FORETELL MED

FORETELL MED 

Intelligent Quality Assurance (IQA) 
Redefining Radiotherapy Safety

Transforming radiotherapy quality assurance with AI-powered result prediction and complexity metric statistical analysis that enhances patient safety, reduces clinical workload, and minimizes treatment delivery delays.

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Radiotherapy Quality Assurance (QA) Process
Current patient-specific QA involves a physical measurement of dose using radiation detectors for every treatment plan, creating bottlenecks in clinical workflows.
Treatment Planning
Radiotherapy treatment plans are created in a treatment planning system for each patient.
Physical Measurement
Plans must be delivered to a set of radiation detectors before treatment begins.
Analysis
Measurements are compared against expected values from the treatment planning system.
Approval
Only after QA passes can the treatment plan be finalized and treatment can begin.
The Problem with QA
Delayed QA Results
Treatment machines only available for QA measurements after clinical hours, resulting in late-night results that can delay patient treatments.
Time Consuming & Labor-Intensive
Daily setup of radiation detectors and machine delivery of every single treatment plan required for QA measurements, consuming valuable clinical resources and staff time.
Measure Every Plan
Current approach treats each treatment plan as if we have no QA experience, ignoring valuable historical data that can be used to predict QA outcomes with AI.
The Solution
Complex radiotherapy plans are more likely to fail QA. Our patented solution leverages this correlation to predict outcomes without the need for physical measurement.
Extract Complexity Metrics
Our IQA platform extracts 30 classes of complexity features from the radiotherapy treatment plan
AI Prediction
IQA's AI model predicts QA measurement results based on plan complexity
Clinical Decision Support System
The IQA platform displays QA predictions and statistical information about the plan complexity, enabling clinicians to identify and focus on the most complex plans

Value of Intelligent Quality Assurance
Immediate QA Results
Real-time QA results during treatment planning eliminate delays in patient care and enable faster treatment-to-delivery time. Crucial for online adaptive radiotherapy!
Reduced QA Work
Reduction of up to 70% in resource-intensive QA measurements by not measuring non-complex plans, freeing time for complex cases.
Risk-adjustable
Customizable QA measurement thresholds based on institutional risk tolerance, ensuring alignment with customer-specific protocols.
Integrable & Scalable
Uses DICOM standard data for input, ensuring compatibility with existing radiation oncology software for seamless implementation.
Our Founders
Tim Solberg, PhD, FACR, FAAPM, FACMP, FASTRO
Chief Executive Officer 
Dr Solberg brings extensive senior radiation oncology leadership experience to the team. He is a Professor of Clinical Radiation Oncology and former Vice-Chair, Director of Physics at prestigious Radiation Oncology institutions including UCSF and UPenn. Dr Solberg also brings regulatory experience from his service as Special Advisor to the FDA Commissioner.
Gilmer Valdes, PhD DABR                 
Chief Scientific Officer 
Dr Valdes brings cutting-edge AI expertise as Vice-Chair for Machine Learning, Director of Clinical AI, and Associate Professor at Moffitt Cancer Center. Dr Valdes was also the first author on the initial academic publication which led to the patent on which IQA is based.
Alon Witztum, PhD DABR
Chief Technology Officer
Dr Witztum is Director of Patient QA, Director of Reporting & Analytics, and Associate Professor at UCSF Radiation Oncology, bringing practical implementation experience to the team. Dr Witztum was also the senior author on the academic work which developed the IQA prototype.