Why Choose FastVision.ai?
Choose FastVision.ai for advanced lung nodule detection, improved diagnostic accuracy, and enhanced radiologist efficiency. Experience the power of our technology and take your practice to new heights.
Precision & Accuracy
Develop a robust and accurate lung nodule detection system that aids radiologists in identifying potential abnormalities
Speed & Efficiency
Improve the efficiency of radiologists' workflow by automating time-consuming tasks and providing reliable assistance in the diagnostic process
Quality & Robustness
Enable more widespread use of this AI technology across hospitals and clinics
Enhancing Radiology with AI
We aim to revolutionize the field of radiology by harnessing the power of Deep Learning to improve nodule detection and malignancy classification. Our team of data scientists from UC Berkeley is committed to delivering accurate and efficient solutions for critical diagnostics.
Advanced Nodule Detection
Our AI-powered system uses state-of-the-art algorithms and deep learning techniques to detect nodules in lung CT scans with exceptional accuracy, assisting radiologists in identifying potential abnormalities that might have been overlooked manually.
Malignancy Classification
Beyond nodule detection, our AI model goes a step further to classify nodules based on their malignancy, providing radiologists with critical insights to aid in diagnosis and treatment planning.
Seamless Integration
We understand the importance of a seamless workflow in radiology practice. Our AI system can seamlessly integrate with existing radiology software, ensuring a smooth incorporation into the regular diagnostic workflow, ultimately saving valuable time for radiologists.
Scanned Lung CTs
Participating Radiologists
Collaborating Hospitals
Patients' Data Security Managed
Methodology
Our approach to detecting lung nodules and malignancy classification involves a multi-step process, combining cutting-edge AI techniques with radiological expertise. Here's an overview of our methodology:
Data Source
We utilized the National Cancer Imaging Archive, specifically the LIDC-IDRI dataset (Lung Imaging Database Consortium and Image Database Resource Initiative) as our primary data source. The dataset includes a diverse collection of lung CT scans, encompassing both positive and negative cases of nodules, along with associated clinical information
Preprocessing
The collected data underwent rigorous preprocessing, including image normalization, noise reduction, and 3D volumetric transformation for efficient training
Model Development
We designed a U-Net segmentation model to extract lung tissue from a CT scan dicom image, followed by a 3D CNN classification model to accurately locate lung nodules.
Model Training
Both segmentation and classification models were trained on high-performance GPUs on AWS platform, using annotated data to optimize their performance
Model Evaluation
Our model performance had robust evaluation on held out test set, analyzing metrics like precision, recall and F-1 score. The results demonstrate the reliability and effectiveness of our solution in detecting lung nodules
Performance Optimization
Continuous improvement and optimization of the models based on feedback from radiologists and subject matter experts
Our Team
We are a team of data scientists at UC Berkeley, passionate about harnessing the power of data to drive innovation and make a positive impact on the world.
Uzair Mansuri
Chief Engineer, Raytheon Technologies
Radia Wahab
Assoc. Director, Guardant Health
Rohan Mendiratta
ML Engineer | Student @ UC Berkeley
Victor Inyang
Process Engineer, Intel
Kolby Devery
Process Engineer, Intel
Neil Bhatia
Founder & CEO, LobbyingData.comFrequently Asked Questions
Find answers to commonly asked questions about our AI powered lung nodule detection and malignancy classification system
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What data sources were used for training the AI models?
We utilized the National Cancer Imaging Archive, specifically the LIDC-IDRI dataset (Lung Imaging Database Consortium and Image Database Resource Initiative) as our primary data source. The dataset includes a diverse collection of lung CT scans, encompassing both positive and negative cases of nodules, along with associated clinical information.
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What Deep Learning models were used to develop FastVision.ai?
We developed two models: a U-Net model for segmentation, which extracts the boundaries of lung tissue in CT scans, and a 3D CNN model for classification, which identifies the location of nodules in the lung CT scan.
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How is data security and patient privacy ensured by FastVision.ai?
Data security and patient privacy are of utmost importance to us. We have implemented robust security measures to protect sensitive medical data. Here's how we ensure it:
- We utilize a secured cloud server to store and process the medical data. The cloud infrastructure is encrypted and protected with advanced access controls.
- Our system adheres to the Health Insurance Portability and Accountability Act (HIPAA) compliance standards, which set strict guidelines for safeguarding patient health information.
- We follow industry best practices in data encryption, secure access protocols, and regular security audits to identify and mitigate potential vulnerabilities.
- All data access and processing are strictly limited to authorized personnel with appropriate credentials and clearance levels.
- Regular staff training on data security protocols ensures that our team is vigilant in maintaining the highest standards of privacy and security.
Rest assured that we prioritize the confidentiality and privacy of patient information, ensuring compliance with all relevant regulations and standards.
Dr. John Doe
Clinical Radilogist, Cleveland Clinic
As a radiologist, I'm impressed with FastVision.ai team's efforts. Their U-Net segmentation model has been particularly interesting in extracting lung volume out of CT scan image.
Dr. Mike Smith
General Radiology, UIMCC Dept. of Radiology
FastVision.ai's AI-powered nodule detection and classification models have a lot promise in this arena. Current downtime from a CT scan to actual diagnosis is a time consuming process. This has the potential to revolutionize cancer care.
Dr. Jena Karlis
Senior Radiologist, Mayo Clinic
If current process flow is shortened from CT scan to radiologist review, we can focus more on patient care and treatment planning which is vital in cancer care where time of essence. I wish FastVision.ai team continued success in their work.
Prof. John Larson
Professor of Data Science, UC Berkeley
FastVision.ai's AI-based lung nodule detection system is an exceptional example of the potential of data science in the medical field. As a data science professor, I am impressed by the sophisticated techniques used to develop this cutting-edge solution. I am excited to see how this technology will enhance the capabilities of radiologists and improve patient outcomes.
Project Gallery
Take a glimpse into our journey of medical imaging and AI research. Browse through some captivating images and moments that showcase our team's dedication and passion in advancing healthcare through technology.
Contact
If you have any inquiries regarding our product or would like to get in touch with us to know more, please feel free to reach out. We are here to help!
Location:
102 South Hall Rd, Berkeley, CA 94720
Email:
info@fastvision.ai
Call:
+1 5589 55488 55s