Wednesday, January 07, 2026

Breakthrough for Alzheimer’s test that uses finger-prick blood samples

A groundbreaking NIHR-supported international study has shown that Alzheimer's disease biomarkers can be accurately detected using simple finger-prick blood samples. These samples can be collected at home and posted to laboratories without refrigeration or prior processing.

The research, led by US institute Banner Health working with the University of Exeter Medical School and supported by the NIHR, has been published in Nature Medicine

It represents the first large-scale validation of a testing approach that could take place anywhere in the world without requiring specialised healthcare infrastructure. 

The DROP-AD project

The DROP-AD project was conducted at 7 European medical centres, including the University of Exeter. It successfully tested 337 participants and proved that finger-prick blood collection can accurately measure key markers of Alzheimer's pathology and brain damage. 

Alzheimer's disease is usually confirmed through brain scans or spinal fluid tests. These are invasive and expensive. Blood tests that measure biomarkers, such as p-tau217, are emerging as accurate and accessible tools for detecting it. 

Drawing blood through venipuncture (inserting a needle into a vein) is much simpler than spinal taps or brain scans. However, practical hurdles remain, including how samples are handled and stored, and whether people have access to trained staff to collect them.

Professor Nicholas Ashton, senior director of Banner's Fluid Biomarker Program and lead investigator of the study, said: 

"This breakthrough could fundamentally change how we conduct Alzheimer's research by proving that the same biomarkers doctors use to detect Alzheimer's pathology can be measured from a simple finger prick collected at home or in more remote community settings. 

“While we're still years away from clinical use, we're opening doors to research that was previously impossible – studying diverse populations, conducting large-scale screening studies, and including communities that have been historically underrepresented in Alzheimer's studies.

“Ultimately, we are moving toward a pathway of treating people for Alzheimer’s disease before symptoms emerge. If this trajectory continues, we will need innovative ways to identify eligible individuals who are not routinely presenting in clinical settings. This work represents one such approach in that direction and further validation remains.”

“This type of research – with the potential to transform diagnosis and care for people with Alzheimer’s disease – showcases the importance of NIHR infrastructure funding and the expertise of its researchers supporting internationally collaborative commercial research.”
Professor Marian Knight, Scientific Director for NIHR Infrastructure

How the study worked

The researchers tested a new method using a few drops of blood obtained from the fingertip and then dried on a card. They used the samples to look for proteins linked to Alzheimer's disease and other brain changes in the 337 participants.

Levels of p-tau217 in the finger-prick samples closely matched results from standard blood tests. They could identify Alzheimer's disease-related changes in spinal fluid with an accuracy of 86%. Two other markers, Glial Fibrillary Acidic Protein (GFAP) and neurofilament light (NfL), were also successfully measured and showed strong agreement with traditional tests.

The University of Exeter Medical School played a pivotal role, recruiting participants from the PROTECT-UK study and serving as the only site to test self-collection capabilities. Participants successfully collected their own finger-prick samples without the guidance of study personnel after watching trained staff and receiving written instructions.

Professor Anne Corbett, Professor in Dementia Research at the University of Exeter, said:  

"What excites me most is that this work makes this type of research far more accessible. We're moving toward a future where anyone, anywhere, can contribute to advancing our understanding of brain diseases. This isn't just a technical advancement – it's a paradigm shift in how we conduct neuroscience research.”

Co-author Professor Clive Ballard, Professor of Age-Related Diseases at

the University of Exeter Medical School, added: 

“Our ongoing work will determine whether this could also be a valuable way of identifying people in the community who would benefit from more detailed diagnostic tests for Alzheimer’s disease.”

Professor Marian Knight, Scientific Director for NIHR Infrastructure, said:  

“This type of research – with the potential to transform diagnosis and care for people with Alzheimer’s disease – showcases the importance of NIHR infrastructure funding and the expertise of its researchers supporting internationally collaborative commercial research. The future potential to enable testing in different settings outside of hospital clinics is hugely exciting.”

Application beyond Alzheimer’s 

The method also shows promise for research applications beyond Alzheimer's, including studies of Parkinson's disease, multiple sclerosis, ALS and brain injuries by the detection and accurate measurement of NfL, a key biomarker of neurodegeneration.

The findings suggest that this simple technique could make large-scale studies and remote testing possible, including for people with Down syndrome, who face a higher risk of Alzheimer's disease and for other underserved populations.

The researchers emphasize that significant additional research and validation is required before any clinical application and caution that the method is not ready for clinical use yet.

  • A minimally invasive dried blood spot biomarker test for the detection of 28 Alzheimer’s disease pathology is published in Nature Medicine.

 

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Thursday, September 12, 2019

New AI detects heart failure with 100% accuracy

With the help pf Artificial Intelligence (AI), researchers have developed a neural network developed that can accurately identify congestive heart failure with 100% accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat.

Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.


The researchers have worked to tackle these important concerns by using Convolutional Neural Networks (CNN)- hierarchial neural networks highly effective in recognising patterns and structures in data.


