Role of Computer Vision in Medical Diagnosis
The healthcare industry has already seen many benefits coming from the rise of artificial intelligence (AI) solutions. One of the emerging AI fields today is computer vision. By leveraging computer vision technology doctors can analyse health and fitness metrics to assist patients to make faster and better medical decisions.
With growing advancement, computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and prediction of diseases. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much-needed quantitative information not easily available by trained human specialists.
Currently, there are several areas in healthcare where computer vision is being utilized and benefiting medical professionals to better diagnose patients, including medical imaging analysis, predictive analysis, health monitoring and many more.
Types of AI Medical Diagnostics Done through Image Annotation:
Brain Tumor Segmentation -3D segmentation of brain tumor has high clinical relevance for the estimation of the volume and spread of the tumor.
Prostate Segmentation -Prostate segmentation gives precise estimates of prostate volume which further helps for prostate cancer diagnosis.
Kidney Stones Detection-Considering Kidney related problems like infection, stone, and other ailment affecting the functioning of the kidney. There are various popular medical image annotation techniques used to annotate the images making AI possible in detecting the kidney related to various problems.
Cancer Cell Detection-Cancer being a life-threatening disease, its detection at early stage is a challenge. Detecting cancers through AI-enabled machines is playing a big role in saving people from such illnesses.
Dental Image Analysis-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.
Eye Cells 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 diagnose the possible disease.
Medical Record Documentation-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.
Types of Documents Annotated through Medical Image Annotation:
There is no shortage of areas where computer vision could bring groundbreaking innovation to medical imaging: CT, MRI, ultrasound, X-rays, and more are just a few of the use cases.
X-Rays
The role of X-rays is to identify if there is any abnormalities or damage to a human organ or body part. Computer vision can be trained to classify scan results just like a radiologist would do and pinpoint all potential problems in a single take.
MRI
problems in softer tissues, like joints and the circulatory system, are better highlighted by magnetic resonance imaging (MRI). Training a computer vision system to identify clogged blood vessels and cerebral aneurysms can help save those patients who would be under the radar if the images were analyzed by the naked eye.
Ultrasound
Using computer vision during pregnancy and for other routine check-ups could help future mothers see if the pregnancy is unfolding naturally or there are any health concerns to take into consideration. Relying on extensive data sets that combine years of medical knowledge, computer vision-equipped ultrasound systems can show more experience than a single physician would.
CT scans
The advantage of using computer vision here is that the entire process can be automated with increased precision, since the machine could identify even those details that are invisible to the human eye. This method is used to detect tumours, internal bleeding, and other life-threatening conditions
Conclusion
The futuristic dream of completely automated diagnosis still has countless technical and ethical barriers, but consistent advancements have been made in the last years. AI can be used at various stages of the hospital-patient relationship, from easier admission via chatbots to personalized treatment based on DNA analysis. Medical image analysis is already becoming a field where AI proves to bring groundbreaking results.