The adoption of technologies like artificial intelligence and machine learning in medical data is increasing, with widespread applications in digital radiology. AI has proven to be valuable for radiologists, helping them with high-resolution imaging in X-rays, CAT scans, MRIs, and other relevant tests. Radiologists can now quickly interpret X-rays, CT scans, and other images for diagnoses using neural networks and computer vision models.
However, an AI/ML model is only as good as the dataset used for training. Any bias, discrepancy, or inaccuracies in the training datasets accentuate when fed into an AI/ML model. Some medical companies use data labeling tools to annotate and label images to build training datasets for their models.
But the reality is data labeling tools do not appropriately support medical data imaging and its formats, making it essential to use a hybrid approach of creating training datasets with tools and human-in-the-loop.
In this blog, we will discuss how data annotation advances AI in digital radiology and review four use cases of data annotation in medical imaging. We will also review how hybrid annotation, an approach where doctors work hand-in-hand with teams of specialized annotators, helps achieve the best results at scale.
Role of Medical Image Annotation in Digital Radiology
AI-based medical imaging requires a large volume of accurately labeled data to perform. Medical image annotation, therefore, demands expert knowledge and domain expertise.
- Expertise & Experience: Medical data annotation involves labeling medical images, including X-rays, CT scans, MRIs, ultrasounds, and PET scans, to train machine learning models. Medical annotation helps ML models learn from earlier cases and provide predictions about new and unlabelled images to diagnose various diseases, including cancers, infections, or other abnormalities. Not every annotator can label medical images and needs skillsets and experience to create high-quality training datasets for medical AI.
- Compliance: One of the primary reasons for its complexity is the need for medical professionals to ensure robust annotation and labeling and compliance with the HIPAA (Health Insurance Portability and Accountability Act) regulations while working with sensitive patient data.
- Data Accessibility: Access to medical image data is often limited due to privacy concerns, making obtaining large datasets for training and validation difficult. Additionally, the collected data requires anonymization to protect patient privacy, further complicating the process.
These factors combine to make medical image annotation a challenging and time-consuming process. At iMerit, our dedicated data labeling team for the medical domain works with medical subject matter experts and Solution Architects to navigate this complex landscape.
Have you explored our cutting-edge Radiology Annotation Suite? Click here to discover its powerful capabilities.
Data Annotation Use Cases in Digital Radiology
iMerit has worked with various companies building radiology AI products and solutions, providing data annotation for different use cases including:
COVID Diagnosis: During the pandemic, there was a rising need for accurate and efficient diagnosis of the coronavirus disease. Our medical data labeling specialists performed 3D pathology classification, and segmentation on several non-contrast chest CT scans to identify if they indicated COVID-19 pneumonia – a Covid-19 complication causing inflammation and fluid in the lungs. An AI algorithm can assess X-rays and other images for evidence of opacities that show pneumonia and alert providers to potential diagnoses.
Neuropathology Triage: AI can help in the early detection and diagnosis of neurological disorders. It can also help in triaging cases based on the severity of the condition, allowing medical professionals to prioritize treatment and care. To support the AI-based detection ofmalignant brain tumors by analyzing contrast head CT scans, our expert annotators have worked with several healthcare companies on classification and tool-assisted segmentation
- Breast Can
cer Diagnosis: In the case of breast cancer, microcalcification in tissue can often be challenging to identify as either malignant or benign decisively. Moreover, false positives could lead to unnecessary invasive testing ortreatment, and missed malignancies could result in delayed diagnoses and life-threatening outcomes. Our data experts have worked on breast ultrasound analysis, classification, and segmentation using Smart Scissors. A 3-step, hybrid, and dual-shore workflow is used for such complex projects.
Cardiac Ultrasound: Clinical professionals can learn about an individual’s risk for cardiovascular abnormalities by studying the heart structure, arteries, blood flow, size, and shape of the ventricles. We worked with a leading medical device manufacturer to create an AI-powered ultrasound device for use in the field. This company needed the annotation of 23,000 cardiac ultrasound videos. Using keypoint and polyline annotation, our data specialists and medical experts performed large-scale echocardiography analysis, measurement, and segmentation across multiple views. View case study
We use a combination of technology, talent, and techniques to ensure high accuracy and efficiency. Our expert annotators have experience working with various medical images and can provide accurate and reliable annotations. Our data experts also have experience in MSK imaging, mammography, maxillofacial CT, fetal ultrasound, x-ray, angiography, and transcranial ultrasound.
The level of stringency for data security requirements usually increases with the size of your project. It is recommended to begin with a partner with experience and resources to adhere to the various data formats, regulatory requirements, and user experience aspects necessary for a scalable medical AI project.
