Healthcare

MedScan AI: Automated CT Scan Anomaly Detection

MedScan AI is a zero-latency diagnostic assistant built for clinical environments to review medical images and pre-flag critical anomalies with high accuracy.

Problem Statement

The core challenge

Radiologists at clinical scale face a heavy volume of diagnostic scans, leading to fatigue and potential diagnostic delays for critical, life-threatening anomalies in CT scans.

Key Requirements

  • Automated DICOM image parsing from hospital PACS servers.
  • Sub-200ms inference latency for real-time radiologist assistance.
  • High-sensitivity anomaly detection for micro-structural abnormalities.

Architectural Breakdown

  • Client Gateway: PACS network integration module for DICOM image streams.
  • Inference Core: Containerized PyTorch 3D CNN model hosted on AWS SageMaker.
  • API Microservices: FastAPI async handlers managing scan queues.

Implementation Strategy

We designed and deployed a customized 3D CNN deep learning vision architecture trained on anonymized medical imaging datasets. The model processes incoming DICOM scans via SageMaker endpoints and returns anomaly heatmaps to PACS workstations.

Technical Decisions

  • Selected 3D CNN PyTorch architecture for volumetric scan analysis.
  • Packaged endpoints with Docker microservices on AWS SageMaker for sub-200ms response times.

Key Challenges Solved

  • Achieving sub-200ms inference on multi-frame volumetric scans was solved by optimizing model quantization and GPU memory allocation.

Quality Assurance & Testing

Validated against verified clinical benchmark datasets, measuring sensitivity, specificity, and inference latency under concurrent hospital network loads.

Project Outcome

Achieved 99.4% diagnostic accuracy and 150ms inference latency, reducing diagnostic scan review times by 65% across clinical pilot teams.

Technology Stack

PyTorchPython (FastAPI)AWS SageMakerDockerDICOM / PACS

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