NVIDIA Inception startups are developing AI applications to support clinicians in breast cancer care, addressing gaps in imaging, risk assessment, and treatment planning. Over 40 million mammograms are performed annually in the U.S., but a projected shortage of tens of thousands of radiologists is straining the system’s capacity to read them.

iSono Health’s FDA-cleared ATUSA platform captures a standardized breast volume in about two minutes per breast, compared to up to 45 minutes for conventional handheld ultrasound.

The system’s AI, trained on thousands of full-breast scans comprising over 1.5 million ultrasound frames, automates image acquisition and uses NVIDIA GPU acceleration to deliver a 3D scan that the company says is 28% more sensitive than a handheld 2D ultrasound.

"Getting the scan closer to the patient is the first breakthrough," said Neda Razavi, CEO of iSono Health.

"Our vision is to make that scan increasingly informative: helping clinicians see what is there, understand what has changed and make more informed decisions." iSono Health has developed AI capabilities for lesion detection, 3D segmentation, and lesion classification, with a multicenter clinical study involving 3,200 patients underway.

Whiterabbit.ai’s FDA-cleared WRDensity software automatically assesses breast density from mammograms and has been used in the care of hundreds of thousands of patients.

"Every day, breast radiologists face a needle-in-a-haystack problem, trying to find roughly one cancer in every 200 mammograms," said Jason Su, cofounder and chief technology officer of Whiterabbit.ai.

"We hope AI can be a powerful sidekick to radiologists, helping to clear away the hay so they can focus their expertise where it matters most."

Ataraxis AI’s AI models interpret patterns in pathology slides to predict treatment response and recurrence risk. "The tools oncologists rely on today to guide therapy decisions were largely trained once, fifteen years ago, and never updated," said Joseph Cappadona, member of technical staff at Ataraxis AI. "Our models get stronger every time we acquire more clinical trial data."

SimBioSys uses NVIDIA MONAI for training and validation data, and NVIDIA CUDA-X libraries including cuBLAS and MONAI Deploy for its imaging technology, which runs on NVIDIA GPUs in the cloud. "NVIDIA technology gives us the computing power to take hundreds or thousands of images, apply," said Stacey Stevens, CEO of SimBioSys.

Source: nvidia