Colorectal cancer diagnosis has long relied on invasive, subjective procedures. Apex AI's patent (No. 10-2021-0025707) introduces a powerful AI-driven system that objectively analyzes colonoscopy images to predict cancer risks—improving accuracy, efficiency, and patient outcomes. Here's how it works: Overview The patent filed by Apex AI outlines a sophisticated system designed to predict the risk of colorectal cancer by analyzing colonoscopy images using artificial intelligence (AI). The system aims to overcome limitations of traditional diagnostic approaches, such as biopsies and manual image analyses, by providing a more objective, accurate, and efficient diagnostic tool. Technical Background Colorectal cancer (CRC) is a condition characterized by uncontrolled cell proliferation, which significantly affects bodily functions. Conventional diagnostic methods include tissue biopsy and imaging techniques (X-rays, MRI, etc.), both having limitations such as invasiveness, patient discomfort, potential inaccuracy, and high dependence on clinician experience. Purpose of the Invention The invention seeks to address these limitations by utilizing AI to analyze colonoscopy images systematically, providing quantitative risk assessments for colorectal cancer, thereby enhancing diagnostic reliability and accessibility. System Components The invention describes a colorectal cancer risk prediction system comprising three key modules (as depicted on page 14): 1. Base Condition Analysis Unit (기저질환 판단부) - Utilizes AI algorithms to examine multiple colonoscopy images from various colon locations. - Identifies and diagnoses basic colon-related diseases including inflammatory bowel disease (IBD), Crohn's disease, ulcerative colitis, polyps, low-grade adenomas, and high-grade adenomas. 2. Associated Disease Analysis Unit (유관질환 분석부) - Analyzes the severity, location, size, color, and surface structure of detected colon diseases. - Determines relationships between basic diseases and other associated conditions. 3. Colorectal Cancer Risk Calculation Unit (대장암 위험성 산출부) - Synthesizes data from the Base Condition and Associated Disease Analysis units. - Calculates individual risk scores for each identified disease, aggregating them into a final comprehensive colorectal cancer risk score. Methodology The AI-driven system employs the following methodology: - Image Acquisition and AI-based Diagnosis: Acquires multiple colonoscopy images from distinct colon segments (rectum, sigmoid colon, descending colon, transverse colon, ascending colon, cecum, and appendix). AI algorithms classify the presence or absence of colorectal pathologies. - Comprehensive Analysis of Pathologies: Evaluates pathology characteristics (location, severity, visual traits) to identify correlated disease patterns and potential progression pathways. - Quantitative Risk Assessment: Computes risk scores for identified conditions, ultimately presenting an aggregate colorectal cancer risk profile that assists clinicians in making informed diagnostic and treatment decisions. Advantages and Clinical Impact This invention enhances early detection, diagnostic accuracy, and risk stratification of colorectal cancer, offering a substantial improvement over traditional diagnostic methods. The AI-driven approach significantly reduces subjectivity and human error, provides rapid assessments, and facilitates better clinical decision-making.