Research

Researchers are developing novel algorithms, approaches and frameworks across a spectrum of diseases in the oncological space, including breast cancer, brain tumors, colorectal cancer, lung cancer, oral and oropharyngeal cancer, prostate cancer, and renal cell carcinoma. With partners, projects run the gamut of data modalities, from using digitized tissue biopsies for risk stratification to creating imaging tools for predicting response to treatment.

Diseases pay no heed to borders; therefore AI.Health will strive to improve the health of people worldwide through the use of low-cost diagnostic tests. One prime example is the collaboration with the Tata Memorial Centre, a national cancer center in Mumbai, India, focused on cancer prevention, treatment, and research. AI.Health and Tata are developing and validating an inexpensive tissue image-based predictor for identifying women with early-stage breast cancer to determine who will benefit from chemotherapy and who may not need it.

Through AI.Health, researchers are using AI to reveal differences in biomarkers between ethnic populations, for example between Black and white men with prostate cancer and South Asian and North American women with breast cancer. This work aims to provide population-specific AI risk assessment tools as well as address the issue of racial bias in the field of AI.

AI.Health develops and validates innovative analytical methods for diagnosis and predicting response to advanced therapy in patients with retinal disturbances due to long-standing diabetes. While other AI-based approaches show promise in diagnosis, novel approaches in identifying the architecture of the retinal vessels, as well as quantifying the shape and engorgement of vessels in the retina, could provide ophthalmologists a decision support tool to help select patients more likely to respond to targeted retinal therapy.

Although cancer leads in utilizing big data, other diseases are catching up, including kidney diseases. The National Institute of Diabetes and Digestive Diseases and Kidney Disorders has called for precision medicine initiatives inspired by the cancer community. The team is developing and applying novel machine learning tools for notation of structures within kidney pathology images. This will allow for construction of annotated kidney atlases, which could lead to the discovery of prognostic morphologic outcome signatures for FSGS.

Researchers at AI.Health are developing and validating tools to predict therapy response, no matter the disease. The team is developing AI and neuroinformatics tools to utilize MRI, CT, and digital pathology images to identify which brain, rectal, and lung cancer patients are most likely to respond to therapy and to predict treatment response to tumors following chemo, radiation, and immunotherapy.

Up to 6.1 million people in the US have atrial fibrillation, the most common heart arrhythmia. Research at AI.Health has focused on tackling this very important problem. The team actively collaborates to harness computerized descriptors from routine MRI images to predict which AFib patients are more inclined to experience recurrences after curative ablation. Additionally, the team is looking at computational image analysis of biopsy scans to identify which patients are the best candidates for heart transplantation.