Our proprietary platform enables us to ingest various types of data to better characterize individuals (imaging, genetic, phenotypic and clinical variables).
We are able to rapidly and precisely target subgroups of interest in large, heterogeneous populations.
Perceiv’s biomarkers are highly specific and thoroughly validated, making them ready for action in any clinical setting.
We reduce the cost and length of clinical trials by using our digital biomarkers for smarter patient enrollment.
Our platform helps our partners gain novel insights from their raw data
We improve treatment outcomes by better targeting responders, and by helping our partners re-prioritize low efficacy treatments via companion diagnostics.
Patient variability presents a significant problem in Alzheimer’s drug development: about 99.6% of Alzheimer’s disease trials fail. Using our high precision digital biomarkers, we can enrich trials to predict disease progression and identify the subgroups of patients most suitable for Alzheimer’s disease clinical trials, thereby greatly increasing the likelihood of trial success. We can also predict and pre-screen amyloid-beta positive subjects using readily-available modalities on our predictive platform.
Clinical trials assessing post-myocardial infarction treatment are particularly challenging due to their difficulty in predicting disease evolution as well as patient responses to drugs, as only 10% of recruited patients may develop a second cardiovascular event. The ideal trial should include a high percentage of these patients to demonstrate the benefit of the investigated drug. Even more, this makes them require enormous patient sample sizes. To address these challenges, we are developing AI-driven tools to optimize patient selection allowing for better management of resources, to improve clinical trial execution, reducing research costs while targeting the right patient for the right treatment.
Dr. Dansereau has over a decade of experience doing machine learning research applied to neuroimaging and medical data. Ph.D. in Computer Science from University of Montreal. Master in Biomedical Engineering from McGill University. Bachelor degree in Electrical Engineering.
Mr. Laurent leads the software and machine learning development for Perceiv AI. He is a Deep Learning researcher from MILA supervised by Dr. Pascal Vincent. He has extensive experience with medical data and large scale machine learning framework development.
Dr. Tam did her postdoctoral fellow at the National University of Singapore and PhD in neuroscience from McGill University. Expertise in multimodal imaging of neurological diseases and psychiatric disorders.
Dr. Noriega de la Colina's experience is in clinical research focussing in cardiovascular and neurodegenerative disorders. Ph.D. candidate in Biomedical Sciences from University of Montreal. Doctor in Medicine (M.D.). M.Sc. Health Economics from the London School of Economics. Specialized training in clinical research from Harvard Medical School.
Mr. Rajchgot has experience in business development, strategic analysis, neuropharmacology research, and venture capital. He earned a Master of Science in Physiology (Georgetown University), an M.Sc. in clinical pharmacology (Université de Montréal), and a Bachelor of Science (McGill University).
Recipient of the Turing Award from the Association for Computing Machinery (ACM). Officer, Order of Canada. Professor, Department of Computer Science and Operations Research, University of Montreal. Canadian Research Chair in Statistical Learning, CIFAR Fellow, NSERC/Ubisoft Industrial Research Chair. Ph.D. in Computer Science, McGill University; Post-Doctorate at MIT and AT&T Bell Labs.
Director of the Alzheimer’s Disease Research Unit at McGill University, Montreal, Canada. Professor of Neurology & Neurosurgery, Psychiatry and Medicine at McGill University, Montreal, Canada. Member, Douglas Mental Health University Institute’s Research Center. Membre Associé, Institut Universitaire de Gériatrie, Université de Montréal.
Cardiologist, former president of CAE healthcare, professor of medicine and well-published researcher. An accomplished executive and board member of The Royal College International. Simulation Hall of Fame inductee (2018).
Certified corporate board director with 20+ years of international executive experience in NASDAQ-listed, BioPharma companies (Merck, Novartis, Celgene and Intercept) in the US, Canada and Europe. Extensive global operations and go-to-market medical-marketing expertise across therapeutic areas with several blockbusters and pipeline compounds. Active mentor for several MedTech start-ups. Manon has a master’s in nursing from McGill/University of Montreal, an executive MBA from McGill/HEC, she is a certified corporate board director from Harvard Business School.
Scientific director of the functional imaging unit. Researcher at the CRIUGM, Computer Science and operations research, University of Montreal. Co-lead of the biomarker team of the Canadian Consortium on Neurodegeneration in Aging (CCNA).
Expertise in financial, health and medical technology sectors. Experienced in startup, growth and governance of companies. Board member of Montreal CHUM hospital.
Founder and chief scientific officer at KalGene Pharmaceuticals. University and industrial research scientist.
Headquartered in Montreal’s Innovation District, Perceiv AI is a precision medicine technology company founded in 2018 and dedicated to improving treatment efficacy through refined patient selection. Thanks to multiple partnerships with pharmaceutical companies, hospitals, and research centres, Perceiv AI is developing digital biomarkers to help clinical trials succeed by pairing multimodal biomedical data and proprietary artificial intelligence technology to target the most suitable patients. Our turn-key solutions can be used for prognostic enrichment and for precise targeting of responders to a particular therapeutic intervention. Perceiv AI is helping build the future of precision medicine.
Perceiv is currently developing applications for Alzheimer's disease and cardiovascular disease.