Computer Vision in HealthCare
The focus of computer vision revolves around comprehending images and videos. It encompasses a variety of tasks such as image classification, object detection, and segmentation.
Recent technological advancements have proved beneficial in medical imaging, radiology, pathology, and dermatology, making diagnostics easy and simple for health workers. Deep-learning technologies could help doctors by providing second opinions and highlighting potentially dangerous spots in medical reports.
Healthcare is revolutionized with artificially intelligent systems and it has significantly changed the landscape of diagnosis and treatment over the past few years.
Let’s probe into the applications of CV in healthcare:
Gone are the times when people used to get panic attacks and suffer from depression on hearing the news of cancer, that too at the verge of going towards the final stage. The chance to treat later-stage disease was truly a difficult challenge for the oncologists.
By the virtue of ML and AI techniques, we are fortunate enough to detect malign illness and timely move closer to its cure.
Machine learning is adequately used in the medical field to diagnose breast and skin cancers, as skin cancer is often difficult to diagnose early because the symptoms are a lot similar to those of other skin diseases.
Likewise, deep learning computer vision models have attained accuracy as of physicians at diagnostic tasks such as distinguishing between cancerous and non-cancerous skin lesions, malign and benign, and melanomas from moles, which is indeed a remarkable achievement.
Image recognition allows scientists to recognize minor changes between cancerous and non-cancerous cells growing inside the body, diagnose data from MRI scans, and write prescriptions accordingly.
They say desperate times call for desperate measures. We all got slammed by a deadly pandemic last year and eventually placed our last hopes on stopping this spread with AI-driven weapons. It clearly posed a major threat to the worldwide healthcare system, with countries all around the world attempting to combat the disease. Computer vision came to the rescue in making a huge contribution towards overcoming this lethal virus.
The breakneck COVID-19 spread can be tackled with the deep learning computer vision model based on X-ray methodology. COVID-Net, developed by Darwin AI in Canada, is the most widely used method for detecting COVID-19 cases using digital chest x-ray radiography (CXR) images. It has brilliantly shown an accuracy of 92.4% in coronavirus diagnosis.
Coronavirus can be nipped in the bud if we abide by the safety precautions of wearing masks at all times especially while dealing with the public outside.
To prevent the spread of coronavirus and keep one’s eye on people who are not following public health guidelines, face mask detection is solely designed to detect the mask on faces through facial recognition and amalgamation techniques.
Similarly, computer vision technologies assist countries in implementing masks as a control tool for coronavirus sickness.
As a result, for safe public transit, commercial companies like Uber have developed computer vision technologies like face detection which is installed in mobile apps to keep a check on passengers whether they are wearing the mask or not.
You can take a look at our remarkable face mask detection system that is designed by our trailblazers to combat this pandemic. It also generates alerts against people who are not wearing masks, Secondly, it works for multiple cameras simultaneously.
Tumors usually spread quickly in the human body, causing great harm if left untreated. Brain tumors, often cause severe damage to the spinal cord and other parts of the brain. This makes the treatment a tedious task for the patient as well as for the doctor.
Early tumor detection is a blessing in disguise for the patient’s life and thanks to the computer vision applications that have proven immensely useful in the accurate detection of tumors.
Deep learning models and computer vision can detect neurological and musculoskeletal illnesses such as strokes, balance, and gait issues without the need for medical examination. Computer vision applications that analyze patient movement, such as pose estimation, help clinicians diagnose patients more quickly and accurately.
The marvels of computer vision don’t just end here, they help us segregate and identify critically ill patients from the mild sickness category. On this basis, it grants permission to send patients to critical screening wards, for instance, patients who were suffering from COVID-19 had a faster respiration rate, primarily due to severe cough and lungs infection.
Atypical breathing patterns in COVID-19 infected patients can be detected through depth cameras based on a deep learning system that allows subdued and accurate screening of patients on a large scale.
CV is not entirely limited to medical diagnosis, instead, it has stretched out to medical skill training as well. Currently, surgeons are not relying on conventional methods of learning surgery through hands-on practice in operation theatre; instead, simulated surgical platforms have proven to be an excellent tool for assessing and training surgical skills.
Trainees are well equipped with simulation technology, which makes them well-versed before entering into the practical field to perform any surgery. It helps them in attaining a proactive assessment and detailed feedback of their traineeship. It gives them an edge in gaining a better understanding of the patients’ safety and cares in the hospital before performing surgery on them.
Computer Vision can also be used to evaluate the quality of the procedure by evaluating activity levels, detecting frantic movement, and analyzing the number of time patients spend in specific places.
Stroke survivors and sports injury patients usually go through physical therapy sessions for quick recovery. Physiotherapists encounter the pivotal challenge of bearing supervision costs which put the liability on medical professionals, agency or hospitals.
Now you can take rehabilitation training sessions from the comfort of your home through vision-based applications. They are quite economical. It facilitates every age group to get back to normal life and practice movements that support daily life activities while taking care of physical, mental, and cognitive abilities.
Computer-aided therapy allows human action evaluation which is used to aid patients with at-home training, assist them in performing activities correctly, and mitigate the risk of further injuries.
Doctor’s offices, hospitals, outpatient surgical centers, medical labs, medical research institutions, and other healthcare-related organizations can all benefit from using computer vision solutions for various use cases.
Artificial Intelligence is already being used in the medical field. Now, computer vision is being used in this industry, and it has the potential to enable a variety of applications that could save patients’ lives. More doctors are using AI-powered technology to help them better diagnose their patients, prescribe the best treatments, and track the progression of various diseases.