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Edge devices are revolutionizing diagnostics. Edge devices can reside withinor adjacent to imaging tools such as digital Xray, CT, MRI, or ultrasoundequipment. These devices are either CPUs or GPUs with advanced processing deepand machine learning (artificial intelligence) algorithms that assist inclassification and triage solutions to flag studies as either normal orabnormal, TB or healthy (in case of TB screening), suspected COVID-19/otherpneumonia or unremarkable (in hospital or hotspot settings). These can bedeployed as screening point-of-care (PoC) solutions; this is particularly truefor digital and portable X-ray devices. Edge device learning can also be usedfor mammography and CT studies where it can identify microcalcification andstroke, respectively. These solutions can be considered the first line ofpre-screening before the imaging specialist actually reviews scans and makes afinal diagnosis. The key advantage of these tools is that they are instant, canbe deployed remotely where experts are not available to perform pre-screeningbefore the experts actually review, and are not limited by internet bandwidthas the nano learning data centers are placed next to the device.

Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screening