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Abstract Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians. 

Systems engineering captures the desires and needs of the customer to conceptualize a system from the overall goal down to the small details prior to any physical development. While many systems projects tend to be large and complicated (i.e., cloud-based infrastructure, long-term space travel shuttles, missile defense systems), systems engineering can also be applied to smaller, complex systems. Here, the system of interest is the endoscope, a standard biomedical screening device used in laparoscopic surgery, screening of upper and lower gastrointestinal tracts, and inspection of the upper airway. Often, endoscopic inspection is used to identify pre-cancerous and cancerous tissues, and hence, a requirement for endoscopic systems is the ability to provide images with high contrast between areas of normal tissue and neoplasia (early-stage abnormal tissue growth). For this manuscript, the endoscope was reviewed for all the technological advancements thus far to theorize what the next version of the system could be in order to provide improved detection capabilities. Endoscopic technology was decomposed into categories, using systems architecture and systems thinking, to visualize the improvements throughout the system’s lifetime from the original to current state-of-the-art. Results from this review were used to identify trends in subsystems and components to estimate the theoretical performance maxima for different subsystems as well as areas for further development. The subsystem analysis indicated that future endoscope systems will focus on more complex imaging and higher computational requirements that will provide improved contrast in order to have higher accuracy in optical diagnoses of early, abnormal tissue growth. 

Positive outcomes for colorectal cancer treatment have been linked to early detection. The difficulty in detecting early lesions is the limited contrast with surrounding mucosa and minimal definitive markers to distinguish between hyperplastic and carcinoma lesions. Colorectal cancer is the 3rd leading cancer for incidence and mortality rates which is potentially linked to missed early lesions which allow for increased growth and metastatic potential. One potential technology for early-stage lesion detection is hyperspectral imaging. Traditionally, hyperspectral imaging uses reflectance spectroscopic data to provide a component analysis, per pixel, of an image in fields such as remote sensing, agriculture, food processing and archaeology. This work aims to acquire higher signal-to-noise fluorescence spectroscopic data, harnessing the autofluorescence of tissue, adding a hyperspectral contrast to colorectal cancer detection while maintaining spatial resolution at video-rate speeds. We have previously designed a multi-furcated LED-based spectral light source to prove this concept. Our results demonstrated that the technique is feasible, but the initial prototype has a high light transmission loss (~98%) minimizing spatial resolution and slowing video acquisition. Here, we present updated results in developing an optical ray-tracing model of light source geometries to maximize irradiance throughput for excitation-scanning hyperspectral imaging. Results show combining solid light guide branches have a compounding light loss effect, however, there is potential to minimize light loss through the use of optical claddings. This simulation data will provide the necessary metrics to verify and validate future physical optical components within the hyperspectral endoscopic system for detecting colorectal cancer.