[HN Gopher] Detection of L-Ventricular Systolic Dysfunction from...
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Detection of L-Ventricular Systolic Dysfunction from
Electrocardiographic Images
Author : johntfella
Score : 20 points
Date : 2023-08-14 08:47 UTC (14 hours ago)
(HTM) web link (doi.org)
(TXT) w3m dump (doi.org)
| uj8efdjkfdshf wrote:
| Let me guess, they just rediscovered that Q waves in V1-V3 are
| predictive of LVSD?
| xattt wrote:
| Yes, but with machine learning this time!
|
| > This approach represents an automated and accessible
| screening strategy for LV systolic dysfunction, particularly in
| low-resource settings.
|
| If the low-resource setting means there is no one to interpret
| a 12-lead that suggests LV dysfunction, what are the chances
| the individual will have reliable access to an echo or, further
| down the line, an ACEi to slow remodelling?
| uj8efdjkfdshf wrote:
| I should point out that ECG machines that use heuristics to
| provide on the spot diagnoses already exist, are widespread
| and are way easier to implement.
|
| [0] https://www.nejm.org/doi/full/10.1056/NEJM199112193252503
| haldujai wrote:
| So what they're doing here is using deep learning on
| pictures of ECGs instead of the electrical signals used by
| machines that provide heuristics.
|
| The proposed use case/workflow seems to be that (somehow)
| someone, somewhere is using an ECG machine that doesn't
| provide an automatic preliminary interpretation (i.e. > 20
| years old) that is (somehow) still operational and the
| operator doesn't know how to interpret an ECG. They would
| then presumably upload a picture of the ECG to a platform
| that can run a deep learning model on the image. This is
| also apparently happening in a place where a clinician is
| then available and echo (for confirmation, quantitative EF
| and etiology) as well as medications are still accessible
| to impact patient management/outcomes.
|
| This reads like something done by pure CS folks who don't
| understand how medicine works but the authors include
| cardiologists.
|
| Ignoring the glaring validity issues of a study population
| that only included patients who had an indication for echo,
| the only explanation I can see for why someone would do
| this is to puff up the author's h-index as this will
| undoubtedly be cited in several "emerging applications of
| AI in medicine" papers.
| csdvrx wrote:
| > If the low-resource setting means there is no one to
| interpret a 12-lead that suggests LV dysfunction, what are
| the chances the individual will have reliable access to an
| echo or, further down the line, an ACEi to slow remodelling?
|
| Why would a human be needed to interpret anything, if the
| detection can be done by software?
|
| It may be legally required, but that can change with the
| stroke of a pen.
|
| As for getting access to an echo, why wouldn't it be possible
| to also have that done by software?
|
| Then for ACEi, if people already can easily purchase illegal
| drugs, why do you think they won't be able to buy ACI?
|
| In a "low resource setting", I think enforcing drug laws may
| also be affected by the "low resources": people who want to
| avoid heart problems may be strongly incentivized to
| disregard the already poorly enforced laws to acquire
| whatever they need thay may increase their lifespan.
| haldujai wrote:
| > Why would a human be needed to interpret anything, if the
| detection can be done by software?
|
| ECG machines already do this based on the electrical signal
| data rather than using a picture of the ECG.
|
| > As for getting access to an echo, why wouldn't it be
| possible to also have that done by software?
|
| You need an ultrasound machine which at the very least is a
| point of care model (~$1-2000) as well as an operator
| competent in acquiring the images. Ironically if you have
| one of these the software already exists to do what this
| model is doing with higher accuracy and provides
| substantially more information.
|
| > Then for ACEi, if people already can easily purchase
| illegal drugs, why do you think they won't be able to buy
| ACI?
|
| I can't imagine a location having a healthcare provider,
| ultrasound machine and ACEI accessible while still using an
| ECG machine obsolete enough to require this.
| csdvrx wrote:
| Using pictures instead of signals, allowing to do
| automation with existing (or obsolete) tools, is a true
| innovation IMHO.
|
| > I can't imagine a location having a healthcare
| provider, ultrasound machine and ACEI accessible while
| still using an ECG machine obsolete enough to require
| this.
