AI-Powered Medical Imaging Analysis Platform

Medical ImagingDeep LearningRadiologyPACS Integration

An advanced medical imaging AI platform built for a radiology group with 8 imaging centres across Melbourne. The system uses deep learning models to perform preliminary analysis of X-rays, CT scans, and MRIs, automatically flagging potential abnormalities and prioritising urgent cases for radiologist review. The platform integrates with existing PACS systems and dramatically reduces reporting turnaround times while maintaining the radiologist's role as final decision-maker.

Timeline

16 months

Team Size

15 specialists

Investment

Upon request

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Challenges

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Achieving clinically acceptable sensitivity and specificity rates for abnormality detection

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Integrating with legacy PACS and RIS systems across 8 imaging centres

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Processing high-resolution DICOM images at scale without impacting existing workflows

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Meeting TGA requirements for AI-assisted medical device classification

Solutions

Trained deep learning models on 500,000+ anonymised Australian imaging studies

Built DICOM-native integration layer compatible with major PACS vendors

Deployed GPU-accelerated processing with sub-60-second analysis per study

Worked with regulatory consultants to ensure TGA Class IIa compliance pathway

Key Features

Automated preliminary analysis of X-rays, CT, and MRI studies

Abnormality flagging with confidence scores and heat maps

Urgent case prioritisation for radiologist worklist

PACS integration with major vendors (Agfa, Fujifilm, GE)

Radiologist feedback loop for continuous model improvement

Audit trail and reporting for quality assurance

Multi-modality support (X-ray, CT, MRI, Ultrasound)

Referring doctor portal with report tracking

Results & Impact

Deployed across 8 imaging centres processing 2,000+ studies daily

50% reduction in average reporting turnaround time

96% sensitivity rate for critical finding detection

35% improvement in radiologist productivity

100% of urgent cases flagged within 60 seconds of study arrival

Zero missed critical findings since deployment

Project Metrics

8 imaging centres

50% faster reporting

96% sensitivity rate

Technologies Used

frontend

Next.js 14React 18TypeScriptDICOM ViewerAnnotation Tools

backend

Python FastAPINode.jsDICOM ProcessingHL7 Integration

database

PostgreSQLMinIO Object StorageRedisStudy Archive

ai

PyTorchDeep Learning CNNTransfer LearningAbnormality Detection

deployment

AWSGPU InstancesDockerOn-Premise Hybrid

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Let's discuss how we can help you build innovative solutions that drive real business results.