Intelligent X-ray analysis, deployed locally.
Detect tuberculosis from a chest radiograph in under three seconds. Engineered exclusively for offline operation in Ethiopian clinics, prioritizing absolute data sovereignty.
Pathology Detected
Confidence: 97.7%. Radiologist review required.
SOURCE / RAW
ATTENTION / GRAD-CAM
Hardware is abundant.
Expertise is scarce.
Most rural Ethiopian clinics possess functioning X-ray equipment. The critical bottleneck is interpretation. A radiograph can languish for days before a qualified radiologist reviews it.
By the time a diagnosis is formed, patients mapping to early-stage tuberculosis have already returned to their communities undiagnosed.
156,000+
Annual estimated new cases of TB in Ethiopia.
0.01
Radiologists per 100,000 people in rural sectors.
72 hrs
Average delay for expert radiograph assessment.
90%
Cure rate if intercepted during the screening phase.
Frictionless integration.
Zero retraining required. The platform operates within existing clinical procedures, acting as a silent, high-speed triaging assistant.
Image Acquisition
Radiographers utilize standard DR/CR hardware to capture the film, bypassing proprietary ecosystem locks.
Neural Inference
Local processing analyzes structural abnormalities and generates an immediate probability vector.
Expert Verification
Clinicians receive prioritized queues with visual overlays, drastically reducing time-to-diagnosis.
Engineered for edge deployment.
Cloud reliance is a systemic vulnerability in developing regions. RadView is built natively as an edge-computing solution.
Absolute Air-Gap Security
The inference engine runs entirely on the host machine. Radiographic data never traverses the internet. Compliance is guaranteed by default.
Ultra-lightweight
Distilled models optimized to run on dual-core clinical desktops without discrete GPUs. 28MB runtime footprint.
Interpretable Outputs
Grad-CAM visualizations isolate attention zones. AI ceases to be a black box.
Extensible Pipeline
Future-proof architecture ready for COVID-19 and pneumonia classifier integration.
Verified Metrics
Rigorous evaluation across standardized global clinical datasets.
Radically transparent validation.
All algorithms are trained on canonical public datasets and evaluated against held-out control sets ensuring robust generalization. Our clinical methodology is fully documented to withstand peer review and institutional scrutiny.
Deploy intelligent screening today.
Experience the core inference engine directly in your browser. Upload an open-source DICOM/JPEG and evaluate the precision instantly.