alt Hacker NewsSome initial thoughts as a practicing radiologist:
- This looks really cool and I hope they keep innovating on this. I love seeing new modalities develop and despite my (many) reservations and criticisms, if even one good use case comes out of it that truly helps people, it's tech money well spent imo.
- They show the reconstructed images as though they are a low resolution CT, and promise that quality will improve as they iterate. This is cool, but ultrasound is not CT. Ultrasound cannot image the lungs, as they are filled with air. You cannot find bone lesions, as the sound waves do not penetrate the cortex. You cannot image many structures in the abdomen if they are surrounded by gas-filled bowel. The brain is encased in bone, so you might get some penetration but it will be very limited. Even with theoretically perfect AI reconstruction, these scans will not be true "full body" in that there will be structures that are not reliably imaged. Imagine paying for weekly full body scans for years, everything looks fine, then its the lung cancer surrounded by air and invisible to ultrasound that kills you (that's why we use CT for lung screening!)
- The images they show are very cool, and do appear to show the correct structures. I realize this is early, but fuzzy shapes of organs is very, very far from medically useful. The whole point of screening is to identify problems early, often by definition, small. This technology looks like it will be best for seeing large, superficial (close to the skin) structures, whereas for effective screening, you want the opposite - small, deep structures.
- "Incidentalomas" or unexpected, probably benign, findings are annoying to physicians, but I in general have no problem with people collecting data on themselves where they can. To me it's similar to heart rate monitors or home blood pressure cuffs. The main issue here is education, so that patients know what the data is and is not telling them. The more complex the data, the more difficult that is.
- Many people mistakenly believe that early diagnosis is the final boss in medicine, that if only we could find every cancer early we could prevent all those deaths. There are, in fact, many, many other hurdles and bottlenecks. Many chronic, expensive diseases do not have clear imaging manifestations. The claim that "it's completely possible that with enough early imaging in the future, the world could avoid 30% of all deaths and 50% of all healthcare costs", I think, to any practicing physician, would sound completely divorced from reality.
Replies
I do a lot of MRI analysis including segmentation of small structures in the hippocampus called the hippocampal subfields. To collect these segmentations, we collect partial-field-of-view high in-plane resolution T2-weighted images on a 3 or 7 Tesla magnet. These sequences are generally only included in research protocols if the research specifically cares about hippocampal subfields...therefore they are rarely collected. There have been attempts to enable segmentation of these small structures using lower resolution T1-weighted scans, leveraging deep-learning or other models trained on concurrent T2w high resolution scans and the lower resolution scans within the same subject, allowing the model to predict the higher resolution information from the lower resolution inputs. This produces spectacularly beautiful segmentation on shitty data. Data whose resolution is about the same as the thickness of the structures you are segmenting or less. The problem is this: 1. The lower resolution image barely has any information in it on these smaller structures 2. The accuracy of the resulting segmentation depends entirely on how much the person fits the training distribution. But much research is on specific populations: children, autism, etc. 3. Some big names in imaging analysis tools have published these tools, lending their credibility to them. 4. The beautiful segmentations and (3) tend to convince non hippocampal experts that the resulting data is trustworthy, especially to an eager beaver researcher trying to maximize the impact of their already collected datasets.
I've rejected a number of papers for this.
But my point is this. Midjourney Medical might train a model to produce pretty images with this technique, but the more they need to depend on deep-learning models to get usable data, the more that the match between the training distribution and patient will matter.
I'm not a medical doctor at all, just an engineer who works in medical devices and I'm definitely sceptical about this.
I'm not totally sure of the value of an imaging system that only gives you very low resolution images if they're not accurate enough to determine anything from. You'd need a secondary CT or MRI anyway so why not skip to that?
My real concern is the dependence on external servers to reconstruct the images
Edit: From reading other people's comments, people are acting as if this is the first device trying to sell itself as improving pre-diagnosis imaging and this is totally revolutionary. This is not, and if any of the other products have convinced the entire medical industry that frequent imaging is beneficial then neither will this
> Many people mistakenly believe that early diagnosis is the final boss in medicine,
True. My more nuanced take on it: For most chronic conditions (including cancer) diagnostics have improved but resolution has not. The PR machine of course hypes things to make it seem like we have more cures today that in the past. What is have is more diagnostics and more procedures.
Not a physician. Some observations on these statements.
The predicate is "given how we practice medicine and the limits of humans ability to interpret the imaging modalities we have."
The more specific predicate is "for my specialty would this replace or prove superior to the tools that I have?"
Both of these are totally reasonable, however the history of medicine, and science in general, is that creating new ways to look at things has a tendency to reveal information that we never knew we needed.
For example, for years I thought of blood sugar as something that was either in a good or bad range. Then I tried a continuous blood sugar monitor. The full picture of the body's response to specific foods that I ate was eye opening. There's so much more to learn when you get a higher resolution (temporal in that case) view into your body.
Another wonderfully hopeful example is the retinal imaging ML work done by google. A completely non-invasive image of the retina for diabetic issues, that also happened to be able to predict things like age, sex, smoking status, previous cardiac events and more! Just take high-res pictures of things! The body is interconnected in ways that you can infer from one system so much about others.
So while I don't think anything the Dr. said is "wrong", I think it represents a very common blinkered mindset of pragmatic practitioners who need to deliver reliable performance daily.
this is why i always come to hacker news for the expert opinions. thank you for being critical yet optimistic.
> ultrasound is not CT
They're using "CT" in its literal sense: tomography*, using computers. In this case, ultrasound is the penetrating wave rather than x-ray. It is of course a very different thing than what the medical world knows as "CT" today.
I'm far more willing to trust someone in the field every day (you), over keyboard warriors who want to validate their new toy is alive.
They spent a lot of time selling the spa, and not a lot of time showing us the data.
I have a friend who may be dying. It's not cancer but equally dangerous. They caught it early but they're powerless to do anything. We're just watching, waiting, and hoping.
> "Incidentalomas" or unexpected, probably benign, findings are annoying to physicians
For a lot of these things I wonder why they don't just do multiple scans just to see how things develop. Is it a cost issue?
Couldn't have said it better.
> "Incidentalomas"
Good word for it. We were a bit worried about something that showed up my my mum's scans, as if an inoperable tangerine-sized lung tumour wasn't a big enough problem. It was a round dot about the size of a pea on her adrenal gland that lit up like crazy in contrast dye. Now, that as you probably know was a worry because oh shit, lymphatic system involvement, that's going to spread like crazy.
But after two years of immunotherapy, and about six years after being detected while the lung cancer is gone, the pea is still there, still as bright as ever, neither getting bigger nor smaller.
No-one is in a hurry to poke at it and see what it is, just in case that turns out to be a mistake.
89 this year and at least got to see her grandchildren start primary school, so good work from your lot and the oncologists!
Now I know it's an "incidentaloma" :-)
> The brain is encased in bone, so you might get some penetration but it will be very limited.
Radiologist as well. Remember this is full wave inversion not pulsed wave B mode. You can get much more useful information from both high low frequency and capture transmitted waves.
There is promise with this and we use it for example with MRgFUS. With advanced computational models or patient specific CT/ZTE MR aberration correction it is theoretically very feasible to image the brain with ultrasound, whether that’s more useful than say portable low field strength MR is a different question altogether.
> This is cool, but ultrasound is not CT.
Not to be pedantic but since this is a tech forum I would clarify that FWI US is computed tomography by definition (at least in this and many applications). Gas degrades conventional CT too, it’s just worse with US as you have little to no forward propagation and of course innumerable interfaces in the lungs to reflect and scatter.