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2023MediCore Labs

Radiology Assistant AI

PythonTensorFlowDICOMFastAPIDocker

SYSTEM VISUALIZATION

99.8%

Sensitivity

3s

Processing Time

60%

Workload Reduction

Pending

FDA Approval

The Challenge

Radiologists were overwhelmed by the volume of MRI scans, leading to fatigue and potential diagnostic errors. MediCore needed an assistant to triage scans and highlight potential anomalies with high sensitivity.

The Solution

We trained a custom Computer Vision pipeline using a dataset of 500,000 annotated scans. The model uses a U-Net architecture for segmentation and an attention mechanism to highlight regions of interest, serving as a 'second pair of eyes' for doctors.

Key Deliverables

  • Architecture Blueprint
  • Production Model Weights
  • API Documentation
  • Dashboard Interface

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