Medical Image Analysis for Early Disease Detection

The Challenge

A leading private hospital group in Dubai Healthcare City was experiencing a 60% increase in radiology cases due to medical tourism and population growth. With only 12 radiologists handling 800+ scans daily, diagnosis delays averaged 48 hours for non-emergency cases. The hospital needed to maintain Dubai Health Authority's quality standards while managing the growing workload efficiently.

Our Solution

Our deep learning solution analyzes chest X-rays for pneumonia, tuberculosis, and lung nodules; CT scans for brain hemorrhages and tumors; and MRIs for musculoskeletal abnormalities. The system was trained on 2.8M anonymized medical images from international datasets, then fine-tuned with 180,000 cases from regional populations to account for genetic and environmental factors specific to the Middle East region. All processing maintains HIPAA and DHA compliance standards.

Implementation Steps

• Deployed DenseNet-121 and ResNet-152 models trained on 2.8M medical images
• Integrated seamlessly with existing Philips PACS and Siemens RIS systems
• Implemented DICOM-compliant processing pipeline with end-to-end encryption
• Created radiologist workstation with AI annotations and confidence scoring
• Established automated case prioritization based on urgency scores
• Built continuous learning system with radiologist feedback integration

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