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AI techniques in medical imaging may lead to incorrect diagnoses:
A team of researchers, led by the University of Cambridge and Simon Fraser University, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad artefacts, or unwanted alterations in the data, among other major errors in the final images. The effects were typically not present in non-AI based imaging techniques.
The phenomenon was widespread across different types of artificial neural networks, suggesting that the problem will not be easily remedied. The researchers caution that relying on AI-based image reconstruction techniques to make diagnoses and determine treatment could ultimately do harm to patients. Their results are reported in the Proceedings of the National Academy of Sciences.
"There's been a lot of enthusiasm about AI in medical imaging, and it may well have the potential to revolutionise modern medicine: however, there are potential pitfalls that must not be ignored," said Dr Anders Hansen from Cambridge's Department of Applied Mathematics and Theoretical Physics, who led the research with Dr Ben Adcock from Simon Fraser University. "We've found that AI techniques are highly unstable in medical imaging, so that small changes in the input may result in big changes in the output."
A typical MRI scan can take anywhere between 15 minutes and two hours, depending on the size of the area being scanned and the number of images being taken. The longer the patient spends inside the machine, the higher resolution the final image will be. However, limiting the amount of time patients spend inside the machine is desired, both to reduce the risk to individual patients and to increase the overall number of scans that can be performed.
Using AI techniques to improve the quality of images from MRI scans or other types of medical imaging is an attractive possibility for solving the problem of getting the highest quality image in the smallest amount of time: in theory, AI could take a low-resolution image and make it into a high-resolution version. AI algorithms 'learn' to reconstruct images based on training from previous data, and through this training procedure aim to optimise the quality of the reconstruction. This represents a radical change compared to classical reconstruction techniques that are solely based on mathematical theory without dependency on previous data. In particular, classical techniques do not learn.
[...] The researchers are now focusing on providing the fundamental limits to what can be done with AI techniques. Only when these limits are known will we be able to understand which problems can be solved. "Trial and error-based research would never discover that the alchemists could not make gold: we are in a similar situation with modern AI," said Hansen. "These techniques will never discover their own limitations. Such limitations can only be shown mathematically."
Journal Reference
Vegard Antun, Francesco Renna, Clarice Poon, et al. On instabilities of deep learning in image reconstruction and the potential costs of AI [$], Proceedings of the National Academy of Sciences (DOI: 10.1073/pnas.1907377117)
(Score: 2) by All Your Lawn Are Belong To Us on Wednesday May 13 2020, @06:17PM (1 child)
Not quite sure what you mean by, "in parallel." This would be hard to do Level 1 randomized control trials on. Lest you have to say, "Yeah, sorry your father died of that tumor. If we'd only seen it in the initial scans we could have excised it, but that AI extrapolator just wasn't working correctly and your father was in the test group instead of the control group." One can't, AFAICT, take the images and then have the raw images interpreted, have the raw data reconstructed, and then interpreted again as a reconstruction and compare the interpretations, because the essence is that a different (faster) sampling rate is used in the AI generated reconstruction run. One could theoretically do two runs, one after the other at the different rates, but a big confounding variable is patient movement (even minor inadvertent movement) during the scan resulting in different data. Plus you encounter an ethical question about having the patient be in the test significantly longer than normal. (At least there isn't an ionizing radiation concern like a CT or Fluoro would have).
So we end up with the methodology that these researchers used - take the raw data, introduce extremely small data anomalies to the image and then see what the interpretation algorithms do to that known data to see if they distort them significantly in a way that an interpreter might not be able to recognize it.
I'm not saying the study was correct, just that this is what they seem to have done and found. Maybe this does open the door to allow more active testing of the current algorithms. (And that's the other aspect... this certainly reads like these are currently-in-use algorithms that are being tested. So maybe there is need to raise concerns now rather than wait).
This sig for rent.
(Score: 2) by JoeMerchant on Wednesday May 13 2020, @06:48PM
The sequential scans should not be a major concern to the patients - they've already showed up, been prepped, etc. If they happen to be claustrophobic, then they should ethically be excused from the double scan process - there's no need to include claustrophobics in the study, even though they are one of the major beneficiaries of the fast scan process - and differentially the fast scan should be better on squirmy subjects.
If the sequential scans show an acceptable level of interpretation agreement, then there is no reason to believe that that a fast scan alone would be any worse than the fast scan obtained sequentially with a traditional scan.
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