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Transforming Organ Transplantation: InformAI, Inc. Secures NIH Grant for Innovative AI Solution, TransplantAI

HOUSTON, TEXAS, US, February 13, 2024 / InformAI, Inc. is proud to announce that we have been awarded a grant from the National Institutes of Health (NIH). This Phase I/II Fast-Track grant (Project # 1R44HL172564-01) will support TransplantAI, our initiative to address the pressing issues of organ scarcity, discard, and allocation inefficiency in transplantation. The grant was awarded as part of the omnibus SBIR solicitation (PA-22-176) as a Fast-Track proposal, an application process that expedites award decisions and funding of Small Business Innovation Research (SBIR) scientifically meritorious projects with high potential for commercialization. The NIH fast track awards are highly competitive and difficult to win on the first try.

TransplantAI leverages data from over 500 parameters per organ and CT images to enhance decision-making to ultimately service more than 250 transplant centers and 56 organ procurement organizations in the United States. By utilizing advanced machine learning (ML) and deep learning (DL) methods, TransplantAI has already shown superior predictive accuracy for transplant outcomes compared to existing models.

Our proposal aims to develop more precise predictive models for heart and lung transplant outcomes and to create a clinical decision support informatics platform for donor-recipient pairing. This initiative is expected to improve organ allocation efficiency, save lives, and reduce healthcare costs by providing data-driven insights for real-time clinical evaluation.

Rowland Pettit MD, PhD, the Chief Science Officer for InformAI, explains, “With TransplantAI, we aim to develop a comprehensive clinical tool that brings together donor and recipient data in real time, enabling informatics for high-quality, granular, point-of-care readouts.”

The SBIR grant funding will facilitate further research and development (R&D) work on building TransplantAI, a novel artificial intelligence application designed to assist organ transplant surgeons in matching donor organs with patient recipients.

The grant is valued at $2.2 million and will allow InformAI to build upon previously funded efforts by a Phase I STTR grant (Project # 2014827) from the National Science Foundation (NSF), which supported the initial development of kidney and liver algorithms. The NIH grant funds will be used for heart and lung algorithm development (Phase I) and Phase 2 efforts to fully commercialize TransplantAI.

Dr. Abbas Rana, Associate Professor of Surgery at Baylor College of Medicine and the lead clinical investigator for the grant, states: “There is an urgent need for improved and integrated predictive clinical insights in solid organ transplantation, such as for real-time assessment of waitlist mortality and the likelihood of successful post-transplantation outcomes. This information is essential for healthcare teams and patients to make informed decisions, particularly in complex cases where expanded criteria allocation decisions are being considered. Currently, the separation of donor and recipient data into different systems requires clinical teams to conduct manual, parallel reviews for pairing assessments. Our team, along with those at other leading transplant centers nationwide, receives hundreds of organ-recipient match offers weekly.”

He adds: “Our collaboration with InformAI has been long-standing and productive. With this recent NIH award, we're in a very strong position to finally enable accurate prospective assessment and fairly evaluate retrospective performance in our field”.

Jim Havelka, founder and CEO of InformAI, states, "At InformAI, we are relentlessly focused on delivering high-quality AI solutions in healthcare to enable modern and well-informed delivery of healthcare. Our team has worked diligently to position InformAI as a leader in this area; with TransplantAI being just one of several innovative products we have developed. We are confident this approach can lead to an optimized transplant process with enhanced organ allocation and improved patient outcomes."

About InformAI: InformAI is a technology company dedicated to advancing healthcare through the development of AI-driven solutions. Our expertise lies in creating sophisticated machine learning and deep learning products that enhance clinical decision-making, optimize treatment planning, and improve patient outcomes. Our diverse portfolio spans across high-impact medical fields, including oncology, organ transplantation, and diagnostic imaging, aiming to address critical challenges in healthcare with data-driven precision and efficiency.

Research reported in this press release is supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health under award number R44HL172564.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


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