Qmetrics can uncover important data insights by seeing more. Whether it is automatically segmenting hard-to-detect features of the knee or leveraging machine learning to detect early mild cognitive impairment in the brain, Qmetrics expertise is unique in the industry.
Now, Qmetrics is pleased to announce its new service, DiscernAI™. DiscernAI improves data analyses through the use of artificial intelligence (AI) and machine learning (ML). DiscernAI’s data mining platform includes proprietary software and a growing catalogue of machine learning-based “signatures.” A DiscernAI Signature is a set of quantified clinical and post-processed imaging features that identify unique patient characteristics, disease states, or treatment responses. The DiscernAI platform has been developed over many years by Qmetrics’ imaging and data science experts.
By using DiscernAI to see more, Qmetrics brings unique value to biopharma and CROs. For example:
Clinical trials can take less time by knowing the deeper characteristics of patients that are responsive to the treatment.
Clinical trial efficiency can be increased, and budgets reduced, through automated advanced image analysis.
The risk and cost of failed trials can be reduced with a deeper understanding of patient phenotypes and the ability to optimize the stratification of patient cohorts.
Immediate data insights for clinical trials can be achieved by applying DiscernAI Signatures to predict knee osteoarthritis pain or to detect early stage Alzheimer’s Disease.
DiscernAI can analyze multi-modal data, including patient reported data, clinical EMR data, genetic data, and medical image analysis. DiscernAI uses a proprietary approach to pre-processes image data using radiomics and feature extraction to enable improved feature selection versus analyses of raw image data only.
Qmetrics’ DiscernAI service can be deployed retrospectively and prospectively, with either an existing DiscernAI Signature or based upon a ML discovery process to find a new and relevant signature. Customized approaches are possible through further collaboration with Qmetrics.
Examples of applying the DiscernAI Signatures include:
Retrospective analysis using a DiscernAI Signature, based on clinical data and brain MRIs from a failed Alzheimer’s trial, to determine the proportion of “true” Alzheimer’s patients versus other types of dementia patients in the clinical trial cohort.
A prospective application of a DiscernAI Signature to detect early stages of mild cognitive impairment to ensure the earliest stage Alzheimer’s patients can be included in a clinical trial.