While it isn’t uncommon to hear about the involvement of ML algorithms and AI technologies in the healthcare vertical, training the associated models to perfection holds the key to incrementally improving diagnostics. This is where Medical Image Annotation has a role to play as it efficiently imparts requisite knowledge to the AI-powered Medical diagnostic setups for furthering the presence of accurate computer vision, as the underlying model development technology.
The presence of AI in healthcare has amplified over the past few years. Be it managing records without human intervention or offering digital consultation as a part of the precision treatment consortium, intelligent systems have made ailment diagnosis and health monitoring, easier than ever.
Also, the impact of healthcare AI seems to be moving beyond simple monitoring, with diagnostic automation, gene sequencing, drug development, and even treatment predictions coming off the age. But then, as much as we are aware of the benefits of Artificial Intelligence in the concerned vertical, it is the implementation that is more intriguing and requires a more unabridged discussion.
The Role of Healthcare Training and Data Driving the Same
To create intelligent AI systems that can diagnose conditions accurately, you must train the models with large volumes of relevant data. Supervised learning is, therefore, necessary to optimize and develop ML algorithms. But then, more than the quantity, it is the data quality that matters.
The first concern is always to find a high-quality dataset as data protection standards in the healthcare realm, are quite stringent, for obvious reasons. Therefore, if you are planning to develop an avant-garde, intelligent diagnostic setup, it is important to access a first-rate, uncluttered medical dataset.
Better termed as AI training data, the curated insights require cleaning before they can be fed into the systems for training. Plus, unlike any other vertical, the concept of annotation becomes all the more vital as healthcare, to a larger extent, depends on image recognition and computer vision and therefore, needs to be trained, accordingly.
In comes Medical Image Annotation!!!
Unlike education, banking, and other verticals, healthcare focuses a lot more on imageries and visual analysis. Therefore, in addition to NLP, AI models for medical diagnostics also need to be trained, keeping computer vision in mind. This is where medical image annotation comes into play as it concerns feeding the relevant AI models with marked and labeled images to help with object tracking, detection, classification, segmentation, and transcription.
Annotation and Semantic Segmentation
Medical image annotation to hone the Computer Vision skills of a setup is a tad different as compared to using it for other verticals. In healthcare, we primarily deal with organs, diseases, and internal conditions that are better approached using semantic segmentation. Here are some of the techniques that can be applied to annotate 2D and even 3D images for the healthcare domain:
- Bounding Boxes
A basic annotation technique that aligns perfectly with symmetrical images and uses boxes for 2D images and cuboids for the 3D images!’
Primarily used for intelligent systems that assist with digital healthcare consultation, Landmarking is one form of annotation that is used for helping models identify expressions, postures, gestures, and the entire context of monitoring, better.
This image annotation technique is primarily used for asymmetrical objects, which in turn is best used in Dentistry and nephrology, where elements can be uneven at times.
Apart from the mentioned techniques, semantics and segmentation are used in Brain or cerebral diagnosis and Liver diagnosis respectively, as an approach to make the models more intuitive and perceptive in time.
Benefits of Holistic Image Annotation
Scaled image annotation is definitely the future of medical diagnostics, with AI taking center stage. Here are the aspects that an annotated AI training dataset can help you reinvent:
- Detecting blood clotting, tumors, and other brain-related conditions
- Using ultrasound to detect liver-specific ailments
- Cancer cell identification
- Bounding box image annotation for detecting kidney stones
- Dental analysis
- Analysis of retinal images
- Microscopic cellular picturing
- Improved documentation
Now that it is clear that medical diagnostics need AI’s intervention to become more perceptive and proactive, the onus lies in data procurement and image annotation. While relevant and first-rate medical datasets are good enough to strengthen the foundation of these intelligent models, their role in diagnostics i.e. intelligent detection of ailments and diseases is only possible if the systems are fed with gargantuan volumes of annotated images, to facilitate supervised learning and better patient experience.