From Storage to Smart Triage

A Unified Cloud System for Secure Medical Image Access and Real-Time Clinical Prioritization

Transforming medical imaging through cost-effective cloud-based open-source mobile-first diagnostic support framework with AI-driven triage and privacy-preserving DICOM repositories.

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Cloud Storage
AI Triage
Secure Access

About the Project

Revolutionizing medical imaging infrastructure for global healthcare

The Challenge

Medical imaging benefits remain unevenly distributed globally. While advanced hospitals leverage sophisticated digital systems, the WHO estimates that two-thirds of the global population still lacks basic radiology services. Current systems suffer from limited access, surging data loads, high costs, and interoperability gaps.

Our Solution

We propose a Cloud-Based Open-Source Medical Diagnostic Support Framework that transforms how imaging data is stored, shared, and utilized. This innovative framework establishes a multimodal DICOM image repository in the cloud with built-in privacy safeguards.

DICOM Repository

Multimodal cloud repository with privacy safeguards

AI-Driven Triage

Automated prioritization of critical cases

Privacy Compliance

HIPAA, GDPR compliant data handling

Zero-Footprint Viewer

Browser-based secure image access

₹12.80L
Total Project Cost
24
Months Duration
2/3
Global Population Lacks Basic Radiology

Research Framework

Comprehensive cloud-based medical imaging workflow

Scalable cloud-based medical diagnostic imaging workflow

Figure 1: Scalable Cloud-Based Medical Diagnostic Imaging Workflow

Comprehensive workflow integrating cloud storage, AI triage, and secure access for diagnostics centers, patients, and doctors.

Patient-centric workflow

Figure 2: Patient-Centric Workflow

Secure information flow from hospital systems to encrypted cloud storage with role-based access control.

Project Objectives

01

Multimodal DICOM Repository

Create a privacy-preserving DICOM image repository to support real-world validation of cloud-based medical imaging framework.

02

Automated Pipeline

Develop automated pipeline to parse, de-identify, and structure DICOM metadata in compliance with international health data privacy standards.

03

Secure DICOM Proxy

Implement secure, standards-compliant DICOM proxy for real-time, encrypted communication between imaging devices and cloud platforms.

04

AI-Driven Triage

Integrate AI-driven triage for intelligent routing and prioritization of critical cases with zero-footprint viewers.

Research Team

Expert researchers from academia and industry

Dr. Rahul Upadhyay

Investigator

Associate Professor

Department of Electronics and Communication, TIET

Biomedical Image & Signal Processing, AI, Brain Computer Interface

Dr. Vinay Kumar

Investigator

Professor

Department of Electronics and Communication, TIET

Image & Video Processing, AI, Natural Language Processing

Dr. Mudit Gupta

Investigator (Industry)

Radiologist & co-CEO

Saral Advanced Diagnostic Private Limited

Data collection and decision making from imaging

Dr. Nitin Arora

Investigator

Assistant Professor

Department of Computer Science Engineering, TIET

Computer Vision, Content-based Image Retrieval

Mr. Sarthak Tyagi

Intern

Computer Science Graduate

Department of Computer Science Engineering, TIET

Data collection and decision making from imaging

Mr. Ritesh Kapoor

Intern

Computer Science Graduate

Department of Computer Science Engineering, TIET

Data collection and decision making from imaging

Project Timeline

24-month milestone overview

Project Gantt Chart

M1: Foundation (Months 1-4)

Project setup, requirements analysis, and initial framework design

M2: Core Development (Months 4-8)

DICOM repository implementation and metadata processing pipeline

M3: Security & Integration (Months 8-12)

Secure proxy implementation and privacy compliance features

M4: AI Implementation (Months 12-16)

AI triage algorithms and intelligent prioritization system

M5: Testing & Optimization (Months 16-20)

System testing, performance optimization, and user interface development

M6: Deployment & Validation (Months 20-24)

Real-world deployment, validation, and documentation

Expected Impact

Transforming healthcare delivery globally

Healthcare Providers

Low-cost, vendor-neutral solution eliminating costly on-premise servers and providing instant, remote image access for enhanced telemedicine.

Patients

Improved access to imaging results and faster diagnoses when time-critical conditions are automatically flagged by AI systems.

Global Healthcare

Democratizing imaging availability and enabling next-generation workflow that is more efficient, interoperable, and equitable.

Research Advancement

Open-source framework fostering transparency and global collaboration in medical imaging research and development.

Contact Information

Get in touch with our research team

Institution

Centre of Excellence in Data Science and Artificial Intelligence
Thapar Institute of Engineering and Technology
Patiala 147004, India

Email

rahul.upadhyay@thapar.edu; vinay.kumar@thapar.edu

Project Duration

24 Months (April 2025 - March 2027)