Qynapse unveils QyPredict and QyScore data for early Alzheimer’s detection at AAIC 2025

Qynapse, a Toronto- and Paris-based neuroimaging AI company, has unveiled compelling new clinical evidence at the 2025 Alzheimer’s Association International Conference (AAIC) supporting the capabilities of its flagship platforms—QyScore and QyPredict—in identifying and tracking neurodegeneration in the earliest phases of Alzheimer’s disease (AD). The announcement marks a critical milestone in the convergence of artificial intelligence, imaging biomarkers, and precision medicine for neurodegenerative disorders.

QyScore is Qynapse’s FDA-cleared and CE-marked neuroimaging platform that automates the segmentation of brain structures from MRI scans. The software demonstrated superior accuracy in segmenting key regions involved in neurodegeneration compared to incumbent academic tools such as FreeSurfer, FSL, and ANTs. Meanwhile, QyPredict—a research-use-only machine learning platform leveraging QyScore outputs—delivered statistically significant predictive power in distinguishing patients with preclinical Alzheimer’s disease and mild cognitive impairment (MCI) who are likely to experience cognitive decline over a two-year period.

These results not only reinforce Qynapse’s technology validation trajectory but also reflect the broader industry push toward biomarker-guided patient selection in clinical trials. As pharmaceutical pipelines face increasing pressure to prove efficacy in early-stage Alzheimer’s populations, tools like QyPredict offer a critical data-driven advantage.

How QyScore Improves Imaging Accuracy in Neurodegenerative Research

QyScore has established itself as a high-resolution, fully automated brain MRI analysis platform targeting neurodegenerative diseases such as AD, Parkinson’s disease, and multiple sclerosis. At AAIC 2025, Qynapse reported benchmarking data comparing QyScore to leading open-source tools. Across key brain structures—including the hippocampus, amygdala, and entorhinal cortex—QyScore achieved higher segmentation accuracy and test-retest reliability, both critical metrics in longitudinal tracking of brain atrophy.

MRI segmentation accuracy is a foundational challenge in both research and clinical neurology, particularly for conditions with slow progression. QyScore’s automation not only improves reproducibility but also shortens processing time for radiology and clinical research teams. Given the FDA and CE clearances, QyScore is now actively being used in hospital systems and by biopharma sponsors to generate imaging biomarkers in prospective trials.

This focus on measurement precision aligns with recent regulatory trends from the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA), which are encouraging the use of digital endpoints and imaging quantification tools to complement cognitive scoring and PET-based assessments in AD drug development.

QyPredict Enables Early Risk Stratification in Preclinical Alzheimer’s Trials

QyPredict, Qynapse’s investigational platform, builds on QyScore outputs to predict the trajectory of cognitive decline in patients who are asymptomatic or have mild cognitive impairment. Using historical imaging and cognitive data, QyPredict applies machine learning algorithms to model future decline, offering a forward-looking risk stratification tool for both clinical trials and future clinical deployment.

At AAIC 2025, the company presented data showing that QyPredict successfully filtered out 73.7% of predicted stable participants from the modeled placebo group in a preclinical Alzheimer’s population. This twofold enrichment of declining patients in the remaining placebo group translated to significantly enhanced statistical power (p < .001) in detecting treatment effects.

The model’s predictive performance extended previous 12-month findings in MCI and AD populations to a longer 24-month window in preclinical Alzheimer’s, a notoriously difficult population to track due to subtle symptomatology. By identifying participants at higher risk of decline, QyPredict can potentially enable smaller, shorter, and more efficient clinical trials—addressing a key economic challenge in AD drug development.

Expert Commentary Supports Clinical Impact Potential

The clinical significance of Qynapse’s predictive modeling work was underscored by key academic endorsements. Dr. James E. Galvin, a professor of neurology at the University of Miami and a prominent AD researcher, commented on the difficulty clinicians face in determining which preclinical patients will worsen cognitively.

“AD treatments will be most effective when delivered early, but this is also when it is hardest to predict decline,” said Galvin. “While more validation is needed, QyPredict’s ability to enrich for cognitive decliners could be transformative in both research and practice.”

Such sentiment mirrors recent industry dynamics following the FDA’s accelerated approvals of amyloid-lowering drugs like Eisai’s Leqembi (lecanemab) and Eli Lilly’s donanemab. These therapies have shown stronger effects in earlier disease stages, shifting industry priorities to earlier diagnosis and targeted recruitment—a challenge Qynapse is directly addressing.

Industry Demand for AI-Driven Imaging Biomarkers Is Accelerating

Qynapse’s presentation at AAIC comes amid a surge in demand for AI-powered imaging tools in neurology. With over 55 million people living with dementia globally and AD alone accounting for 60–70% of cases, the economic and social burden continues to grow. The Alzheimer’s Drug Discovery Foundation (ADDF) and National Institute on Aging (NIA) have increased funding in biomarker research, specifically calling for AI-enhanced imaging tools to reduce trial failure rates and improve patient stratification.

Moreover, pharmaceutical companies are ramping up AI integrations across their R&D pipelines. Biogen, Roche, and Novartis are all reported to be using machine learning platforms to model progression in neurodegenerative trials. Qynapse’s growing credibility places it in a favorable position for biopharma partnerships, particularly in adaptive trial design and real-world data capture.

Qynapse’s Strategic Path Forward in Alzheimer’s Diagnostics and Trials

Olivier Courrèges, CEO of Qynapse, emphasized the company’s strategy of co-developing imaging-based digital biomarkers with leading drug developers. “We are very encouraged by these new and expanded findings and look forward to validating our offerings further through collaborations with pharmaceutical sponsors,” Courrèges stated.

He also hinted at ongoing discussions with global clinical research organizations (CROs) and hospital systems to deploy QyScore and QyPredict as companion technologies in therapeutic development programs.

Qynapse’s future milestones include further multicenter validation studies, submission of peer-reviewed publications, and regulatory interactions for QyPredict’s potential path toward FDA breakthrough designation or inclusion as an exploratory endpoint in major trials.

Qynapse’s Platform Evolution and Funding Outlook

Privately held Qynapse has raised more than €30 million across several rounds backed by European investors, including Kurma Partners and Go Capital. While not currently publicly traded, the company is rumored to be evaluating strategic funding options, including a potential Series C round or private equity partnership to accelerate commercialization in the U.S. and EU markets.

Revenues remain confidential, but Qynapse is said to be generating income through both SaaS subscriptions for QyScore and custom clinical trial collaborations. The company is competing in a growing neurodiagnostics market projected to reach over $8 billion globally by 2030, with AI-based imaging solutions expected to represent a significant growth vertical.

Where Does This Place Qynapse in the AD Diagnostics Landscape?

As the AD clinical trial landscape becomes more biomarker-driven and precision-focused, Qynapse is emerging as a key enabler of data-rich, AI-guided trial design. While QyPredict remains investigational, its clinical promise is attracting attention from neurologists, trial sponsors, and payer organizations alike.

Investor sentiment, while muted due to the company’s private status, has remained consistently positive in academic and translational research circles. Analysts familiar with the space suggest that Qynapse may follow a similar growth arc as other AI diagnostics companies like Cognoa or Deep 6 AI, which successfully translated research platforms into regulated clinical products.

With strategic collaborations, additional validations, and regulatory traction, Qynapse is poised to become a central player in reshaping how Alzheimer’s disease is quantified, monitored, and ultimately treated.