We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100% accuracy by checking just one heartbeat we are able to detect whether or not a person has heart failure, said the researcher.


The Prof. said, our model is also one of the 1st known to be able to identify the ECG's morphological features specifically associated to the severity of the condition.


The research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors. Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100% accuracy.


With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology, said the researcher.



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Saturday, June 01, 2019

MRI can be used to diagnose heart disease

Magnetic Resonance Imaging (MRI) can be used to measure how the heart uses oxygen for both healthy patients and those with heart disease, a study has found.

Reduced blood flow to the heart muscle is the leading cause of death in the Western world, said researchers. Currently, the diagnostic tests available to measure blood flow to the heart require injection of radioactive chemicals or contrast agents that change the MRI signal and detect the presence of disease.


There are small but finite patients associated risks and it is not recommended for a variety of patients including those with poor kidney function. This new method, cardiac functional MRI ( cMRI), does not require needles or chemicals being injected into the body, said a researcher. It eliminates the existing risks and can be used on all patients, he said. Our discovery shows that we can use MRI to study heart muscle activity, he said.  have been successful in using a pre-clinical model and now we are preparing to show this can be used to accurately detect heart disease in patients,he said.


Repeat exposure to carbon-di-oxide is used to test how well the heart's blood vessels are working to deliver oxygen to the muscle. A breathing machine changes the concentration of carbon0di-oxide in the blood. This change should result in a change in blood flow to the heart, but does not happen when disease is present. The cMRI method reliably detects whether these changes are present. Other researchers have explored oxygenation-sensitive MRI but initial results contained a high level of noise with blurry images, Project leader, believed that the noise was actually variation in the heart's processing of oxygen.


He engineered a way to average this variation and through testing, the team discovered that the noise is actually a new way to study how the heart works. We've opened the door to a new era and totally novel way of doing cardiac stress testing to identify patients with ischemic heart disease, the researcher said.This approach overcomes the limitations of all in the current diagnostics-- there would no longer be a need for injections or physical stress testing like running or treadmills, he said.


Using MRI will not only be safer, than present methods, but also provide more detailed information and much earlier on in the disease process, he said. Following initial testing through clinical trials, he said the technique may be used with patients clinically within a few years. In addition to studying coronary artery disease, the method could be used in other cases where heart blood flow is affected such as the effects of a heart attack or damages to the heart during cancer treatment. Due to its minimal risk, the new tool could be safely used with the same patient multiple times to better select the right treatment and find out early on if it is working.


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Tuesday, May 21, 2019

Google AI may be able to predict lung cancer more accurately

A team of Google researchers has used a deep-learning algorithm to detect lung cancer accurately from computed scans.
The work demonstrates the potential for Artificial Intelligence (AI) to increase both accuracy and consistency, which could help accelerate adoption of lung cancer screening worldwide.

Lung cancer is the deadliest of all cancers worldwide -- more than breast, prostate, and colorectal cancers combined -- and it's the sixth most common cause of death globally, according to the World Health Organization.

"Using advances in 3D volumetric modeling alongside datasets from our partners, we've made progress in modeling lung cancer prediction as well as laying the groundwork for future clinical testing,"  explained a researcher.

Google researchers created a model that can not only generate the overall lung cancer malignancy prediction (viewed in 3D volume) but also identify subtle malignant tissue in the lungs (lung nodules).

In the research, Google AI leveraged 45,856 de-identified chest CT screening cases (some in which cancer was found). 


"When using a single CT scan for diagnosis, our model performed on par or better than the six radiologists. We detected five per cent more cancer cases while reducing false-positive exams by more than 11 per cent compared to unassisted radiologists in our study," said Google.

For an asymptomatic patient with no history of cancer, the AI system reviewed and detected potential lung cancer that had been previously called normal.

These initial results are encouraging, but further studies will assess the impact and utility in clinical practice, said Google.


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Saturday, December 16, 2017

AI may help identify bacteria quickly and accurately

Microscopes enhanced with artificial intelligence (AI) may help clinical microbiologists diagnose potentially deadly blood infections and improve patients’ odds of survival.

Researchers demonstrated that an automated AI-enhanced microscope system is “highly adept” at identifying images of bacteria quickly and accurately.

“This marks the first demonstration of machine learning in the diagnostic area,” said a Dr. “With further development, we believe this technology could form the basis of a future diagnostic platform that augments the capabilities of clinical laboratories, ultimately speeding the delivery of patient care,” the Dr. said.

According to the study published , the researchers used an automated microscope designed to collect high-resolution image data from microscopic slides. They trained a convolutional neural network (CNN) - a class of artificial intelligence to analyse the visual data and then categorise the bacteria based on their shape and distribution.The machine intelligence sorted the images into three categories of bacteria (rod-shaped, round clusters, and round chains or pairs), with nearly 95 percent accuracy.

Automated classification can “conceivably reduce technologist read time from minutes to seconds,” added the Dr.

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