This post was originally published by Vikram Singh Bisen at Medium [AI]
AI in healthcare is becoming more prevalent with more effective computer vision-based machine learning model developments.
The more training data is used with the machine learning algorithm the AI model will learn with more variations making it easier to predict the results with more accuracy in various scenarios for the healthcare sector.
And to make the training data useful and productive, the annotated medical images are used to make the disease or body aliments detectable through machines. Medical image annotation is the process used to create such data with an acceptable level of accuracy.
Medical image annotation is the process of labeling the medical imaging data like Ultrasound, MRI, and CT Scan, etc. for machine learning training.
Apart from these radiologist images, other medical records available in the text formats are also annotated to make it understandable to machines through deep learning algorithms for accurate predictions.
Medical image annotation is playing an important role in the healthcare sector, so right here we will discuss the importance and role of the medical image annotation. And what is the types of medical images can be annotated to create the training data sets for the different disease.
Medical image annotation is playing a big role in detecting the various types of diseases through AI-enabled devices, machines and computer systems.
Actually, this process provides the real information (data) to the learning algorithms, so that model becomes user to detect such diseases when similar medical images put in front of the system.
From normal bone fracture to deadly disease like cancer, medical image annotation can detect the maladies at microscopic level with accurate predictions.
Hence, you can find here the types of diseases or diagnosis performed by AI in medical imaging diagnostics, trained through set of data generated through medical image annotation.
Medical image annotation is used to diagnosis the disease including brain tumors, blood clotting, or other neurological disorders. Using the CT Scan or MRI, machine learning models can detect such diseases if well-trained with precisely annotated images.
AI in neuroimaging can be possible when brain injuries and other ailments are properly annotated and feed into the machine learning algorithm for the right prediction.
Once the model, get fully trained to it can be used on the place of radiologist making with the better and more efficient medical imaging diagnosis process saving the time and efforts of the radiologist in taking other decision.
Liver related problems and complications diagnosed by the medical professionals using the ultrasound images or other medical imaging formats.
Usually, physicians detect, characterize, and monitor diseases by assessing liver medical images visually. And in some cases, he can be biased due to his personal experiences and inaccuracy.
While medical image annotation can train the AI model to perform the quantitative assessment by recognizing imaging information automatically instead of such qualitative reasoning as more accurate and reproductive imaging diagnosis.
Similarly, Kidney related problems like infection, stone, and other ailment affecting the functioning of the kidney.
Though AI applications in kidney disease is currently no significant but right now it is mainly focused on various key aspects like Alerting systems, Diagnostic assistance, Guiding treatment, and Evaluating prognosis.
And when the algorithms get the right annotated data sets of such images, the model comes capable enough to even diagnosis the possibilities if kidney failure.
Apart from bounding box annotation, there are various other popular medical image annotation techniques used to annotate the images making AI possible in detecting the kidney related to various problems.
Detecting cancers through AI-enabled machines is playing a big role in saving people from such life-threatening diseases. When cancer is not detected at the initial stage, it becomes incurable or takes extraordinary time to cure or recover from such illnesses.
Breast cancer and prostate cancer are the most common types of cancers found in women and men respectively, globally with high death rates among both genders.
But now AI models trained with medical image annotation can help machine learning models to learn from such data and predict with the condition of maladies due to cancer.
Teeth or gums related problems can be better diagnosed with AI-enabled devices. Apart from teeth structure, AI in dentistry can easily detect various types of oral problems.
Yes, a high-quality training data set, can help the ML algorithm recognize the patterns and store in its virtual memory to use the same patterns in the real-life.
Medical image annotation can provide high-quality training data to make the AI in Dentistry possible with quantitative and qualitative data used to train the model and accuracy will improve in machine learning for dental image analysis.
Eyes scanned through retinal images can be used to detect various problems like ocular diseases, cataracts, and other complications.
All such symptoms visible in the eyes can be annotated with the right techniques to diagnosis the possible disease.
It is impossible to see the microscopic cells with normal human eyes, buy using the microscope it can be easily seen.
And make such extremely small size cells recognizable to machines, the high-quality image annotation technique is required for right model development.
The images of these microscopic cells are enlarged on the bigger computer screen and annotated with advanced tools and techniques.
And while annotating the images, the accuracy is ensured at the highest level to make sure the AI in healthcare can give precise results. Our experts can label microscopic images of cells used in the detection and analysis of diseases.
Diagnostic imaging like X-ray, CT & MRI scan gives the better option to visualize the disease to find out the actual condition and provide the right treatment.
Our experts in the image annotation team can generate imaging and label specific disease symptoms using diverse annotation techniques.
Medical image annotation is giving the AI in radiology a new dimension with a huge amount of label data for the right machine learning development.
And for supervised machine learning, annotated images are must to train the ML algorithms for the right diagnostic imaging analysis.
Medical image annotation also covers the various documents including texts and other files to make the data recognizable and comprehensible to the machine.
Medical records contain the data of patients and their health conditions that can be used to train the machine learning models.
Annotating the medical records with text annotation and precise metadata or additional notes makes such crucial data used for machine learning development.
Highly experienced annotators can label such documents with a high level of accuracy while ensuring the privacy and confidentiality of data.
Types of Documents Annotated through Medical Image Annotation:
- CT Scan
- Other Images
To annotate such highly sensitive documents with acceptable levels of accuracy, and AI medical diagnostics companies need a huge amount of such data to train the AI model for the right prediction.
Cogito offers the world-class medical image annotation service to annotate the medical image dataset for AI in healthcare. It can annotate the huge amount of radiology images with high-level accuracy.
Cogito offers a great platform to generate a huge amount of training data sets for AI in various industries and sectors.
AI companies seeking high-quality training data for machine learning development into wide-ranging fields like healthcare, retail, automotive, agriculture, and autonomous machines can get the best quality training datasets available here at the best pricing.
This article was also featured on www.vsinghbisen.com
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This post was originally published by Vikram Singh Bisen at Medium [AI]