PulseAI was engineered to solve high diagnostic latency in hospital triage environments. By processing chest radiographs through deep convolutional neural networks, PulseAI highlights subtle pulmonary lesions and consolidations for emergency radiologists within 1.2 seconds.
Core Architecture & Engineering
- Inference Pipeline: Built with PyTorch DenseNet-121 pre-trained on the NIH ChestX-ray14 dataset, fine-tuned with localized clinical annotations.
- Microservices Layer: High-speed asynchronous inference server built using FastAPI and Docker, orchestrating GPU batching.
- Reactive Clinician UI: Real-time canvas heatmaps (Grad-CAM) overlaid directly on DICOM medical imaging.
- Security & Compliance: End-to-end encrypted TLS 1.3 transmission with strict zero-retention HIPAA compliant anonymization.
Measurable Outcomes
- 96.4% sensitivity across 14 distinct pulmonary pathology categories.
- 65% reduction in emergency triage waiting times for urgent diagnostic escalations.
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Machine Learning
