How AI (CV, NLP) is used in Combating Covid-19
The first few months of 2020 were marked by the Covid-19 outbreak across the world and has a dramatic change in our lives. The Covid-19 pandemic has posed critical challenges that boost the adaptation of AI in various fields. The widespread use of autonomous vehicles and collaborative robots has become a reality.
The serious pandemic situation allows the professionals to improve the performance of many existing systems in the medical field to diagnose the disease, predict the risk, predict the disease diffusion rate and suggest preventative measures. A large amount of data is generated from the spread of Covid-19.
Thanks to big data analytics that its powerful techniques are fused with the AI to process a bulk of data for extracting the patterns and relations of the data. This will assist the medical professionals to diagnose the intensity of the disease, predict the risk factor, and making health care decisions. The adaptation of the sophisticated technologies has a drastic change in many sectors like pharmaceutical companies are scrutinized to keep a record of the pricing, availability, and productivity of the drugs.
Various chatbots are developed by IT professionals that provide virtual nursing services to many Covid-19 patients. AI-based drones are used to transport medicines and health care facilities to remote areas. At the same time, the AI thermal cameras in airports, malls, hospitals, and offices are used to stop the Covid-19 spread.
Role of Computer Vision to Combat Covid-19
The widespread of Covid-19 across the world causes an immediate need of the researchers and government around the globe to find new and effective ways of treating pandemic Covid-19. In response to the call, many researchers from all over the world are working in finding ways to cure this disease through computer vision healthcare applications.
With the help of efficient, affordable, and widely available diagnostic procedures the health care professionals determine the intensity of the disease, its expected development, and the chances to get better. Many of the computer vision use cases in combating Covid-19 are described in the next sections.
Chest X-ray radiography is used to detect Covid-19 symptoms through the CT scan imagery just like other diseases like Asthma and Cystic fibrosis. The images are detailed enough to visualize the soft tissues of the lungs in order to diagnose Covid-19, unlike other X-ray images.
Computer vision models are aiding professionals to fine-tune models by using a wide range of chest radiography images associated with Covid-19. Various deep learning-based models are used to detect Covid-19 features.
The image samples need pre-processing to get a clear visualization of the infected area of the lungs. The most popular system that aids in diagnosing Covid-19 through chest radiography images is known as COVID-Net, developed in Canada by Darwin Al.
The system is trained over the COVIDx dataset consisting of 16,756 chest X-ray images of 13,645 patients and achieved the Covid-19 diagnoses accuracy of 92.4%. The main aim of developing this model is to assist doctors in determining the disease complexity and to check the recovery progress of the patients.
Various surveys have identified that pandemic drones are used for various purposes around the world. One of the major tasks of the pandemic drone is to supply medical facilities in remote areas. Many countries have deployed drones for medical and Covid-19 commodities delivery. The benefits of using drones are speedy delivery, limited physical contact, and low risk of disease transmission.
Another use case of the pandemic drone is the aerial spraying covering 3km2 of area. Many countries are using this facility like China, Spain, South Korea, and UAE. Drones are also facilitating law enforcement and surveillance agencies by deploying drones for spreading public service messages about health and safety during the pandemic situation. Many countries are using drones embedded with AI-based thermal cameras to trace infected people.
Companies using Pandemic Drones
- Dragonfly: The company “Dragonfly” claims that it can use pandemic drones to track people with Covid-19 symptoms like high fever, sneezing, coughing, heart and respiratory rates with the help of specialized sensors, thermal cameras, and computer vision techniques. It also claims that autonomous drones detect infectious conditions from the height of 190 feet.
- Flytrex: This company “Flytrex” provides an end-to-end delivery service for delivering medical and covid-19 commodities. The drones are small in size that can carry a maximum weight of 6.6 pounds and cover a distance of approx. 6.2 miles.
- Alphabet’s Wing: During the pandemic hit, the Wing company can deliver medicines, toilet paper, and groceries. The company also start delivering school and library books through a drone. The drones are operational in Australia, Finland, and United States.
- Support Vaccination Development
The computer vision techniques allow the researchers to design the Covid-19 vaccine. Researchers from all over the world have studied immunology along with machine learning techniques to identify the components of the dangerous virus that causes lung failure and make the immunity system week.
The researchers are still trying to combat covid 19 through the generation of effective drugs. For that, they are using computer vision techniques along with machine learning tools to visualize the patterns and relations between the data samples.
Many scientists and pharmaceutical companies are working to find the main immunity booster protein that would make a good vaccine. Each viral protein is scanned through dedicated AI-based software to identify the strong antibody targets of the virus regions.
Various machine learning-based models are trained to discover the effective drug used for the cure of Covid-19. For that, large training samples are needed for the better performance of these models.
