Industry: Healthcare & Medical Imaging
Challenge: It is estimated that around 25% of radiologists may miss early signs of breast cancer during image interpretation, primarily due to the high volume of cases and subtle signs of early-stage cancer that are difficult to detect. Our client, a leading medical diagnostics provider, wanted to integrate AI-driven solutions to assist radiologists in detecting breast cancer more accurately and even predict future cancer risk based on patient imaging. The goal was to reduce human error, improve early diagnosis, and provide better outcomes for patients.
Solution: We developed an advanced AI-based diagnostic system that uses deep learning models to analyze DICOM images and assist radiologists in detecting and predicting the onset of breast cancer. Here’s how we achieved it:
- AI Model Integration:
- We integrated multiple AI models, including convolutional neural networks (CNNs) and predictive algorithms, trained specifically on breast cancer datasets.
- The system is capable of analyzing DICOM images in real-time to identify suspicious areas in the breast that could potentially indicate malignant growths or pre-cancerous conditions.
- Predictive Analysis:
- In addition to detecting current abnormalities, the system uses historical imaging data to predict future cancer risk.
- This predictive feature estimates the probability of cancer developing in a specific region, helping clinicians focus on high-risk areas in subsequent screenings.
- Annotation and Probability Mapping:
- The AI system automatically annotates suspected regions on the DICOM images, highlighting areas where cancer is likely to be present.
- The system also provides a probability score for each annotated area, indicating the likelihood of malignancy. This helps radiologists prioritize follow-up actions and additional tests based on the severity of the risk.
- Medical Terminology and Reporting:
- The AI models are trained to recognize standard medical terminologies like BI-RADS (Breast Imaging-Reporting and Data System) scores, enabling them to categorize findings in a familiar clinical context.
- Radiologists receive detailed reports with clear annotations, including lesion size, location, and density, along with the probability of malignancy for each identified area.
- Clinical Workflow Integration:
- The AI system was seamlessly integrated into the client’s existing PACS (Picture Archiving and Communication System), allowing radiologists to review AI-suggested annotations alongside traditional mammogram and ultrasound reports.
- The interface was designed to be intuitive, giving radiologists the ability to toggle AI suggestions on or off during their review process.
- Continuous Learning:
- The AI models are designed to continuously improve through deep learning. Each new scan analyzed helps refine the system’s accuracy, particularly in detecting subtle signs of early cancer.
- Data feedback loops from real-world diagnostic outcomes are used to update the model regularly, ensuring it stays accurate and relevant.
Results:
- 30% Increase in Detection Accuracy: The AI-assisted system improved radiologists’ accuracy in detecting breast cancer, especially for early-stage cancers and dense breast tissues.
- Earlier Predictions: The system’s predictive capability identified potential cancer risk up to 5 years before clinical symptoms, allowing for earlier interventions.
- Reduction in Missed Diagnoses: By flagging suspicious areas with high probability scores, the system significantly reduced the number of missed diagnoses in routine screenings.
- Improved Workflow Efficiency: Radiologists could now focus on high-risk areas faster, reducing the time needed for review and increasing the overall throughput of cancer screenings.
Technologies Used:
- Backend: Node.js, Express
- Frontend: AngularJS, Cornerstone.js (for DICOM image rendering)
- AI Models: TensorFlow, Keras, CNN (Convolutional Neural Networks), Transfer Learning
- Data: DICOM imaging data, historical patient records
- Cloud Services: AWS EC2 for scalable model deployment, S3 for storing DICOM images
- Integration: PACS (Picture Archiving and Communication System)
