A low-cost AI screening system for rural clinics, accepted at an IEEE conference

Over 65% of India's population lacks timely access to diagnostic imaging. Abhigna set out to close that gap with ArogyaScan, a point-of-care screening framework that community health workers without specialist training can run. It screens pregnancy, liver, kidney and gallbladder conditions from ultrasound and lung conditions from chest X-rays, using organ-specific deep learning classifiers. The best model reached 98.08% accuracy on lung classification, held up on external validation, and the full system costs 70 to 80% less than a conventional hospital ultrasound setup while running fully offline.

FIELDMedical AI & Global Health

RESULTFirst-author paper accepted at an IEEE conference

VENUEIEEE, 2026

Abhigna Naik, YRI FellowIEEE

BEFORE THE FELLOWSHIP

A student at Chirec International School in Hyderabad who cared about the diagnostic gap in rural India but had never turned that concern into research.

AFTER

First author of ArogyaScan, an offline, low-cost AI screening framework accepted at an IEEE conference, with lung screening accuracy above 98%.

THE LEDGER

01First-author paper accepted at an IEEE conference

0298.08% accuracy and 98.06% abnormal-class recall on lung classification

0397.20% recall on external validation against a 1,000-image COVID-19 radiography set

04Hardware cost reduced 70 to 80% versus conventional hospital ultrasound

05Built to run fully offline, with regional language voice output

NEXT CASE STUDYAkhilesh Kuppili, First-author paper accepted at an IEEE conference