The covid-19 pandemic came into our life as a storm that turned our lives upside down. The intensity of this pandemic was so widespread that it forced us to forget many habits we were used to. We have been forced to start adapting to new habits like wearing masks, using sanitizers, etc. No case study or assignment help could have prepared us for this impending doom. However, the human race has always become triumphant over the several challenges they faced over time, and we aim to repeat the feat this time as well. While the medical and pharmaceutical fraternity is working tirelessly to formulate a cure, the tech industry is not trailing behind.
A lot of technologies are emerging to address the Covid-19 challenges. They are harnessing the power of AI and computer vision to develop the tools we need to diagnose, prevent and treat covid-19. Here are eight applications that are worth checking in this battle –
Digital X-Ray radiography, or CXR, is a new technology that has decreased the costs of chest pathology when compared to computerized tomography imaging (CT Imaging). CT imaging also needed an elaborate infrastructure as most diagnostic centers weren’t able to afford something as costly as a CT Imaging machine. So, digital X-ray imagery has become increasingly popular for diagnosing Covid-19 symptoms. This technology uses computer-aided methods to detect any blockages and patches inside a patient’s chest.
This machine has a wide application as doctors also use this for treating other diseases like cardiac issues and even cancer. However, detecting the symptoms of Covid-19 isn’t easy with X-ray imagery. The pathologists need to pre-process the visuals since the digital images lack contrast against the soft tissues. Over the last couple of years, we have seen multiple digital X-ray radiography machines being launched. One of the best among them is Covid-Net. This machine was developed especially to treat Covid and has a detailed dataset to deliver near-perfect accurate results.
CT or Computed Tomography is a non-invasive test to produce an in-depth image of the patient’s chest. This technology uses radiology examination to generate a more detailed image than any conventional x-ray machine. CT imaging helps you get the details of all the bones, muscles, fats, and organs, not just tissue images.
This technology has helped researchers to understand the symptoms of Covid-19 and how it affects the chest. Computed Tomography showed us that it resembles many features of pneumonia, like affected lower lobes, ground-glass opacification, and other significant features depending on the severity of the disease.
CT imaging uses UNet++ semantic segmentation model to highlight the infected areas from an image. This helps them to distinguish between the CT images of a healthy and infected patient. This model can give accurate predictions up to 95.24% during Covid-19 diagnosis.
Masked Face Recognition
During the early days of Covid-19, doctors advised all of us to use masks as a primary prevention method from the virus outbreak. We abundantly used N-95 and Clinical masks as the doctors prescribed to check the spread of the virus. Many governments even mandated the use of masks as a control strategy. So, scientists came up with a computer-aided solution to facilitate its implementation. Computer vision systems helped them develop masked face recognition technology using multi-granular face recognition models.
This technology has managed to achieve more than 90% accuracy in correctly detecting faces from a masked face image database. Although some companies implemented it in certain special facilities for organizational benefit, the data was later made public. This data contained three types of datasets, and they used it for further research like –
- Researchers used MFDD or the Masked Face Detection Dataset to train the masked face detection model for specific masked face recognition tasks.
- The Simulated Masked Face Recognition Database, or SMFRD, helps users to simulate masked faces by scanning over five million faces.
- RMFRD, or the Real-world Masked Face Recognition Dataset, is the world’s largest masked face database. It comprises real-world data with pictures of thousands of people, both with and without wearing masks.
Infrared thermography is used to detect the traits of Covid-19 at an early stage. If any people exhibit any early symptoms of coronavirus, thermography is an effective way to tag them. Most airports and malls have installed thermography on their premises to screen any individuals whose body temperature exceeds 98.3 degrees Celsius.
Thermal guns use this technology to easily detect if anyone has a fever. The tester simply needs to put the gun on the forehead of the suspected individuals to get the readings. Infrared screening is also possible with CCD cameras and thermography. These are better and more effective methods than thermal guns since it doesn’t involve any physical contact. Yet you can get accurate vital sign measurements through the MUSIC algorithm and feature matching.
Photographers mostly use drones for stunning aerial shots. A drone uses computer vision to generate digital images and remote sensing technology to operate. You don’t need to personally man a drone, and this feature led to the introduction of pandemic drones. Medical facilities and NGOs use these drones to help Covid-affected patients without being near them.
Researchers also used similar applications to create vision-guided robots. These used 3D object recognition technology and thermography to identify infected people and help them by delivering medical supplies, foods, and other necessary items.
Scientists and researchers also used computer vision to scan for germs during the battle against Covid-19. They formed a convolutional neural network for better screening of germs. This technology identifies bacteria with light sheet microscopy images. The results have shown that this method can ensure approximately 90% accuracy in correctly detecting viruses.
Disease Progression Score
The medical experts and researchers identified that they could treat the patients better if they could classify patients according to the severity of the infection. For example, diagnostic centers can use computer vision to screen and segregate patients who are critically ill and needs immediate medical attention.
So, with a set parameter to assess the severity of the patients, doctors can classify their patients more easily. They learned to make a disease progression scorecard by measuring the infected areas from CT images. We can track these images to monitor the progress of the patients over time. This will help us easily understand which patients are not recovering per the schedule and need more attention.
Researchers have also used computer vision-aided cameras for identifying anomalies in respiratory patterns. One such example is RSM or Respiratory Simulation Model. It is based on Gated recurrent units of GRUs neural network. This classifies six significant respiratory patterns to identify clinically and critically ill patients. For example, people suffering from Covid-19 have a faster respiration rate. This model maintains a 94.5% accuracy rate to identify the respiration rates and detect if they are infected with the virus.
Support Vaccination Development
Researchers also take the help of computer vision applications for QSAR analysis. QSAR, or Quantitative structure-activity relationship analysis, incorporates 360° images of molecular conformations into computer vision.
Using deep cameras, researchers can get a detailed image of the molecules, and this helps them to create new drugs and supports the development of vaccinations.
Computer vision technology is multidisciplinary in nature. Hence, it is used in various fields and applications, from medical to engineering. It is also extensively used by support teams to support the main teams. Since artificial intelligence and machine learning makes the creation of new things so easy, it is becoming more popular as an option to combat the Covid outbreak. Read this blog to know how technology is helping us to fight the pandemic and get inspired to make better and newer applications using computer vision. Hire good coursework help experts and learn how to develop an app that can eradicate diseases for good.
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