimer’s with high sensitivity and specificity using Optical Coherence Tomography technology, surpassing the state of the art. Optical coherence tomography (OCT) can provide higher quality image data for Atherosclerosis and AD. OCT offers an axial resolution between 1-15 μm—one or two orders of magnitudes finer than conventional ultrasound, CT, and MRI (50-200 μm) while maintaining an imaging depth an order magnitude greater than microscopy. OCT can enable identification of key pathological features directly, like TCFA with a cap thickness of 65 μm. Despite these advantages, the complexity of OCT images makes interpretation difficult and volume of image data constrains real time analysis. Implementing Artificial Intelligence (AI) has the potential to make analysis of OCT images more accurate, consistent and rapid and reveal otherwise unseen features. AI can leverage machine learning and neural networks to learn optimal image features for each arterial tissue type. Furthermore, AI can be trained on ground truth guided by histology, the clinical gold standard, to identify commonly mistaken morphologies like TCFA and macrophages. While, various IVOCT plaque-classification approaches have been developed, they are poor medical diagnostics lacking in precision, reproducibility, and speed. In particular, the development of these approaches have been limited by a lack of comparison to histology (precision, reproducibility), or are not built using clinically standard IVOCT devices, require user-selected regions of interest (speed), or lack sensitivity to detect lipidic or calcified tissue (precision) (1,2,3,4). Expert human readers cannot reliably distinguish macrophages from TCFA and importantly are too slow for clinically relevant analysis of the hundreds of frames in an artery image. AI is well suited for medical diagnostics, as its training on gold standard data and optimized learning provides excellent precision. Lack of inter-operator variability with AI enhances reproducibility and GPU-powered AI provides a faster turn-around time compared to expert human readers, with near real-time results. In this dissertation I develop an automated histology validated plaque classification AI that identifies in IVOCT images three fundamental atherosclerotic tissue types: lipidic, fibrous, and calcific.
Numerous studies (5) have demonstrated that neuropathology also presents in the retina, retinal OCT imaging is useful in characterizing and possibly providing early detection — the eye serving as a window to the brain. However, state-of-the-art retinal OCT analysis still focuses on macroscopic tissue features, like retinal nerve fiber layer thinning, which occur late in disease progression. A need is recognized to identify cellular biomarkers of neuropathology that occur early in AD. Work by Barsoum et al (5) have shown mitochondria as a promising early stage biomarker. Mitochondria are less tightly clustered and undergo fission during neurodegeneration. Light scattering theory (6) suggests that the angle distribution of back scattered light from mitochondria will differ between healthy and fission states. Thus, the dissertation evaluates whether a scattering angle resolved OCT (SAROCT) system can identify mitochondrial fission, a potential early stage AD biomarker. Studies by Gardner et al (6) have also demonstrated that SAROCT when combined with statistical Burr distribution fitting can identify retinal vascularity, another neuropathology biomarker. This dissertation extends this work by incorporating distribution fitting over a pixel panning window and convolutional neural networks to generate full angiography images from SAR OCT.