4 Ways to Deliver Cleaner Colonoscopy Datasets
Colonoscopies are exams that can help healthcare providers spot potentially life-threatening issues within the intestines. These exams have been around for decades and are largely effective. But the human element still provides varying results for patients.
Heterogeneous performance during endoscopy can lead to differing opinions and inaccurate diagnoses. Fortunately, things are changing, and there are many ways to deliver cleaner colonoscopy datasets.
Computer Vision
Computer vision is a form of AI that's dramatically changing healthcare. Instead of relying solely on a healthcare provider's knowledge and keen eye, operators can use this technology to spot issues. It works similarly to the human eye. Machines can "view" colonoscopy imaging and learn how to identify artifacts that matter.
AI models for gastroenterology computer vision have the potential to substantially improve diagnostic accuracy, helping providers make better decisions for their patients.
AI-Assisted Annotation
Of course, computer vision needs detailed training before operators can deploy it. That's where annotation comes in.
Accurate annotation involves creating the context for the images computer vision sees. Before deployment, computer vision needs to process thousands of datasets. The more information it learns, the better it performs.
Traditionally, manual annotation was the only way to get clear colonoscopy datasets. But AI-assisted annotation changes that. High-quality training platforms can label colonoscopy videos of any length, annotating content in many ways. Because AI is at the helm, operators can focus less time on menial tasks and focus on other ways to improve training efficiency.
Infinite Oncologies
Another way to improve AI models for gastroenterology computer vision is to create as many gastroenterology models as possible. Colon issues are complex, and there are many potential issues to look for during endoscopy. Training computer vision to spot only polyps can result in missed problems.
Multiple classifications and sub-classifications can help operators build a highly accurate gastroenterology model capable of identifying several issues.
AI and Human Review
Finally, it's important to review and correct datasets. Accuracy is paramount, as a computer vision model is only as accurate as its training models.
AI can help improve accuracy by spotting areas of interest and tagging artifacts. Operators can then go in to review datasets, make necessary corrections and feed machine learning models highly accurate datasets for faster deployment.
Read a similar article about computer vision models here at this page.
