Medical Annotation Services
What Is Medical Annotation?
Medical images are expertly annotated to create accurate healthcare training datasets for training medical machine learning models. The process involves labeling medical imaging data, including CT scans, X-Ray, ultrasounds, mammography, and MRI scans.
The resulting annotated datasets are then used to train AI algorithms to analyze medical images accurately and improve the diagnosis process helping doctors save time and make informed decisions that improve patient outcomes.
Medical Annotation Services
Medical image annotation is at the core of improving medical AI applications in the healthcare industry. With the growing demand for better patient outcomes, healthcare professionals are turning to AI to diagnose illness, assist in creating patient health plans and complement early detection of anomalies such as cancer-causing cells in the body.
Diagnostic Assistance
Diagnosis is at the core of creating feasible health plans in the medical field. Smart diagnostic assistants’ are trained using medical datasets to identify relationships and patterns that would otherwise be impossible to identify manually. That significantly improves the process of medical diagnosis by helping medical professionals make better and more informed decisions.
Digital Pathology
Leading pharmaceutical companies are using specialized teams to annotate tissues and cells to create medical datasets for training medical AI to understand the effect of medication on the body. Medical AI is then used to help develop better products that treat different pathogens, thereby reducing the burden on health facilities.
Training Next-Gen Deep Learning Products & Services
To identify their effectiveness, medical machine-learning models use deep learning to accurately analyze different medical products, including medical machine components. That helps device manufacturers, health professionals, and healthcare institutions improve patient diagnosis and healthcare delivery.
Robotics Assisted Surgery
Medical AI is helping deliver high-quality surgical procedures that are precise and less invasive, reducing hospitalization and recovery time. Medical CV models are trained to assist surgeons in performing precise incisions that deliver care to the hard-to-reach areas in treating medical conditions such as brain tumors, prostate cancer, and digestive system complications.
Medical Documents Analysis
Accurate analysis of patient medical records is central to making an accurate medical diagnosis. Healthcare facilities often use different provider networks with different data formats to store their medical records. Machine learning models to understand the different medical document formats, analyze them and produce useful insight
Digital Radiology
Digital radiology annotation is useful in helping quickly analyze different medical images, including MRI scans, X-ray images, CT scans and Pet scans. Annotated digital radiology images make it easy to identify bone fractures, bone structure or any other area of interest to carry out the necessary advanced diagnosis.
Let Us Be Your Preferred Choice For Medical Annotation Services
An AI model’s success is determined by its training data. Inaccurate ML models are the result of poorly annotated data.
Here at Annotation Box, we provide high-quality medical annotation services to accurately train your AI Application for the precise diagnosis and treatment.
95% high accuracy
At Annotation Box, our annotation experts guarantee nothing short of 95% accuracy. We pride ourselves as an accuracy hub.
cost-effective pricing
Our pricing model is designed to cater to the needs of our clients by offering competitive prices and not putting the project needs in jeopardy
data security
We understand medical images and data are extremely sensitive. We have invested heavily in our data security systems to ensure that any data you provide remains secure and private.
Different Types Of Medical Annotation Techniques
Medical image annotation is carried out for different purposes. Depending on the intended purpose, different data annotation techniques can be used to achieve the desired results. The annotation techniques used to annotate medical image is the same as those used in other image annotation projects.
Polygon Annotation
Polygon annotation involves drawing a line along the entire area of interest. The line is drawn by plotting points along the edges of the annotation area and then joining the points using a line. Polygon annotation can train medical AI to detect bone fractures or unwanted regions in a medical image.
Polyline Annotation
Polyline annotation draws straight lines along the object of interest. It can be used to train medical AI to identify the structure of different body parts, including bones, muscles and blood vessels. With accurate polyline annotation, doctors can accurately diagnose CT scans, MRIs, and X-ray reports and provide quick treatments.
Semantic Annotation
Semantic segmentation involves annotating an image at the pixel level. It groups pixels of the annotation area and assigns them a predetermined class label. Semantic segmentation is useful in medical image analysis helping medical professionals easily identify abnormal regions.
POINT OF INTEREST TAGGING
Point of interest tagging places points at predetermined points in an image. The annotated image data is then used to train medical machine-learning models to identify the position of the said point of interest. This annotation technique is useful in identifying the absence or presence of certain medical conditions in an image.
Landmark Annotation
Landmark annotation is also called pose estimation or dot annotation. It involves making dots throughout the object or area of annotation to help computer vision models identify the structure of the object. In medical annotation, pose estimation can train a computer vision model to identify the structure of normal organs in the body.
Bounding Boxes
Bounding box annotation involves drawing a box around the annotation object. It can be used when the shape is not essential. Bounding boxes find many uses in annotating medical images, including in digital radiology, to identify foreign objects in the body, fracture regions and abnormal regions.
Why Choose Us?
