Phase II Amount
$1,493,151
The PainQx ALGOS System is a machine learning based medical device that can objectively assess the intensity of pain being experienced by chronic pain patients. The ALGOS System achieves this by assessing neural activity from a patients brain using electroencephalography (EEG) and processing the data through proprietary algorithms. The PainQx platform can currently classify subjects into a pain level of No Pain, Mild/Moderate Pain, or Severe Pain that correlates with the patients self-reported NRS Pain Score. The core of the ALGOS system is a series of software modules used to process the EEG and produce the pain classification. The modules include an overall signal quality check, removal of contaminated EEG segments, selection of the most useful EEG segments, QEEG feature extraction and finally classification via a discriminant function composed of a weighted set of QEEG features. The QEEG features used and the weights assigned to them are determined through the rigorous application of PainQxs machine learning methodology and confirmed through domain knowledge and previously published literature. The objectives for the proposed project are to conduct a 300-patient Clinical Study, then utilize study data to refine the performance of PainQxs current pain intensity assessment algorithm(s) and develop additional objective algorithms to assess pain related conditions aligned with components of the Department of Defense Pain Assessment Screening Tool and Outcomes Registry (PASTOR). Additionally, we will utilize final ALGOS product specifications, algorithm testing results and end user Pain Clinician feedback to establish documentation and data required to successfully conduct a follow-up FDA Pre-Submission Meeting in preparation for an FDA Validation Study and FDA Clearance. In sum, the proposed project will ensure PainQx meets algorithm performance targets and end user needs necessary to enter an FDA Validation Trial, obtain clearance and successfully commercialize the ALGOS system.
