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Apple Watch may miss hypertension cases

Apple Watch hypertension alerts may miss cases of high blood pressure, with the absence of an alert offering limited reassurance, particularly for older adults, researchers say.
Hypertension, or high blood pressure, occurs when blood consistently pushes too forcefully against artery walls, increasing the risk of heart disease and stroke.
The study applied performance data from the smartwatch’s blood pressure notification feature to US population statistics to estimate how it performs in real-world use.
Researchers found that around 69 per cent of people who received an alert did have hypertension, while about 79 per cent of those who did not receive an alert were free of the condition. Performance varied by age, race and ethnicity.
The feature, cleared by the US Food and Drug Administration last year, is not intended to diagnose hypertension. It uses an optical heart sensor to flag patterns that may suggest raised blood pressure. A validation study submitted to the regulator showed that roughly 41 per cent of people with undiagnosed hypertension received an alert, while nearly 59 per cent did not. Among people without hypertension, 7.7 per cent received an inappropriate alert.
The analysis was carried out by researchers at the University of Pennsylvania and the University of Utah.
In groups where undiagnosed hypertension is more common, alerts were more likely to reflect true hypertension, while the absence of an alert was less reassuring.
For adults aged 60 and older, receiving an alert increased the probability of having hypertension from 45 per cent to 81 per cent. However, those in this age group who did not receive an alert still had a 34 per cent probability of having undiagnosed hypertension.
Performance also differed by race and ethnicity, reflecting wider disparities in cardiovascular health linked to social factors. Among non-Hispanic Black adults, an alert increased the probability of hypertension from 36 per cent to 75 per cent, while the absence of an alert lowered it to 26 per cent.
Jordana Cohen, the study’s lead author and an associate professor of medicine and epidemiology at the University of Pennsylvania School of Medicine, wrote: “However, the absence of an alert provides limited reassurance, particularly in older and higher-risk adults, and routine blood pressure measurement with validated cuff-based devices remains essential.”
The researchers added that while the feature could help many people unaware they have hypertension, “an even larger proportion of individuals with undiagnosed hypertension could receive no alert at all.”
They cautioned that false reassurance may lead to missed opportunities for early detection and treatment, adding: “Rigorous validation, strategies to improve cuffless device accuracy, and thoughtful integration into population screening will be essential if cuffless technologies are to contribute meaningfully to hypertension detection.”
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AI can predict Alzheimer’s with almost 93% accuracy, researchers say

Alzheimer’s AI can predict the disease with nearly 93 per cent accuracy using more than 800 brain scans, researchers say.
The system identified anatomical changes in the brain linked to the onset of the most common form of dementia, a condition that gradually damages memory and thinking.
The findings build on years of research suggesting AI could help spot early Alzheimer’s risk, predict disease and identify patients whose condition has not yet been diagnosed.
Benjamin Nephew, an assistant research professor at the Worcester Polytechnic Institute in Massachusetts, said: “Early diagnosis of Alzheimer’s disease can be difficult because symptoms can be mistaken for normal ageing.
“We found that machine-learning technologies, however, can analyse large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
The study used MRI scans, a type of detailed brain imaging, from 344 people aged 69 to 84.
The dataset included 281 scans showing normal mental function, 332 with mild cognitive impairment, an early stage of memory and thinking decline, and 202 with Alzheimer’s.
The scans covered 95 of the brain’s nearly 200 distinct regions and used an AI algorithm to predict patients’ health.
Being able to use AI to help diagnose Alzheimer’s earlier could give patients and doctors crucial time to prepare and potentially slow the progression of the disease.
The analysis showed that one of the top predictive factors was brain volume loss, or shrinkage, in the hippocampus, which helps form memories, the amygdala, which processes fear, and the entorhinal cortex, which helps provide a sense of time.
This pattern held across age and sex, with both men and women aged 69 to 76 showing volume loss in the right part of the hippocampus, suggesting it may be an important area for early diagnosis, the researchers noted.
However, the research also found that the way brain regions shrink differs by sex.
In females, volume loss occurred in the brain’s left middle temporal cortex, which is involved in language and visual perception. In males, it was mainly seen in the right entorhinal cortex
The researchers believe this could be linked to changes in sex hormones, including the loss of oestrogen in women and testosterone in men.
These conclusions could help improve methods of diagnosis and treatment going forward, Nephew said.
More than 7.2m Americans are living with Alzheimer’s, according to the Alzheimer’s Association.
More research is being done to reveal other impacting factors.
Nephew said: “The critical challenge in this research is to build a generalisable machine-learning model that captures the difference between healthy brains and brains from people with mild cognitive impairment or Alzheimer’s disease.”
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