Abstract A036: Smart Health Screening in Identification of Individuals at High Cancer Risk Using Artificial Intelligence
Cancer is the second leading cause of death worldwide and causes nearly 9.6 million deaths each year according to the WHO. Although medical technology and screening programs continue to improve, delayed diagnosis remains a major challenge. Artificial intelligence (AI) offers a promising approach for cancer detection and risk prediction. This study developed an AI-based platform that integrates gene panel data, traditional risk factors, and healthcare professional assessments to identify individuals at high cancer risk. The system applied machine learning models including random forest, support vector machines, convolutional neural networks (CNN), deep learning algorithms, and Natural Language Processing to analyze complex associations between genetic variants and clinical risk factors. The platform used Python programming with Django, MySQL, ChartJS, JSCharting, HTML, CSS, and JavaScript frameworks. Developers created independent front-end and back-end modules and managed version control with Git. Researchers evaluated the platform in 514,506 cases across 39 multicenter units. The deep learning engine analyzed medical data and risk factors using CNN, support vector machines, and random forest classifiers. The platform also integrated ECG and mammography analysis with general health screening to provide comprehensive risk assessment. Two CNN models supported colon cancer analysis with different fully connected layers for classification of colon adenocarcinoma and benign colonic tissue (https://smartcancer.ir/). Random Forest-based models estimated short-term 5-year risk and long-term lifetime risk. The platform integrated Electronic Health Records (EHR) with blockchain technology to improve security, privacy, and data integrity. The system also generated family trees to support genetic counselling. The platform recommended the next clinical step based on low-risk or high-risk status and stored previous records for specialist follow-up. This study presents a novel framework that combines multiple screening modalities with AI-driven digital analysis for identification of high-risk individuals. Integration of federated learning, blockchain security, and AI-based diagnostics can improve cancer screening, support clinical decision-making, reduce cancer risk, and optimize healthcare resources. Aida Yavari Kondori, Ahmadreza Tavasouli, Mona Maftouh, Ghazaleh Pourali, Zahra Yousefli, Soodabeh Shahidsales, Ali Alamdaran, Masoud Pezeshkirad, Marjaneh Farazestanian, Elisa Giovannetti, Amir Avan, Hamid Naderi. Smart Health Screening in Identification of Individuals at High Cancer Risk Using Artificial Intelligence [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A036.