Building AI models for radiology is a complex and demanding task that requires a unique blend of technological innovation and healthcare expertise. On September 28th, iMerit’s Sina Bari, Senior Director of Medical AI, and Gökalp Urul, Director of Technology, delved into the challenges and solutions surrounding radiology AI development. In this blog post, we’ll explore some of the key insights shared during their discussion, focusing on iMerit’s Radiology Annotation Product Suite and how it’s revolutionizing radiology annotation.
The Challenges of Radiology AI
Before diving into the specifics of iMerit’s solution, it’s crucial to understand the difficulties associated with building AI models for radiology. Here are some of the primary challenges:
Data Sourcing
Radiological data, critical for training AI models, can be scarce and difficult to obtain. It often contains sensitive patient information, making it subject to strict data privacy regulations like HIPAA in the United States. Additionally, the radiology data is fragmented across various healthcare institutions in different formats and systems. This fragmentation makes it challenging to aggregate and standardize data for AI model training. Lastly, deep learning AI models require a large volume of data to train effectively, and collecting and maintaining radiology data can be expensive, involving costs related to storage, infrastructure, and personnel.
Automation
Radiology images are highly complex and vary in format (e.g., DICOM, Nifti, TIFF) and modalities. Automation Algorithms for accurately processing and annotating these diverse formats are challenging to develop. Invest in versatile annotation software that supports multiple image formats and leverages machine-learning techniques for format recognition and conversion.
Ensuring the quality and accuracy of annotations is also crucial in medical imaging, and automation may introduce errors that go unnoticed if not carefully monitored.
Domain Expertise
Radiology is a specialized field where precise interpretation of medical images is critical. Annotating radiological data requires a deep understanding of anatomy, pathology, and medical imaging techniques. Lack of domain expertise in this context can lead to incorrect or imprecise annotations that can compromise the quality of training data, resulting in unreliable AI models.
With annotations lacking clinical relevance, developing AI systems effective in real-world healthcare settings can be challenging.
iMerit’s Radiology Annotation Product Suite
iMerit’s solution tackles these challenges head-on, offering a comprehensive suite of tools and services tailored to the needs of radiology AI development:
Regulatory-Grade Data Sourcing
Users can access a wide range of imaging data, augmenting their training datasets with clinical data tailored to their use cases. It ensures the availability of high-quality data to train AI models effectively.
Radiologist-Designed Automation and Tooling
The suite is built to support various medical imaging formats, including Nifti, NRRD, DICOM, TIFF, and MP4. It automates and accelerates manual annotation tasks with AI assistance, and its customization features enhance workforce productivity.
Data Annotation with Domain Expertise
iMerit’s hybrid workflows bring together medical annotators and healthcare experts, allowing for efficient scaling of operations. This collaborative approach ensures accurate and reliable data annotation.
Benchmarking & Regulatory Model Validation
All annotations and processes are rigorously validated to meet FDA submission requirements. iMerit’s proactive monitoring of regulatory changes guarantees project success and compliance.
Conclusion
iMerit’s Radiology Annotation Product Suite is a game-changer for the development of AI and ML models in radiology. By combining automation technology with domain expertise and offering access to regulatory-grade data, iMerit empowers users to master every step of the MLOps journey while setting their projects up for success.
