Born out of the Paris Brain Institute (ICM) at Hôpital de la Pitié-Salpêtrière, Qairnel’s algorithms represent over a decade of peer-reviewed mathematical breakthroughs in statistical progression modeling and Riemannian geometry applied to the human brain.
Validated on nearly 5,000 Alzheimer’s patients and benchmarked as the top-performing progression forecasting algorithm in the international TADPOLE Challenge, published in Nature Communications.
Published in leading medical and machine learning journals including Nature Communications, The Lancet Digital Health, Scientific Reports, and JMLR.
Exclusive worldwide patents (patented since 2016) calibrated across multi-center cohorts in Alzheimer’s, Parkinson’s, Huntington’s, and ALS.
Explore how CourseMap AI and DocMemo have been deployed and validated alongside leading pharmaceutical sponsors and clinical research networks.
Evaluated CourseMap AI digital twins on Sanofi’s MOVES-PD trial (venglustat in GBA1-associated PD), integrating prognostic covariates into standard MMRM models.
Embedded CourseMap AI directly within a Top-10 global pharmaceutical sponsor’s secure environment on blinded baseline data during an ongoing multicenter Phase 2 trial.
Activated DocMemo’s 24/7 community screening across clinical sites in Florida and Pennsylvania for two late-phase Early Alzheimer’s Disease programs.
Validated CourseMap AI across nearly 5,000 Alzheimer’s disease patients from multi-center cohorts, demonstrating #1 predictive performance compared against 56 competing methods in the international TADPOLE Challenge and showing 15%–25% clinical trial sample size reduction.
Foundational spatiotemporal Riemannian mixed-effects framework mapping multimodal cognitive, cortical atrophy, and biomarker cascades along an individualized disease progression time axis.
Large-scale longitudinal analysis of prodromal risk factors and early clinical markers occurring up to 15 years prior to formal Alzheimer's disease diagnosis, informing DocMemo's early community risk stratification.
Demonstrates ~15–16% gain in statistical power and narrower treatment effect confidence intervals using CourseMap AI prognostic digital twins in Sanofi’s Phase 2 MOVES-PD trial.
Evaluates baseline progression forecasting and enrichment vs. covariate adjustment strategies on Phase 2 Parkinson’s disease trial data.
Quantifies statistical power improvements on time-to-event and clinical worsening endpoints when incorporating digital twin prognostic covariates.
Demonstrates how prediction-powered inference with CourseMap AI digital twins reduces required Phase 3 sample sizes from 361 to 304 patients.
Real-world validation of DocMemo’s permanent community screening infrastructure across the US and France, accelerating pre-qualified referrals to clinical trial sites.
Deep phenotyping of subjective memory complainers in the general population using decentralized digital cognitive assessments and risk factor profiling.
Comparative analysis of self-referred versus informant-referred individuals on the DocMemo platform to optimize early Alzheimer’s trial pre-qualification.
Both the FDA (May 2023 Final Guidance) and EMA explicitly encourage adjusting primary analyses for prognostic baseline covariates to increase precision and statistical power in randomized clinical trials.