Companies Leading the Race of Covid-19 Vaccine
The top six companies that playing their role in the Covid-19 vaccination development are:
Role of Natural Language Processing (NLP) to Combat Covid-19
Natural Language Processing (NLP) plays a great role in the misinformation identification regarding Covid-19 that is widely spreading on social media, print media, and other sources. “Covid-19 is just ordinary flu”, Covid-19 is only cured by Chinese medicine”, Covid-19 is spreading through eating bat soup”.
All these types of misinformation can lead to the severity of the Covid-19 spread. To detect this misleading information, deep learning models using NLP techniques play a vital role. Not only this, but NLP has also widespread uses for combating Covid-19 that are discussed in the next sections.
Natural Language Processing Advances Clinical Decision Making
Natural language processing along with the advanced machine learning models helps many radiologists to make clinical decisions efficiently. Coronavirus is spreading widely across many countries which results in a large number of Covid positive cases.
Efficient deep learning models fused with the natural language processing techniques are used by the clinical experts to recognize certain conditions in the radiology report and suggest suitable precautionary measures or medication to the patient. The providers will dictate the findings whereas NLP is used to interpret that note and machine learning will further find the incidental findings.
The decision is based on the decision tree algorithm in order to recommend suitable medication for the patient with Covid-19. The system goes through a black-box testing mechanism where large samples are used for the training of these models to get accurate results.
However, radiologists do not rely on these systems. If any sort of ambiguity occurs, professionals seek help from other sources. AI professionals are still working on these systems so they work on different dictation styles.
Top Clinical Decision Support Tech Vendors
Natural Language Processing Detect Social Isolation in Patients
The spread of the Covid-19 virus around the globe has put the students and job workers under severe pressure to quarantined their selves whereas the infected people are also put into quarantine to prevent the Covid-19 spread.
The closure of schools, offices, markets, and public places for a long time triggers many mental health issues like depression, obesity, and hypertension. Social distancing is necessary to reduce Covid-19 spread whereas it also makes us feel lonely and increase stress and anxiety.
To identify the socially isolated patients, a machine learning-based model along with natural language processing techniques is used. The clinical narratives are present in electronic form where NLP is used to interpret those narratives to identify these patients.
Still, the system does not outperform in identifying socially isolated patients because their variants are not fully identified. Experts are still working on the development of a system to identify the traits that characterize socially isolated patients.
Improving Patient Health Literacy
NLP is a great tool for helping patients with low health literacy. The development of patients portals using NLP-based algorithms has eased the lives of many patients who can’t have access to doctors. The NLP algorithms are used to interpret the narrative of a layman and suggest suitable precautionary measures to him.
For example, a patient who is a chain smoker has low health literacy and can trigger many serious health issues like cardiac arrest, asthma, and lung cancer. Patients from all over the world are using these patient portals and can experience many benefits like ease of use, satisfactory system performance, layperson definition interpretation, and good visual display.
A team of IT experts has developed an NLP tool called NoteAid to increase the use of patient portals. NoteAid uses two strong algorithms i-e., MedLink and CoDeMed that link the definition of an ordinary patient to the complex medical terms. Still, the system needs a lot of fine-tuning to interpret all the narrations of a layperson to suggest accurate medication or preventative measures.
Health professionals have concluded that the use of NoteAid with the patient portal like VA’s MyHealth Vet can improve the patient’s health knowledge, experience, and engagement.
What Should You Prefer AI-Powered Smart Data Preparation in Building ML Models to find Solutions for COVID-19?
Every computer vision, deep learning, and NLP model needs labeled training data. To get highly accurate labeled data an AI-based data annotation platform is best preferred over a manual annotation.
Manually annotating data can be a tedious task. Manual Data Labeling Challenges:
- Managing and maintaining the quality of data labeling
- Workforce management
- Keeping a track of the cost incurred
- Compliance with data privacy requirements
- The task to ensure data security
Labellerr – The Fastest and Smartest Data Annotation Platform
Labellerr provides you simple, feature-rich, affordable data annotation solution.
Why Choose Labellerr?
- Data Labelling at scale is an important concern for an organization since creating labels on large data sets by hand is often too slow and expensive. Labellerr solves this problem with their agile ML-Powered data annotation platform.
- Work Quality and Worker productivity is difficult to track in the case of crowdsourcing and freelance data labeling services. Hence now with Labellerr’s marketplace, you can choose from our hand-picked and most trusted vendors to get data labeling tasks done.
- Domain and context capabilities specific to tasks are limited with workers on crowdsourcing platforms, contractors, and freelancers.
So, if you wish your data annotation task to be automated and error-free then choose Labellerr.
Benefits of Labellerr’s Data Annotation Platform:
- Label data at 10x speed using Labellerr’s ‘Auto Labeling’ feature
- Track work quality and worker productivity with a personalized dashboard experience.
- Get relieved from the hassle of reviewing each dataset, instead review only the ones having low confidence scores.
The above is still just an indicative list, the superior benefit of adopting an automated way of labeling dataset is peace-of-mind and trust. The idea here is simple – let the machine do all the work for you so that your focus can be on your customers!