// CASE STUDY
NeuralDx
A deep learning diagnostic system for medical imaging — analyzing X-ray, MRI, and CT scans with ensemble architecture and explainable outputs.
Medical · Showcase
Overview
NeuralDx is a multi-modal medical imaging diagnostic system designed to support radiologists and clinicians in analyzing X-ray, MRI, and CT imaging. Built on a deep learning ensemble architecture, it detects anomalies, classifies findings, and generates structured clinical reports — with explainability built in from day one.
Each scan is analyzed by multiple specialized models working in parallel: one trained for anomaly detection, one for anatomical classification, and one for severity grading. Their outputs are combined into a single, calibrated diagnostic summary, with attention maps showing the visual evidence behind every finding.
The system is built for the clinical workflow: it produces standardized reports compatible with hospital information systems, surfaces uncertainty when it exists, and never replaces clinician judgment — it augments it. Explainable AI is not a feature; it's the architecture.
Results
3
Imaging Modalities
Multi-model
Ensemble Architecture
XAI
Explainable by Design
DICOM
Standard Compatible
How It Works
Scan Input
Accepts standard DICOM imaging from X-ray, MRI, and CT sources — direct from hospital PACS or uploaded manually.
Multi-Model Analysis
Parallel specialized models perform anomaly detection, anatomical classification, and severity grading.
Explainable Report
Outputs a structured diagnostic summary with attention maps showing the visual evidence for each finding.
Built With
Who This Helps
Radiologists
Accelerate review. Catch findings that warrant attention, with visual evidence for every flag.
Clinical Decision Support
Provide a second opinion in real time, with calibrated uncertainty when present.
Hospital Workflows
Plug into existing PACS systems via DICOM. Output reports compatible with electronic health records.
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