Another Alzheimer’s test
There’s a new Herald story with the lead
Artificial intelligence (AI) can identify Alzheimer’s disease 10 years before doctors can discover the symptoms, according to new research.
The story doesn’t link (even to the Daily Mail). Before we get to that, regular StatsChat readers will have some idea of what to expect.
Early diagnosis for Alzheimer’s is potentially useful when designing clinical trials for new treatments, and eventually will be useful for early treatment (when we get treatments that work). But not yet. It’s also not as much of a novelty as the story suggests. Candidate tests for early diagnosis are appearing all over the place (here’s seven of them).
Second, you’d expect that the accuracy of the test and its degree of foresight to have been exaggerated — and the story confirms this.
Following the training, the AI was then asked to process brains from 148 subjects – 52 were healthy, 48 had Alzheimer’s disease and 48 had mild cognitive impairment (MCI) but were known to have developed Alzheimer’s disease two and a half to nine years later.
That is, the early diagnosis wasn’t of people without symptoms, it was of people whose symptoms had led to a diagnosis but didn’t amount to dementia
The Herald doesn’t link, but Google finds a story at New Scientist, and they do link. The link is to the arXiv preprint server. That’s unusual: normally this sort of story is either complete vapour or is based on an article in a research journal. This one is neither: it’s a real scientific report, but one that hasn’t yet been published — it’s probably undergoing peer review at the moment.
Anyway, the preprint is enough to look up the accuracy of the test. The sensitivity was high: nearly all Alzheimer’s cases and cases of Mild Cognitive Impairement were picked up. The specificity was terrible: more than 1/4 of people tested would receive a false positive diagnosis.
It’s possible that this test can be re-tuned into a genuinely useful clinical tool. As published, though, it isn’t even close.
Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »