Achieving 96% diagnostic sensitivity and 60% faster image analysis through AI-powered radiology assistance

XCodeIT partnered with RadiologIA to develop a cutting-edge AI-powered medical imaging analysis system that assists radiologists in detecting abnormalities in CT scans and X-rays. The solution leverages deep learning models trained on hundreds of thousands of annotated medical images, has been validated in three major hospitals, and achieved CE Class IIa medical device certification for use in clinical settings.

Client: BioResearch

https://BioResearch.xcodeit.io/dashboard

RadiologIA Analysis

CE Class IIa

Chest X-Ray Analysis

Anomaly 87%

Sensitivity

96%

Analysis Time

-60%

AI Findings

Potential nodule detected 87%

No pleural effusion 99%

Heart size normal 95%

96%

Sensitivity Rate

60%

Faster Analysis

Hospital Validations

CE Class IIa

Medical Certification

The Challenge

RadiologIA, a radiology services provider serving multiple hospital networks, faced a growing crisis: radiologist shortage and increasing imaging volumes were creating dangerous backlogs. Patients waited days or weeks for scan results, delaying critical treatment decisions. Radiologists worked under immense pressure, reading hundreds of images daily, increasing the risk of diagnostic errors and burnout. The challenge was particularly acute for time-sensitive conditions like lung nodules, fractures, and cardiovascular anomalies where early detection dramatically improves outcomes. RadiologIA needed an AI solution that could pre-screen images, flag potential abnormalities, and prioritize urgent cases - but it had to meet the highest standards of medical accuracy and regulatory compliance. False positives would overwhelm radiologists; false negatives could be life-threatening. The solution needed to integrate seamlessly with existing PACS systems, handle diverse imaging modalities, provide explainable results that clinicians could trust, and navigate the complex path to medical device certification in Europe.

Our Solution

XCodeIT developed a comprehensive AI-powered medical imaging platform combining state-of-the-art deep learning with clinical workflow integration. Our approach delivered both technical excellence and regulatory compliance:

Deep convolutional neural networks built with PyTorch, trained on 200,000+ annotated medical images with extensive data augmentation and validation protocols

Multi-modal imaging support handling DICOM formats across CT, X-ray, and MRI with automated preprocessing and quality control pipelines

Explainable AI visualization providing attention maps and region highlighting to show radiologists exactly where the model identified potential abnormalities

Intelligent triage system automatically prioritizing urgent findings and integrating with hospital workflow management systems to expedite critical cases

FastAPI-based inference engine delivering sub-second prediction times while maintaining HIPAA-compliant data handling and audit trails

Clinical validation framework enabling systematic testing across diverse patient populations and imaging protocols in partnership with three hospital sites

Technologies Used

Python PyTorch DICOM FastAPI React GCP Healthcare API

The Results

96%

Sensitivity Rate

Achieved 96% sensitivity in detecting target pathologies, matching or exceeding radiologist performance in validation studies

60%

Faster Analysis

Reduced average time-to-diagnosis by 60% through intelligent pre-screening and prioritization

Hospital Validations

Successfully validated in three major hospitals with diverse patient populations and imaging equipment

CE Class IIa

Medical Certification

Achieved CE marking as Class IIa medical device, enabling clinical deployment across European markets

"What XCodeIT accomplished goes far beyond building an AI model. They built a complete clinical solution that radiologists actually trust and use. The technical achievement - 96% sensitivity with low false positives - is remarkable, but equally impressive was their understanding of the regulatory pathway and clinical validation requirements. They worked hand-in-hand with our radiologists throughout development, incorporated clinical feedback iteratively, and guided us through the CE marking process. This AI system is now an essential part of our diagnostic workflow, helping us deliver faster, more accurate diagnoses while reducing radiologist burnout. It's genuinely saving lives."

Dr. Ana Rodrigues

Project Details

Industry: Medical Technology
Services: AI/ML Development, Medical Device Software, Clinical Integration, Regulatory Compliance
Duration: 12 months