Deans' stroke musings: ISMRM: MRI-based AI predicts organ aging before disease signs emerge

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Monday, May 18, 2026

ISMRM: MRI-based AI predicts organ aging before disease signs emerge

If you really want to get into the minutiae of your aging. I'm not worried about my aging, I'll just get there.

ISMRM: MRI-based AI predicts organ aging before disease signs emerge

A deep-learning model combining MRI scans with blood markers, imaging-derived biomarkers, and lifestyle data successfully predicted organ-specific biological age, according to poster data presented May 14 at the International Society of Magnetic Resonance in Medicine (ISMRM) meeting.

The model also identified accelerated aging in participants who went on to develop Alzheimer's disease and myocardial infarction, wrote a team led by Veronika Ecker of the University Hospital of Tuebingen in Germany.

"Biological age has the potential to capture the combined effects of genetic, lifestyle, and health-related factors on both individual- and organ-specific aging, but remains difficult to quantify," Ecker and colleagues noted. "Integration of MRI with other health-related data may improve estimation and provide insights into early disease risks."

MRI offers detailed information on both structure and function of the human body, capturing patterns that reflect individual differences in the aging process, the group explained, writing that "these patterns allow estimation of biological age which may diverge from chronological age due to genetic, environmental, and lifestyle factors."

The investigators developed a deep-learning model that combined 3D MPRAGE brain MRI and 2D+t cardiac cine MRI results with imaging-derived biomarkers, blood-based measures, and lifestyle information and applied it to 70,000 UK Biobank participants between the ages of 44 and 83 in an effort to predict biological age of the brain and heart and to identify accelerated aging. Because no ground truth for biological age exists, the model was also trained on a subcohort of healthy individuals in which biological and chronological age were assumed to approximate one another, the group explained.

Overall, Ecker and colleagues reported that the model detected a mean brain age gap of 1.98 years and a heart age gap of 0.81 years in disease subgroups compared with healthy controls – a result which suggests that it captures pathological aging processes before clinical diagnosis, they noted.

Predicted age gaps (= predicted age - chronological age) of the brain and heart across chronological age for a healthy test set (blue, upper row) and in comparison with a diseased subcohort (orange, lower row). Diseased subgroups are defined per organ (brain: patients developing Alzheimer's disease; heart: patients developing myocardial infarction).Veronika Ecker and ISMRM

"Integrating … complementary data sources can strengthen the robustness and interpretability of biological age prediction," the team wrote, concluding that "the model revealed consistent age-related embeddings, outperformed single-modality approaches, and captured accelerated aging in participants at higher disease risk."