When you’ve ever had a PET scan, you understand it’s an ordeal. The scans assist medical doctors detect most cancers and monitor its unfold, however the course of itself is a logistical nightmare for sufferers.
It begins with fasting for 4 to 6 hours earlier than coming into the hospital — and good luck to you in case you reside rurally and your native hospital doesn’t have a PET scanner. Whenever you get to the hospital, you’re injected with radioactive materials, after which you will need to anticipate an hour whereas it washes by your physique. Subsequent, you enter the PET scanner and have to aim to lie nonetheless for half-hour whereas radiologists purchase the picture. After that, you need to maintain bodily away from the aged, younger individuals, and pregnant ladies for as much as 12 hours since you’re actually semi-radioactive.
One other bottleneck? PET scanners are concentrated in main cities as a result of their radioactive tracers should be produced in close by cyclotrons — compact nuclear machines — and used inside hours, limiting entry in rural and regional hospitals.
However what in case you may use AI to transform CT scans, that are way more accessible and reasonably priced, into PET scans? That’s the pitch of RADiCAIT, an Oxford spinout that got here out of stealth this month with $1.7 million in pre-seed financing. The Boston-based startup, which is at High 20 finalist in Startup Battlefield at TechCrunch Disrupt 2025, has simply opened a $5 million increase to advance its medical trials.
“What we actually do is we took essentially the most constrained, complicated, and dear medical imaging resolution in radiology, and we supplanted it with what’s the most accessible, easy and reasonably priced, which is CT,” Sean Walsh, RADiCAIT’s CEO, informed TechCrunch.
RADiCAIT’s secret sauce is its foundational mannequin — a generative deep neural community invented in 2021 on the College of Oxford by a staff led by the startup’s co-founder and chief medical info officer, Regent Lee.
The mannequin learns by evaluating CT and PET scans, mapping them, and selecting out patterns in how they relate to one another. Sina Shahandeh, RADiCAIT’s chief technologist, describes it as connecting “distinct bodily phenomena” by translating anatomical construction into physiological perform. Then the mannequin is directed to pay additional consideration to particular options or facets of the scans, like sure kinds of tissue or abnormalities. This centered studying is repeated many occasions with many various examples, so the mannequin can determine which patterns are clinically necessary.
Techcrunch occasion
San Francisco
|
October 27-29, 2025
The ultimate picture that goes to medical doctors for overview is created by combining a number of fashions working collectively. Shahandeh compares the method to Google DeepMind’s AlphaFold, the AI that revolutionized protein construction prediction: Each programs study to translate one sort of organic info into one other.
Walsh claims the staff at RADiCAIT can mathematically show that their artificial or generated PET pictures are statistically just like actual chemical PET scans.
“That’s what our trials present,” he stated, “that the identical high quality of choice has been made when the physician, radiologist, or oncologist is given a chemical PET or [our AI-generated PET].”
RADiCAIT doesn’t promise to switch the necessity for PET scans in particular therapeutic settings, like radioligand remedy, which kills most cancers cells. However for diagnostic, staging, and monitoring functions, RADiCAIT’s know-how may make PET scans out of date.
“As a result of it’s such a constrained system, there’s not sufficient provide to satisfy demand for diagnostics and theragnostics,” Walsh stated, referring to a medical method that mixes diagnostic imaging (i.e., PET scans) with focused remedy to deal with ailments (i.e., most cancers). “So what we’re seeking to do is absorb that demand on the diagnostic aspect. PET scanners themselves ought to choose up the slack on the theragnostic aspect.”
RADiCAIT has already begun medical pilots particularly for lung most cancers testing with main well being programs like Mass Basic Brigham and UCSF Well being. The startup is now pursuing an FDA medical trial — a costlier and concerned course of that’s driving RADiCAIT’s $5 million seed spherical. As soon as that’s accredited, the following step might be to do business pilots and display the business viability of the product. RADiCAIT additionally needs to run the identical course of — medical pilots, medical trials, business pilots — for colorectal and lymphoma use instances.
Shahandeh stated RADiCAIT’s method to utilizing AI to yield legitimate insights with out the burdens of inauspicious and costly checks is “broadly relevant.”
“We’re exploring extensions throughout radiology,” Shahandeh added. “Anticipate to see comparable improvements linking domains from supplies science to biology, chemistry, and physics wherever nature’s hidden relationships could be realized.”
If you wish to hear extra about RADiCAIT be a part of us at Disrupt, October 27 to 29 in San Francisco. Be taught extra right here.
{content material}
Supply: {feed_title}