This AI-driven tool offers significant value in medical imaging and brain tumor management. It provides high segmentation accuracy, reduces variability in clinician assessments, and is trained on diverse data for reliable performance across medical environments. With secure web access, it’s easy for healthcare institutions to adopt without requiring complex infrastructure, making it a crucial advancement in AI-supported diagnosis and treatment planning.
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TECHNOLOGY
Any enough advanced technology is unclear from magic
High Segmentation Accuracy
The AI-driven U-Net model achieved high accuracy, with a Dice Similarity Coefficient (DSC) of 91% and Intersection over Union (IoU) of 83%, enhancing treatment planning precision.
Reduced Observer Variability
Automated segmentation minimizes inconsistencies between clinicians, providing more consistent and reliable tumor delineations.
Robust Data Diversity
The model was trained with diverse data sources, including the BraTS dataset, improving its generalizability across different medical settings.
Web Integration
Designed as a secure web application, the tool is accessible to medical institutions, making it easy to implement without complex infrastructure.
ABOUT TALAL PROJECT
Mission is to bring force of simulated intelligence to business
We specifically focusing on the automated delineation of brain tumors. By integrating state-of-the-art deep learning techniques, we provide an innovative tool aimed at enhancing the accuracy and efficiency of brain tumor segmentation. This platform enables medical professionals to achieve precise and consistent tumor volume estimations, supporting improved patient care and treatment planning.
Our research-driven approach is committed to continuous improvement and collaboration with healthcare institutions, ensuring that our tool remains at the forefront of AI applications in radiology.
Our dedication to innovation and quality reflects our vision of transforming the future of brain cancer diagnostics and treatment.
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TEAM MEMBERS
Talal A.Althabit
Author
ibrahim R. Almohimeed
Supervisor
Mukhtar M. AlShanqity
Participant from King Fahad Medical City
Saad M.Alrashidi
Participant from King Fahad Medical CityCLIENT FAQ’S
Questions and answers for
common ML queries
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What is the tool’s accuracy in brain tumor segmentation?
The tool uses a U-Net model, achieving 91% Dice Similarity Coefficient (DSC) and 83% Intersection over Union (IoU), enhancing treatment planning accuracy.
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How does the tool help reduce observer variability among clinicians?
The tool provides automated, standardized segmentation, minimizing differences between individual clinician assessments and increasing reliability.
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Can the tool be used across multiple medical institutions?
Yes, the tool has been trained on diverse datasets, including the BraTS database, making it adaptable and effective in various medical environments.
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Is the tool accessible online?
Yes, it is designed as a secure web application, allowing medical institutions to access it without complex infrastructure requirements.