Nezamoddin Nezamoddini-Kachouie

Ph.D.

Florida Institute of Technology

Associate Professor

PLCO (Learn more about this study)

PLCO-1633

Jul 24, 2024

A Shared Artificial Intelligence Cancer Treatment Recommender System

The rapid progress in machine learning technologies along with the advancements in the infrastructure for information technologies such as the graphics processing unit (GPU), and the development of public databases, have made it possible to make use of large-scale data and have motivated a great deal of interest in using machine learning and artificial intelligence (AI) technologies in cancer diagnosis and clinical oncology. Nearly 350 AI equipped medical devices have been approved in the US by the FDA. Among them, imaging and diagnostic technologies lead the integration of algorithms to drive clinical decision-making in healthcare. Several AI equipped medical devices have already been used for clinical applications such as diagnostics imaging. Moreover, machine learning techniques have been developed for Precision Medicine by customization and optimization of the medical care for each individual and Precision Oncology is taking advantage of advancements in the machine learning for choosing treatment options. Lung cancer is the leading cause of cancer death in the United States and it makes up around 25% of cancer deaths. It is the second most diagnosed cancer in both men and women in the US. Over half of patients diagnosed with lung cancer die within one year of diagnosis and the 5-year survival is around 17.8%. Despite advances in the detection and treatment of lung cancer, the overall 5-year survival rate still remains poor, only 16% for all stages combined. The proposed research is a novel methodology to improve data sharing, data dissemination, and transparency in patient care for improvement of patient outcomes. The goal of this study is implementation of an artificial intelligence-based repository for dynamic storing, sharing, mining, and analyzing cancer diagnosis, cancer practice, and clinical trials.

Specific Aim 1: Build a platform for shared practice to improve transparency and reduce malpractice. A database will be implemented by assimilation of patients’ demographics, clinical data, outcomes, medical imaging, and gene mutations.

Specific Aim 2: Make a decision using the shared practices for optimal outcome. An artificial intelligence (AI) recommender system will be implemented to learn the complex and nonlinear relationships between the patients’ integrated data, and patients’ outcomes.

Specific Aim 3: Test and evaluate the trained AI recommender system (in Specific Aim 2) in recommendation of interventions based on previous patients’ outcomes available in the integrated database.