WoundExam’s intelligent architecture to help clinicians
WoundExam uses multiple proprietary techniques to provide insights and help in clinical decision-making like no other company.
WoundExam uses an approach of measuring skin lesions of interest with multiple spectral sensors and custom-made Machine Learning algorithms to create and identify ‘spectral signatures.’ These signatures are translated to unique ‘compositional fingerprints’ using developed AI models. All the ‘compositional fingerprints’ extracted from each sensor is integrated (fused) in a separate AI module for the accuracy enhancement. Based on desired type of analysis (e.g. pre-ulcer risk, healing progress, categorization, etc.), proprietary machine learning based classifiers will be triggered to output (display) the desired results.
WoundExam clinical flow approach is to take a holistic view of how the patient is cared for to reveal overlooked ways of improving care for the patient. Pressure Ulcer healing is depending on many factors, including Social Determinants. While WoundAssure provides value to the clinician and patient without any additional advanced technology, by including a holistic view of the patient, WoundAssure will be able to provide feedback and insights to help clinicians and facilities provide the best care for patients in the most efficient, caring way possible.
Multimode Imaging architecture shows layered different combinations of sensor technologies designed for specific needs, with considerations include size, weight, cost and portability. As funnel becomes wider, it has the capability of larger field of view, faster, and lower equipment cost. As funnel becomes narrower, We will reach higher accuracy/specificity while higher equipment cost.