Computer Vision Empowering Healthcare and Manufacturing Industries
Our Jumpstart Program Success Story 1:
A Sensor Reduction Solution for Pipelines
Client:· A large oil refinery and a pipeline company, an integral part of an industrial giant. They have 30 production facilities and over 4000 miles of pipelines across multiple states. Challenge:· Their challenge is to keep the corrosion in check, to ensure safety and product quality. · For
Our Jumpstart Program Success Story 2:
Machine Vision Solution for Marker Detection and Tracking
Client:· A team of neuroscientists at a well-known research university, in Chicago. · Their research objective:Investigate dysphagia or swallowing disorders in humans caused by deteriorating neurological and brain conditions. · They have shot 1000’s of videos of rodents and prime mates, recoding their chewing and swallowing behavior under
Our Jumpstart Program Success Story 3:
Pharmacy Rx FWA Detection
Client · A Pharmacy Benefits Manager · There are about 4 billion prescriptions written is US alone every year. · FBI estimates, potentially, up to about 10% of the transactions can be classified as FWA Challenge:· FWA detection · Current process is manual, can only find known fraud signatures
Fiducial Marker Tracking Using Machine Vision
A Session by Saurabh Ghanekhar, Kavi Global, at the Spark+AI Summit, SFO, June 2018
Advanced machine vision is increasingly being used to investigate, diagnose, and identify potential remedies and their progressions for complex health issues. In this study, a behavioral neuroscientist at the University of Chicago and his colleagues have collaborated with Kavi Global to characterize 3D feeding behavior and its potential changes caused
Pharmacy Claims Fraud Detection Using Apache Spark
A Session by Giridharan Gurumuurthy, Kavi Global, at the Spark+AI Summit, SFO, JUNE 2018
Up to 10% of the pharmacy claims submitted to health plans and insurance companies are estimated to be fraudulent. A two-step process comprising of a combination of unsupervised and supervised learning techniques can be used to effectively identify the Fraud, Waste and Abuse in the Pharmacy industry. Claims submitted by
