Diagnostic procedures in the medical field are challenging. Although there are countless known diseases, there are only a limited number of potential symptoms. It takes time to diagnose and frequently involves several lab tests. Unintentional mistakes are common, and human sight and intelligence are limited in their ability to identify diseases. To overcome all such drawbacks and give healthcare professionals real-time insights into reports and scans, ML/AL (artificial intelligence and machine learning) models are trained.
A trained ML model can help analyze reports, prescribe medications, and draw conclusions regarding diagnoses. For instance, an ML model trained using multiple CT and MRI scans can automatically identify the presence of a particular disease (say, lung infection) without a doctor’s intervention.
But how is an ML model trained?
ML models can be trained through video annotation oworkers (the process of labeling data), further divided into two broad categories - Image and video annotation. Using annotation enables ML models to learn from previous cases and predict new and unlabeled images.
In
this blog, we will briefly discuss video annotation and how it benefits
healthcare centers.
What Is Video Annotation?
Video annotation is the practice of labeling or categorizing video clips. It is used to train ML models to detect or identify things. In simple words, video annotation is a technique for teaching computers to recognize objects.
In video annotation, a combination of human annotators and automated tools are used to label the objects in video footage. After then, the ML/AI model analyzes this labeled video to eventually learn how to spot the target objects in new, unlabeled videos. The primary purpose of this is to allow a computer to mimic the perceptual qualities of human eyes.
An
excellent example of video annotation in the medical field is the capacity of
ML to identify particular cell types and other biological components.
Why Is Video Annotation Important in Healthcare Centers?
Medical centers produce data in the form of surgical videos, therapy sessions, security footage, and many others. These video clips require detailed labeling. Video annotation helps the medical center to label its videos efficiently and feed them to AL/ML models for future analysis. Moreover, once the video has been labeled, it can be used by different models. These models may be able to spot unexpected changes by comparing a group of annotated videos across time.
For instance, a hospital performs and records heart surgery. This video is then labeled frame-by-frame through video annotation. Once the labeled data is fed to ML/AL model, it can;
●
Enhance
and communicate the procedure to the surgical community.
●
Strengthen
the surgical team (e.g., reducing adverse events)
●
Utilize
analytics to improve surgical procedures
●
Make
reference databases for automatic video recognition.
What Are the Applications of Video Annotation In Healthcare?
● Helps in surgical guidance
A trained ML/AI model can help surgeons to perform surgery. For instance, a doctor has to remove the gallbladder. A surgeon makes a keyhole incision, inserts a camera, and then takes out the organ to accomplish the surgery. ML models can help the surgeon understand the safest place to cut. The operating room monitors show the gallbladder image in two different colors, red regions to avoid and green regions safe to cut.
To
perform this entire procedure, ML/AI are the first trained. Numerous
photographs of gallbladders from previous operations are examined by highly
qualified and experienced doctors. Then, they employ video
annotation services or
tools to pinpoint the sections of
the image that can be safely cut. To apply the same knowledge to new cases,
machine learning algorithms use this training data to learn about gallbladder
surgical operations.
● Monitor Patient’s Health
Medical professionals are increasingly using ML/AL technologies to track the well-being and fitness of their patients. These evaluations allow medical professionals to make faster, better judgments, even in life-threatening situations.
For
instance, a senior citizen receives care in a nursing facility without access
to personnel or doctors. The patient tries to wake up but falls to the ground
due to weakness. An ML/AI model can detect the patient’s situation and inform
the doctors. Basically, these ML/AI models have been trained through multiple
standing/falling videos. Video annotation helped the ML/AI system understand
the patient's posture at the given time. This helps the healthcare center take
care of their patients even without staff.
● Avoid Human-prone Error
A
report states, in around 1500 procedures per year in the US, surgeons
forget their instruments inside the patient. To compensate for the doctor's
error, the patient might need to undergo extra, potentially dangerous
operations. This can create havoc in the life of both; the patient and the
doctor. To avoid such incidents, AI/ML
models are trained with full-body CT scan video. These videos clearly label a
healthy human body for ML to compare its patient’s body after the surgery.
Surgeons can depend on ML/AI models to examine the after-surgery body of the
patient.
● Reduce Patient Mixups
In
the medical field, patient misidentification is a frequent problem. Patient
mix-ups lead to serious legal problems for the medical center. To avoid this
mess, medical centers leverage a trained ML/AI system to identify the patient.
It recognizes human faces and compares them to a database of faces. After that,
a doctor can access the entire medical history of the patient. This entire
process is done through video annotation. Multiple live security videos and
private doctor-patient sessions are combined, labeled, and fed to the ML/AI
model.
● Improved Cancer Detection
The
patient's life or death depends on early detection and treatment for any number
of disorders, like cancer and tumor. Early recognition of the symptoms
increases the patient's probability of survival. ML/AI models are trained with
the help of thousands of labeled videos to detect even the smallest change in
the human body accurately. Additionally, It is fed with information about
healthy and diseased tissues to improve the detection of skin and breast
cancer.
● Detecting Minor Diseases
Apart
from cancer, a trained ML/AI model can detect all dental and bone problems. A
dataset is created using videos of healthy teeth and bones, then it is used to
train the ML through data annotation. Data annotation helps to label all the
minor parts of the teeth and bone, showing the actual healthy body. Later,
ML/AI models can use these references to identify any unhealthy activity in the
body.
What Is the Future of AI in Healthcare centers?
Despite being able to identify key
issues like cancer, AI-powered solutions have made only modest strides in
addressing crucial issues and have not yet significantly impacted the global
healthcare industry as a whole. However, in the coming future, it is expected
to diagnose significant diseases, like ALS, stroke, spinal cord injury, and
other hazardous diseases, and will create a great impact on healthcare systems.
Apart from identifying diseases, AI
will reduce wait times, enhance worker productivity, and manage the increasing
administrative load. Slowly, clinical professionals will trust AI more and more
to supplement their expertise in fields like surgery and diagnostics.
Conclusion
All in all, video annotation helps
to label videos and train M/AI to perform efficiently. These models help
healthcare practitioners to make cancer predictions and identify heart and
joint problems, among other conditions. Moreover, doctors can analyze images
instantly, eliminating the need for several scans or a drawn-out image review
procedure to find anomalies.
However, video annotation should be done by doctors and specialists for accuracy. However, since any healthcare firm would not want to waste its doctors' time, the wiser choice is to outsource video annotation service to a reputed and experienced organization. Outsourcing is cost-effective. Additionally, it helps healthcare organizations manage their workload better while keeping their organizational efficiency up.
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