Editor: What To Know
- “The overarching goal is to reduce the time needed to evaluate a PET scan, and by improving our throughput, ultimately increase patient access to a well-established noninvasive diagnostic imaging tool used by oncologists in the care of cancer.
- “Uniting new deep learning technologies with the clinical expertise of academic research institutions like Stanford will lead to the development of a completely new category of AI diagnostics and ultimately improve patient outcomes.
- The purpose of staging lymphoma is to quantify the extent of disease, guide decisions around therapy, and provide a baseline prior to treatment.
Kheiron Medical Technologies today announced its new collaboration with researchers at Stanford University to design functional proof-of-concept deep learning models to solve clinical problems in novel ways, starting with Non-Hodgkin’s Lymphoma (NHL). With the collaboration, Kheiron, which has pioneered the development and deployment of artificial intelligence solutions to help radiologists detect breast cancer earlier, will leverage its existing technologies and expertise to expand into new imaging modalities and cancer types. This furthers its mission of transforming cancer diagnostics through the power of deep learning.
The collaboration aims to harness the collective expertise of the Stanford Center for Artificial Intelligence in Medicine & Imaging (AIMI Center) and Kheiron. This endeavour will be referred to as ‘The Kaplan Project’ in honor of Stanford’s former radiation oncology leader, Dr. Henry Kaplan, who in the 1960s developed some of the earliest treatments for lymphoma. The Kaplan Project aligns to the AIMI center’s mission, where interdisciplinary expertise is the foundation to help solve clinically important problems in medicine using AI.
“Projects like this one are so exciting because they capitalize on collaborations not only between clinicians and data scientists, but also between academics and industry,” said Dr. Curt Langlotz, Director of the AIMI Center.
The purpose of staging lymphoma is to quantify the extent of disease, guide decisions around therapy, and provide a baseline prior to treatment. For oncological radiologists, the tasks of staging and evaluating post-treatment response on PET/CT scan images is manual and time-consuming.
The Kaplan Project will seek to apply Kheiron’s deep learning technology to FDG-PET/CT images of lymphoma patients to enhance two key radiologist outcomes: improving radiologist efficiency and improving radiologist accuracy.
“This groundbreaking project marks a new chapter in the application of AI to transform cancer diagnostics across the entire patient pathway,” said Dr. Peter Kecskemethy, CEO of Kheiron. “Uniting new deep learning technologies with the clinical expertise of academic research institutions like Stanford will lead to the development of a completely new category of AI diagnostics and ultimately improve patient outcomes.”
“Our project aims to improve a time-consuming and mostly qualitative process, the longitudinal assessment of whole-body FDG-PET/CT scans, using deep learning to augment imaging specialists,” said Dr. Guido A. Davidzon, Clinical Associate Professor at Stanford University. “The overarching goal is to reduce the time needed to evaluate a PET scan, and by improving our throughput, ultimately increase patient access to a well-established noninvasive diagnostic imaging tool used by oncologists in the care of cancer.”
