The National Institutes of Health (NIH) has awarded InformAI a $2.2 million grant to further the company's development of its CT-based TransplantAI software for organ donor-recipient pairing.
InformAI plans to use the small business innovation research (SBIR) grant for continued research and development work that will include building predictive models for heart and lung transplant outcomes and creating a clinical decision support informatics platform to assist organ transplant surgeons in matching donor organs with patient recipients, according to InformAI.
“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," said grant lead investigator Abbas Rana, MD, of Baylor College of Medicine in Houston, TX, in a company statement.
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
