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Osteoporosis screening tool secures FDA Breakthrough Designation

A new technology for estimating bone mineral density (BMD) from routine X-rays has received Breakthrough Device Designation from the US Food and Drug Administration (FDA).
OsteoSight was developed to address the unmet need for earlier diagnosis of osteoporosis, a condition characterised by a progressive decrease in bone density. Osteoporosis frequently goes unnoticed until a patient suffers a fragility fracture, a debilitating injury that is estimated to occur every 3 seconds worldwide.
A fragility fracture is defined as a broken bone caused by a fall from standing height or less. According to the International Osteoporosis Foundation, such falls cost global healthcare systems $400b each year.
In the United States alone, 50 per cent of women and 25 per cent of men over the age of 50 will experience a fracture caused by osteoporosis. The Bone Health and Osteoporosis Foundation reports that fragility fractures are responsible for more hospitalisations than stroke, breast cancer and heart attacks combined. Despite this, it’s estimated that up to 75 per cent of those affected remain undiagnosed and untreated.
“While treatments exist that can help slow the progression of bone loss, the biggest challenge we face is the persistent underdiagnosis of osteoporosis and low bone density, patients are still being diagnosed after a fracture, instead of during a period of timely intervention,” said Prof. Robert Pignolo, professor of medicine, geriatric medicine and gerontology at the Mayo Clinic and member of the scientific advisory board of Naitive Technologies.
“By screening patients who are having routine X-rays, we have a real opportunity to identify early bone loss and osteoporosis and prevent fractures. I’m delighted that the FDA has seen OsteoSight’s potential, and I’m excited to see the difference it will make in clinical practice.”
OseoSight was developed by Naitive Technologies, an early-stage, VC-backed population health company led by a team of MDs and PhDs from Oxford and Cambridge Universities.
The device aims to enhance detection rates by leveraging the subtle signals contained within the tens of thousands of X-rays that are taken every day, often for unrelated concerns.
By automatically including an estimate of bone density, along with an osteoporosis classification based on World Health Organisation (WHO) guidelines, into the radiology report, physicians can be alerted sooner to their patient’s bone health. Earlier identification of osteoporosis can enable physicians to initiate intervention before a fragility fracture occurs.
The FDA’s Breakthrough Device Program is intended to provide patients with more timely access to medical devices that have the potential for more effective treatment or diagnosis of life-threatening or irreversibly debilitating diseases or conditions. As part of the programme, the FDA will expedite development, assessment, and review of OsteoSight for regulatory clearance.
“We are thrilled that OsteoSight has received Breakthrough Device Designation,” said Dr. Will Briggs, CEO and Founder of Naitive. “It’s a key milestone for our technology and validates our strong belief that OsteoSight has the potential to revolutionise the way we find patients at risk for osteoporosis. We look forward to bringing this technology to market and helping improve patient outcomes.”
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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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