AI/ML based risk analysis for early cancer detection and prevention for high-risk, understudied, and/or underserved cancer populations.
Principal Investigator
Name
Amalendu Ranjan
Degrees
Ph.D
Institution
UNT Health Science Center
Position Title
Assistant Professor
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1529
Initial CDAS Request Approval
Apr 18, 2024
Title
AI/ML based risk analysis for early cancer detection and prevention for high-risk, understudied, and/or underserved cancer populations.
Summary
Artificial Intelligence (AI) systems and Machine Learning (ML) algorithms have the potential to support decision-making in clinical and population-based health, including pancreatic cancer care. Our proposal focuses on the development of AI/ML-based risk analysis models for the early diagnosis of high-risk patients. The objective is to create, test, and evaluate a risk-based AI/ML model designed for the early detection and prevention of cancer. Implementation of this model has the potential to identify opportunities for earlier diagnosis, thereby facilitating preventive or therapeutic interventions for pancreatic cancer. The proposed research aims to develop cancer risk calculator models to identify high-risk populations for early detection testing. This approach aims to prevent or treat cancer at an early stage and enhance the quality of care for cancer patients.
Aims
1. Conducting an analysis of risk factors associated with cancer and establishing relationships with clinical data and/or electronic health records to enable early detection of pancreatic cancer.
2. Develop and evaluate AI/ML-based risk models to improve the prediction of early detection for high-risk, understudied, and/or underserved cancer populations.
3. Designing and testing a web-based/mobile app-based analysis calculator model to capture symptoms for early detection.
Collaborators
1) Tozammel Hossain
2) Gahangir Hossain
3) Haihua Chen
4) Sharad Sharma