Dr Jose Bernal

If you’re interested in testing DRIPS on your own data or collaborating on new applications, feel free to reach out.

Grateful to Luna Bitar and Mario Díaz for their outstanding work, and to all collaborators who contributed through data collection, manual segmentation, and writing — this project truly was a collective effort.

The segmentation performance of DRIPS remained stable regardless of white matter hyperintensity (WMH) burden, unlike other methods that were biased by their presence.

DRIPS generally outperformed both classical filtering approaches and more recent deep-learning methods, achieving consistent PVS segmentation across imaging data from participants with diverse health conditions.

Out-of-sample, DRIPS accurately segmented PVS across real T1- and T2-weighted scans, isotropic and anisotropic resolutions, and even an ex vivo histology reconstruction — without needing retraining or fine-tuning.

DRIPS synthesises brain images with PVS-like structures and corresponding label maps on the fly, combining anatomical priors with simulated artefacts such as noise, motion, and bias fields to teach networks robust representations.

Current segmentation methods often fail when moving across scanners or cohorts — they depend on extensive manual annotations, retraining, or contrast-specific tuning. This is not sustainable in the long run.

📢 New Preprint 📢
After 3 years of work, I’m proud to present DRIPS (Domain Randomisation for Image-based PVS Segmentation), a new method for accurate & generalisable perivascular space (PVS) segmentation across diverse MRI contrasts and resolutions. @fau.de @dzne.science @svdresearch.bsky.social

As a team, we developed targeted recommendations aimed at accelerating the integration of ML+CSVD into clinical practice.

In need of improvement: Serious methodological issues—such as inconsistent reporting, limited testing of generalisability, and other potential biases—remain common and continue to hinder broader adoption.

Positive: There is growing interest in incorporating CSVD markers into ML models for classifying neurodegenerative diseases, and current performance indicates that this approach merits further investigation.

We carried out a systematic review and meta-analysis to examine the use of neuroimaging CSVD markers in machine learning–based diagnosis and prognosis of cognitive impairment and dementia, while also identifying methodological trends over time and barriers to clinical translation.

Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis - Alzheimer's Research & Therapy Cerebral small vessel disease (CSVD) is a common neurological condition that contributes to strokes, dementia, disability, and mortality worldwide. We conducted a systematic review and meta-analysis t...

🚨 Hot off the press! 🚨
Our systematic review and meta-analysis on ML+CSVD for dementia diagnosis and prognosis has been published! An international collaboration, featuring @Valerie Lohner
@oparent.bsky.social
@helenagellersen.bsky.social
and many others.

doi.org/10.1186/s131...
@dzne.science

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge - Nature Communications Federated learning (FL) algorithms have emerged as a promising solution to train models for healthcare imaging across institutions while preserving privacy. Here, the authors describe the Federated Tu...

📢 Hot off the press! 📢

Want to show that healthcare AI really works in everyday settings? Large-scale, multi-site studies are a powerful path forward. I’m thrilled to have joined this effort as part of the Colombian collaborator team.

doi.org/10.1038/s414...

@dzne.science

Take home 🏠 message:
Cerebrovascular and ageing-related atrophic changes are interrelated and shape cognitive trajectories. Trajectories may nonetheless be modified for the better by managing cerebrovascular and mental health while fostering cognitive engagement.

(B) WMH progression and ageing-related atrophy were independently associated with changes in cognitive performance, underscoring their relevance for brain maintenance.

(C) Neuroticism, depression, and low cognitive engagement predicted worse domain-specific trajectories.

Longitudinal evidence for a mutually reinforcing relationship between white matter hyperintensities and cortical thickness in cognitively unimpaired older adults - Alzheimer's Research & Therapy Background For over three decades, the concomitance of cortical neurodegeneration and white matter hyperintensities (WMH) has sparked discussions about their coupled temporal dynamics. Longitudinal st...

(A) WMH not only co-occur with medial temporal lobe atrophy but may also contribute to and accelerate its progression. We observed this coupling also in a prior study (doi.org/10.1186/s131...). This finding suggests that preserving microvasculature may reduce vulnerability to neurodegeneration.

Using four-year data from 543 cognitively unimpaired participants from the DELCODE study and latent growth curve modelling, we examined the co-evolution of white matter hyperintensities (WMH), medial temporal lobe to ventricle ratio, and cognitive performance.

🚨New preprint 🚨
TL;DR Supporting cerebrovascular and mental health, along with staying cognitively engaged, may help preserve brain function and delay cognitive decline and dementia.
@dzne.science

I had no clue you were working on CSVD!!!

📢 Habemus preprint 📢
Our systematic review and meta-analysis on ML+vascular neuroimaging for dementia diagnosis and prognosis is out! Fantastic collaboration with @ValerieLohner, @oparent.bsky.social @helenagellersen.bsky.social and many others! Check it out! @dzne.science