|
| I can imagine many locations having no healthcare
| provider (or maybe just a nurse) and people putting a
| vest/belt/whatever whose electrodes are hooked to an
| obsolete machine, to get a quick estimations of their
| risk, using special software running on their smartphone
| to interpret the pictures.
|
| Updating software running on the machine would be hard
| and risky.
| haldujai wrote:
| > Using pictures instead of signals, allowing to do
| automation with existing (or obsolete) tools, is a true
| innovation IMHO.
|
| > I can imagine many locations having no healthcare
| provider (or maybe just a nurse) and people putting a
| vest/belt/whatever whose electrodes are hooked to an
| obsolete machine, to get a quick estimations of their
| risk, using special software running on their smartphone
| to interpret the pictures.
|
| So the innovation is that: A low-resource location with
| no medical expertise (and again is using a 20 year old
| ECG machine that's somehow still functional) is going to
| be able to jerry-rig a vest (noting that 12 leads require
| accurate placement) and then is going to take a picture
| of the resultant ECG _with a smart phone_ and use a model
| that 's not been validated on an average risk person or
| noisy ECG data to analyze said picture?
|
| Or we can keep it simple and just use a $50 single lead
| ECG that plugs into a smart phone and/or is already
| incorporated into wearables requiring zero medical
| expertise for accurate use.
|
| https://www.medrxiv.org/content/medrxiv/early/2022/12/04/
| 202...
|
| > to get a quick estimations of their risk
|
| This is my point about not understanding medical
| relevance.
|
| Phenomenal, you know that you have a _risk_ of left
| ventricular systolic dysfunction. Now what? What 's the
| next step? Where are you going to get the echo or medical
| professional?
|
| > Updating software running on the machine would be hard
| and risky.
|
| You don't have to update the software, you just have to
| use a machine from the 2000s.
| walnutclosefarm wrote:
| It's a great demonstration of using AI to see signals that are
| not apparent to practicing clinicians, but I'm not sure how novel
| the algorithm is. Mayo Clinic was testing such an algorithm in
| field clinical trials already a couple of years ago:
| https://www.mayoclinicproceedings.org/article/S0025-6196(22)...
| haldujai wrote:
| This is a terrible demonstration of an unnecessarily
| complicated solution using pictures of signals instead of the
| actual signal data for something that is very unlikely to
| change patient management.
|
| The Mayo study is marginally less questionable with
| uncontrolled confounders and an outcome measure carefully
| chosen to show a positive result for a tool that is patented by
| the Mayo Clinic. Also doubtful that this changes patient
| management and they chose not to look at that in their study
| despite having access to that information.
| gorkish wrote:
| The images _are_ the actual data. Vectorizing
| /normalizing/transforming the representation of data by means
| of moving it into an image space is a completely valid
| approach in ML and can have many advantages, not the least of
| which is being able to take advantage of models and research
| that have been done with image data.
|
| After all, this is the same thing that we do for human
| doctors reading an ECG. Or at least I've never heard one
| exclaim "This chart is useless, can I please have the CSV?"
| haldujai wrote:
| > The images are the actual data.
| Vectorizing/normalizing/transforming the representation of
| data by means of moving it into an image space is a
| completely valid approach in ML and can have many
| advantages.
|
| I'm aware that this is a valid approach in general.
|
| > not the least of which is being able to take advantage of
| models and research that have been done with image data.
|
| Essentially all of the research, clinical and computer
| science, that we would be able to take advantage of is
| based off of digital ECG recordings and numerical data not
| digitized ECG images.
|
| This is the first publication to my knowledge using an
| image based approach and their innovation is partially
| validating a deep learning model that is as accurate as
| explainable models and algorithms described as early as the
| mid 2000s and can already be clinically implemented.
|
| > After all, this is the same thing that we do for human
| doctors reading an ECG. Or at least I've never heard one
| exclaim "This chart is useless, can I please have the CSV?"
|
| So because a human is incapable of analyzing digital
| signals the best approach for ML is to artificially use the
| same limitation?
|
| I would love to hear a reason for why it may be a better
| approach to analyze a noisier and less rich digitized image
| space representation an ECG rather than the raw digital
| signals.
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