Chest X-ray Pneumonia Detection
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Medical Imaging · AI

Chest X-ray Pneumonia Detection

AI-powered triage support that helps radiology teams prioritize chest X-rays and flag potential pneumonia cases in seconds — not minutes. Achieves 98.36% accuracy on a held-out test set of 853 chest X-ray images. Available via REST API for hospitals, teleradiology providers, urgent care clinics, and health tech vendors.

98.36%Test Accuracy
0.9994AUC-ROC
100%Pneumonia Precision
97%Pneumonia Recall
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API Access

Integrate via REST API.

We provide API access to our pneumonia detection model. Upload a chest X-ray image and receive a structured response with classification (Normal / Pneumonia) and confidence scores. Designed for integration with PACS, RIS, and custom workflows.

Sample response:

{
  "class": "PNEUMONIA",
  "confidence": 0.996,
  "processing_time_ms": 45
}
  • Structured JSON responses with class and confidence
  • Supports standard image formats (JPEG, PNG, DICOM)
  • On-premises or cloud deployment options
  • Rate limits and authentication included
  • Sub-second inference for real-time workflows

Validated Performance

Trained on 8,530 chest X-ray images with an 80/10/10 stratified split. Metrics below are on a held-out test set of 853 images. Typical inference time under 100ms per image. Supports both AP and lateral views.

98.36%Test Accuracy
0.9994AUC-ROC
100%Pneumonia Precision
97%Pneumonia Recall

100% specificity on normal cases (zero false positives). Grad-CAM attention maps available to show which regions the model used for each prediction.

Model performance — confusion matrix, ROC curve, and Grad-CAM attention maps
Commercial Use Cases

Built for real workflows.

Hospital radiology departments

Triage incoming chest X-rays before radiologist review. Flag high-priority studies, reduce reporting backlog, and ensure suspected pneumonia cases move to the front of the queue.

Teleradiology providers

Scale read capacity without adding headcount. Prioritize studies across multiple facilities. Support after-hours and overflow volumes with consistent triage logic.

Urgent care & walk-in clinics

Quick screening for patients presenting with respiratory symptoms. Support clinicians with a second-opinion signal before ordering confirmatory tests or referrals.

Community health screening

Mobile or pop-up screening programs in underserved areas. Batch-process X-rays for outreach initiatives, tuberculosis screening, or pneumonia surveillance programs.

PACS & RIS vendors

Embed the API into your imaging platform. Offer pneumonia triage as a value-add module for hospital and clinic customers. White-label integration available.

ED & emergency triage

Accelerate emergency department workflow. Identify potential pneumonia cases within seconds of image acquisition. Support rapid disposition decisions.

Why it matters

Speed and clarity when seconds count.

Rapid triage

Results in seconds. Help radiologists prioritize high-priority studies and reduce time-to-report for suspected pneumonia cases.

Interpretability

Grad-CAM attention maps show which regions of the X-ray the model used for each prediction — building trust and supporting clinician review.

Built for healthcare

Engineered with HIPAA-aligned architecture in mind. Data handling and deployment options designed for clinical environments.

Compliance & Intended Use

Clarity and transparency first.

Not a medical device

This system is intended for triage support and workflow assistance only. It does not replace clinical diagnosis. All interpretations must be verified by a qualified healthcare professional.

Deployment readiness

We offer deployment options designed for healthcare environments, including on-premises and private-cloud configurations. Data can be processed within your infrastructure to support HIPAA and other privacy requirements.

Transparency

We provide model performance metrics, limitations, and validation methodology as part of access discussions. We believe in clear expectations before any deployment.

Technical Summary

  • Trained on 8,530 chest X-rays with 80/10/10 stratified split
  • Binary classification: Normal vs. Pneumonia with confidence scores
  • 98.36% test accuracy on 853 held-out images; AUC-ROC 0.9994
  • Grad-CAM attention maps for interpretable, auditable predictions
  • API-first design for PACS, RIS, and custom integration
Get started

Interested in access?

Get API access or discuss deployment options. We work with radiology departments, teleradiology providers, urgent care clinics, PACS vendors, and health systems. Technical specs, SLA options, and performance reports available upon request.

Request API Access