AI-enabled Biomedical Research Seed Grant Program

AI-enabled Biomedical Research Seed Grant Program is sponsored by CTSI (Clinical and Translational Science Institute). This seed grant program aims to establish collaborations needed to realize the promise and potential of artificial intelligence (AI) in biomedical and health research.

CTSI seed grants awarded to exploratory AI-enabled biomedical research projects | Penn State University Clinical and Translational Science Institute CTSI seed grants awarded to exploratory AI-enabled biomedical research projects Biomedical and clinical researchers work with Penn State CTSI’s informatics core, to leverage artificial intelligence and machine learning to accelerate discoveries and improve health outcomes Top L-to-R: Nathan Lanham, Ravi Shah, Ankit Maheshwari; Bottom L-to-R: Christopher Tseng, Neerav Goyal, Monali Vasekar Credit: Penn State .

Creative Commons UNIVERSITY PARK, Pa. — Four research teams have been awarded funding through Penn State Clinical and Translational Science Institute's (CTSI) Seed Grant Program for 2024-25. The goal of this seed grant program is to establish collaborations needed to realize the promise and potential of artificial intelligence (AI) in biomedical and health research.

“Powered by large data sets, AI offers unprecedented opportunities for advancing biomedical discoveries and individual and population health outcomes,” said Vasant Honavar , co-lead of Penn State CTSI’s informatics core , Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence, and director of the Center for Artificial Intelligence Foundations and Scientific Applications at Penn State.

“CTSI seed grants will support interdisciplinary teams to explore promising but high-risk research projects and obtain preliminary results to support competitive proposals to [the National Institutes of Health], [the Patient-Centered Outcomes Research Institute] and other funders.

” The 2024-25 projects — along with their principal investigators, co-investigators and their affiliated departments/colleges — that were awarded seed grants are: “Analysis and Comparison Anti-coagulation with and without Bridging Therapy after Shoulder Arthroplasty” will examine timing and bridging therapy for oral anticoagulation in relation to wound complications, infection and revision surgery, as well as blood transfusion and thromboembolic events after shoulder arthroplasty.

This project is led by Nathan Lanham of the Department of Orthopedic Surgery at Penn State College of Medicine. “Predicting Cardiovascular Disease Outcomes from Electrocardiograms” will explore the design of artificial intelligence algorithms to predict cardiovascular disease outcomes from electrocardiograms (ECGs). The project aims to assemble a comprehensive database of 1.

4 million Penn State ECGs, allowing for better training models and the creation of impactful tools that can be used at the point of care. This project is led by Ankit Maheshwari and Ravi Shah of the Penn State Heart and Vascular Institute at Penn State College of Medicine.

“Machine Learning Analysis of Incidental Thyroid Mass CT Imaging” aims to minimize the need for ultrasound and increase the ability to counsel for fine needle aspiration for incidental thyroid nodules by training machine learning algorithms to classify masses as benign or suspicious for malignancy and secondarily identify the specific underlying histology.

This project is led by Christopher Tseng and Neerav Goyal of the Department of Otolaryngology-Head and Neck Surgery at Penn State College of Medicine.

“Development of Deep Learning Algorithm Based Predictive Model for Morbidity in Systemic Cancer Therapy Induced Pneumonitis” aims to determine if applying a deep learning artificial intelligence algorithm can predict primary outcomes for immune checkpoint inhibitor-induced pneumonitis and identify specific radiologic features of pneumonitis to reduce variance of imaging interpretation.

This project is led by Monali Vasekar of the Department of Medicine at Penn State College of Medicine. Recent advances in AI offer powerful machine learning (ML) tools for researchers to build predictive models and extract insights from large amounts of clinical data.

The CTSI informatics core provides the necessary training, services and AI/ML data science, and informatics experts to conceptualize, assess the feasibility of, design and execute complex studies using large clinical data sets.

Those interested in learning how to work with the CTSI informatics core can watch the replay of “ Harnessing the Power of EHR Data and IA to Advance Biomedical Research ,” which offers several examples of how clinical researchers in the College of Medicine and Penn State Health have worked with the CTSI informatics core to bring their research ideas to fruition.

Penn State CTSI offers the research support, tools and resources, consulting services, training and education to lower the barriers for biomedical and clinical researchers to realize the promise and potential large data sets and powerful AI, ML, data sciences and informatics tools to power biomedical discoveries and ultimately, improve health outcomes.

Penn State faculty, students and staff can learn more about the CTSI informatics core by visiting ctsi. psu. edu .

They can initiate a consultation request with the Informatics core by completing a research request form . Last Updated June 21, 2024 Information Sciences and Technology Clinical and Translational Science Institute