NEXT-GEN CLINICAL INTELLIGENCE

Diagnostic Triage
Redefined.

An automated fracture localization system utilizing ResNet-18 architectures and Grad-CAM visualization for high-precision radiological assessment.

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> 0.85
AUROC Benchmark
< 200ms
Latency Response
< 5%
Target Error Rate
The Core Engine

Weakly Supervised
Localization Maps.

OSTEO-CORE doesn't just classify; it identifies. Using Gradient-weighted Class Activation Mapping, the system highlights structural discontinuities in bone cortex that align with physical trauma.

  • Grayscale Field Normalization
  • Laplacian Blur Detection
  • Adam-Optimizer Stability
X-Ray Analysis

Unified Logic Architecture.

Single-Executable Topology v1.0.0

Memory Mapping

Handling terabyte-scale datasets by paging disk indices directly into cache without saturating system RAM.

ResNet-18 Core

Modified residual backbone optimized for high-frequency textural disruptions in medical imaging.

One-Click Bundle

Zero-dependency package containing FastAPI backend, Streamlit UI, and AI weights in one file.

Clinical UI

Clean, distraction-free dashboard engineered for radiological reading room environments.

Technological Infrastructure.

The Python Dependency Registry

Core ML & Vision

PyTorch Neural Network & Tensors
Torchvision ResNet-18 Backbone
NumPy Memory Mapping Logic
OpenCV (cv2) Grad-CAM Color Mapping
Pillow (PIL) Grayscale Field Processing

Interface & Backend

Streamlit Clinical Dashboard UI
FastAPI High-Speed Inference API
Uvicorn ASGI Server Implementation

Environment Setup

pip install torch torchvision numpy opencv-python-headless Pillow streamlit uvicorn fastapi pyinstaller
Ready to Install

Standard environment configuration for OSTEO-CORE v1.0.0-alpha development.

Build Systems

Integrated with PyInstaller for EXE bundling and Inno Setup for professional distribution.

System Modules

Leveraging threading and sys (_MEIPASS) for robust single-process execution.