AI in medical imaging is redefining how doctors diagnose and treat diseases, allowing them to look inside the human body non-invasively. AI algorithms can analyze and interpret medical images, providing accurate and timely diagnoses. And to create highly-effective AI algorithms for medical imaging, high-quality annotated training data is essential. They also need a consistent supply of high-quality data throughout the development process, beginning with the training data in the early stages and to full-scale deployment.
However, creating annotated training data for medical imaging is a challenging task requiring high expertise and accuracy. Any errors or biases in the annotated data can lead to incorrect diagnoses, potentially risking patient health. This blog will look at key factors to consider to achieve high-quality structured data for the medical domain.
How to Achieve High-Quality Annotated Data for Medical Imaging
Setting Clear Guidelines
Data annotation guidelines must be based on best practices and need to be specific to the type of medical annotation. Clear and concise guidelines can ensure consistency among annotators, reducing inter-annotator variability and improving the accuracy of the annotations.
Specialized Annotators
Specialized or trained annotators with relevant medical training or experience can help ensure the accuracy and consistency of the annotated data. Some medical data annotations need an understanding of human anatomy, medical terminology, and disease pathologies for accurately identifying and labeling structures. For example, we recently did a project on COVID-19 pneumonia, and we trained our team on disease nuances and terminologies since the disease was relatively new for the team.
Medical Domain Experts
It is essential to have medical subject matter experts and industry experts who can continuously guide the annotation team, monitor, and evaluate the annotation quality, and take necessary actions to improve it to produce high-quality medical data. You must have at least a few radiologists and specialist doctors on medical data annotation projects to guide annotators better and improve results.
Tool Inclusive Annotation
The tool used for image annotation should be user-friendly, scalable, and efficient to handle specific types of medical data annotations. Additionally, the annotation tool you use should provide features such as zoom and pan, region selection, and shape drawing tools to help annotators precisely identify and label structures on medical images.
Data annotation partners with a tool-agnostic approach are the best bet because they can work on any client tools, offer their in-house solutions, or provide many other tool options.
Best-Suited Workflow
The right workflow type depends on the specific needs of the project. Hybrid workflows, which combine manual and automated annotation, can help reduce the time and cost of annotating large datasets. Multi-tiered workflows involving multiple rounds of annotation and verification can improve the accuracy and consistency of the annotated data and reduce errors and inter-annotator variability.
iMerit’s Winning Medical Annotation Process
iMerit is ranked #1 in healthcare data collection and labeling by i360 Research – Sep 2022. Let us now deep dive into the process we follow to deliver quality, secure, HIPAA-compliant data solutions to leading pharmaceutical companies, device manufacturers, health plans, and provider networks.
Expert Consultation
The physician solutions team at iMerit works with clients to recommend a tailored solution, whether choosing the tool or the workflow for the project. We believe that past industry experience grants credibility. Our subject matter experts in the medical domain have prior experience within the industry and utilize the knowledge to guide the client and manage the projects efficiently.
Guideline Development
As mentioned above, a clear and well-defined set of guidelines is essential to ensure consistency and accuracy in data annotation projects. Our solution architects bring their expertise in data annotation and work closely with clients to understand their unique requirements, goals, and challenges. They also understand industry standards and societal guidelines related to medical data annotation. Based on this knowledge and their experience, we help clients develop guidelines custom to their projects that adhere to the highest accuracy, consistency, and quality standards.
Secure Data Pipelines
iMerit is HIPAA, SOC 2 Type II, and GDPR compliant, ISO 27001:2013 certified, and has been audited based on AICPA guidelines. We have over 5,500 full-time employees across the US, India, and Bhutan working on various data annotation projects. For better security, we operate dedicated and monitored facilities and delivery centers with stringent protocols to protect data sensitivities.
Customized Workflows
Our protocols, tools, and workflows are customized based on customer needs. We recognize that the data training process is iterative, ever-evolving, and needs to be agile and flexible. A solution consisting of disaggregated stages allows for flexible solutions for clients.
Assisted Annotation
iMerit has in-house tools for annotation, workflow management, and quality control. We adopt a mix-and-match approach with our proprietary tools, other partner tools, and client tools. Our solution architects put together the optimal workflow for each project in consultation with the technology, delivery, and learning & development teams.
Quality Control
Our closed feedback cycle is built-in with real-time monitoring and service delivery insights. The full-time expert model by iMerit allows for traceback and repeatability down to a granular level. We also employ an evaluation model that continually assesses deliverables, key metrics, quality control processes, and business outcomes. Our workforce operations are actively managed across skilling, rostering, and delivery using our tasking platform – the iMerit People Platform (IMPP). It allows for skill mapping, skill development, workforce allocation, tracking, and performance monitoring.
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Conclusion
Medical AI has immense potential to grow while improving efficiency, reducing costs, and increasing speed for better patient care. However, this is only true if these AI solutions get high-quality training datasets built using a combination of domain experts, trained specialists, and other best practices.
iMerit is an industry leader in labeling, annotation, segmentation, transcription, and analysis of diverse data sets – images, text, video, audio, LIDAR, and more. We have extensive experience in providing annotation services across 20 million data points for the healthcare sector.