At Annotation Box, we provide holistic and HIPAA-compliant data solutions. We strive to deliver high-quality datasets promptly and within budget. Our in-house quality assurance team works round the clock to ensure the delivered annotated images guarantee nothing short of a successful medical image annotation project.
1000+
Trained Experts
95%+
Accuracy
50+
Happy Clients
450+
Successful Projects
Get Us Onboard For Medical Annotation Services
At Annotation Box, our annotation team is diverse, with over five years of medical annotation. We are committed to improving healthcare by providing accurate medical AI training data. We share in your vision to make the world a better place.
AI Application In Healthcare
Artificial intelligence in healthcare is crucial in improving healthcare procedures resulting in quality healthcare services. Medical AI is used to assist in patient diagnosis, analyze the effectiveness of the medicine, help create health plans and aid in surgical procedures.
Help Pathologists in making fast & accurate treatments
Diagnostic and Reporting on X-rays, CT Scans, MRIs
Skin disease analysis & accurate treatment
Medical imaging analysis and pattern recognition
Early detection & treatment of high-impact diseases
Managing medical records & other data
How We Work
STEP : 1
Project Assessment
Upon receiving the inquiry we assign experts to understand your project requirements. After in-depth research by our experts and assessing your requirements, we deploy the best data annotation solution for you.
STEP : 2
Sample Data Labeling
After deploying data annotation solution. We begin our work. The first step is to ask for your samples. Once we receive your samples, we run sample data labeling. We label the samples and send you back for your review.
STEP : 3
Training
Once you’re satisfied with our sample. We deploy a training module for the team to impart an in-depth understanding of the project.
Our Quality analyst keeps checking for the desired quality output with our annotators.
STEP : 4
Production
Our dedicated project manager will oversee the team and monitor them constantly to ensure the annotators are meeting the desired output quality set initially and completing the project on time. Annotation Box puts accuracy first and foremost.
STEP : 5
Evaluation
We believe in transparency and high-quality data annotation. Through our continuous feedback cycle, we make sure the annotation is done correctly. Our flexible workforce enables us to scale up production at any time.
STEP : 1
Project Assessment
Upon receiving the inquiry we assign experts to understand your project requirements.
After in-depth research by our experts and assessing your requirements, we deploy the best data annotation solution for you.
STEP : 2
Sample Data Labeling
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STEP : 3
Training
Once you’re satisfied with our sample. We deploy a training module for the team to impart an in-depth understanding of the project.Our Quality analyst keeps checking for the desired quality output with our annotators.
STEP : 4
Production
Our dedicated project manager will oversee the team and monitor them constantly to ensure the annotators are meeting the desired output quality set initially and completing the project on time. Annotation Box puts accuracy first and foremost.
STEP : 5
Evaluation
We believe in transparency and high-quality data annotation. Through our continuous feedback cycle, we make sure the annotation is done correctly. Our flexible workforce enables us to scale up production at any time.
Medical Annotation Use Cases In Healthcare
In healthcare, annotating medical images is helping improve service provision. Leading digital health companies need high-quality data to improve their medical AI applications used for different purposes in healthcare.
Radiology
Annotated medical image data is needed to train medical AI accurately analyze medical images, including CT scans, X-rays and Pet scans. The images must be accurately annotated to avoid human error that could lead to the wrong diagnosis.
Cardiology
Medical annotation is used to train medical AI models that interpret echocardiograms accurately and quickly to identify signs of cardiac problems, such as blocked vessels that could lead to heart diseases.
Dentistry
In dental imaging, medical annotation plays a crucial role in training AI models to detect and identify changes in the dental structure, including jaw and teeth alignment. It helps orthodontists determine treatment or correctional procedures for issues such as cavities and lesions.
Ophthalmology
In ophthalmology, medical annotation is helping in early detection and treatment procedures for eye problems such as cataracts, retinopathy and glaucoma. That is achieved by allowing ophthalmologists to identify signs and symptoms of these conditions in advance.
Pathology
Human bodies are made of different tissues and cells. Different pathogens affect different cells or tissues in the body. With image annotation, medical AI can be trained to identify the specific cells or tissues affected by a pathogen allowing for targeted medication.
Dermatology
Dermatologists are using labelled dermatological data to train AI tools to detect different dermatological conditions in patients. Medical AI tools also help develop personalized treatment based on a person’s skin type.
Medical Annotation Services – FAQs
Q1 What is HIPAA?
The Health Insurance Portability and Accountability Act is a federal law in the US providing guidelines for protecting medical data.
Q2 Will my annotation project require HIPAA compliance?
Any medical annotation project dealing with private health information must be HIPAA compliant. That includes ensuring that the data does not fall into the wrong hands.
Q3 Where do you get medical annotation data from?
Medical annotation data is hard to come across due to the sensitive nature of the data. You can approach health facilities but need a license to access the data.
Q4 I have the data; what next?
If you have the annotation data ready, approach a reputable data annotation service such as Annotation Box to discuss the project details.