Other AI tools that detect cancer are built using small- to moderately-sized data sets that typically involve a single malignancy or radiotracer. Leung referred to this new model as a "critical bottleneck in the current training and evaluation paradigm for AI and machine learning applications in medical imaging and radiology."

He believes that fully-automated AI tools like this will one day be a staple in imaging centers, helping physicians interpret cancer patients' PET/CT scans. They will be able to automatically detect lesions, delineate volumes of interest, and quantify important features and tumor characteristics that are difficult to evaluate visually.


"This automatic quantitative analysis will reduce the overall physician workload and augment radiologist capabilities by acting as a secondary reader for whole-body imaging scans," said Leung.

In addition to the clinical applications, this tool may also better the understanding of different diseases and cancers. That's because it can provide important molecular insights about the underlying in vivo biological processes and phenomena that might currently be understudied in large-scale populations.

Leung said that plans for making this tool widely available are currently underway.


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