AI-Enabled Microscopic Diagnostic Device for Automated Blood Analysis

INTRODUCTION

This project, developed in collaboration with AIIMS Delhi and IIT Delhi, focuses on creating an AI-enabled microscopic device that can scan blood slides and automatically detect CBC parameters, RBC count, and malaria parasites.
Built for hospitals lacking trained pathologists, the device uses digital imaging, automated slide movement, and AI-based analysis to deliver fast and reliable reports at low cost. It combines field research, product design, and engineering into a compact diagnostic solution for rural healthcare.

Discovering Unmet Needs in Diagnostics

Conducted ethnographic research across Tier-1 to Tier-3 hospitals, OPDs, and rural health centers around Delhi to identify pressing clinical gaps.
Out of 300+ documented pain points, one stood out — the shortage of pathologists delaying basic blood diagnostics.
This research validated the need for an AI-driven, autonomous diagnostic device that could analyze blood samples instantly without expert supervision.

From Insight to Opportunity

Applied a structured need-filtering framework to prioritize ideas based on impact, feasibility, and scalability.
The selected problem: developing an AI-enabled device to scan stained blood slides for CBC, RBC, and malaria detection.
This solution aimed to empower under-resourced labs, reduce dependency on trained technicians, and deliver faster, low-cost results.

Conceptualization & Prototype

Built an initial proof-of-concept microscope using a Raspberry Pi-controlled zoom-lens camera and LCD display.
The prototype captured high-quality digital images of stained slides—establishing the technical viability of automated blood-cell imaging and paving the way for AI-based analysis.

Upgraded the device with a 100× optical lens, CNC-machined slide movement, and a 3D-printed casing to ensure stability and portability.
This transition turned a lab experiment into a compact, standalone diagnostic unit integrating electronics, optics, and a touch interface.

Clinical Impact & Future Vision

The device detects RBC counts, malaria parasites, and other abnormalities autonomously, generating instant reports.
Developed with AIIMS Delhi and IIT Delhi, it demonstrates how AI and affordable hardware can bridge the diagnostic gap in rural India — making reliable blood testing accessible, rapid, and pathologist-independent.

Role & Responsibilities

Conducted field research in hospitals to identify gaps in blood diagnostics
Defined the product concept for an AI-enabled, slide-scanning device
Built a Raspberry-Pi based prototype with automated slide imaging
Designed the optical system, CNC slide mechanism, and 3D-printed casing
Captured and tested slide images for AI-driven analysis
Collaborated with AIIMS Delhi & IIT Delhi for clinical inputs and validation