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How AI is Used in Healthcare to Control the Coronavirus Disease?

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How AI is Used in Healthcare

Coronavirus or scientific name COVID-19, is one the most deadly virus of the century infected around 10 million people and killed tens of thousands of folks globally. Being declared a pandemic by WHO, this infection is spreading among the people at a very fast speed, bringing down the mass population at the hospital bed affecting the world economy.

As, coronavirus is a highly contagious disease, worldwide Governments have lockdown the cities to stop or minimize the further spread of infection due to community transmission. However, this kind of approach is also affecting the economic activities, impacting the global economy due to halted productions of goods and services.

Actually, the reason behind lockdown is that there is no medicine or vaccine specifically developed to treat or cure the coronavirus-infected patients. Many people are confined to their hospital beds with patient whiteboards as their main means of communication. Many of them who are infected with coronavirus infection in critical conditions are killed to death.

Though, medical researchers and scientists are endeavoring day-night with their best efforts to develop the medicine, but until such medicine or vaccine get developed to control this infection spreading among more humans is the best option to minimize its post-impact on the whole world in terms of causalities, social changes or economic growth.

Also Read: How Exactly Coronavirus Attacks, Infects & Affects Body to Death

AI in Healthcare

However, meanwhile, countries are using the cutting-edge and most advance technologies into the healthcare system to combat the coronavirus. And AI in healthcare is playing a vital role in fighting with this disease and assist medical staff to minimize their efforts and help patients to recover soon without risk of community transfer.

Also Read: Where Is Artificial Intelligence Used: Areas Where AI Can Be Used

Though, Machine Learning (ML) and Artificial Intelligence (AI) based systems, machines and models are being used in hospitals, medical centers and healthcare organizations to track the activities of patients, monitor their health, assist them physically or give the useful information of patients to the doctors to provide the right treatments and care.

Actually, there are many AI-enabled devices, machines, systems and applications can be used to deal during the treatment and care of patients fighting with such deadly disease. So, right here we will discuss how big data and AI is used in the healthcare system to combat the coronavirus outbreak in the different part of the countries globally.

AI Robots for Food Deliveries & Disinfection

Robotics technology is not new to the world, but AI-enabled robots are playing a crucial role in assisting medical staff or helping patients. It is used to deliver food, spray disinfectants and performed basic diagnostic functions, in order to minimize the risk of cross-infection, which is the most dangerous part of coronavirus transmission.

In various hospitals, robots are used to diagnose and conduct thermal imaging to check body temperature and other symptoms in humans. In China, Shenzhen-based Pudu Technology, which usually makes robots for the catering industry, installed its machines in more than 40 hospitals all over the country to help medical staff.

AI Drones for Monitoring & Medical Supplies

Similarly, AI in drones developed with the help of machine learning technology to train such autonomous flying objects to perform various actions at places where humans can’t reach easily or need extra time or effort to complete the action.

Actually, AI-enabled drones are equipped with multiple advance features like a high-resolution camera with object and face detection technology or navigate automatically while controlled by the people distantly to deliver goods easily.

Also Read: How AI Based Drone Works: Artificial Intelligence Drone Use Cases

Amid coronavirus control, Drones are also flying with QR code placards that can be scanned to register health information. While agriculture drones that are used for spraying pesticides are now used to spray disinfectants in the countryside.

Furthermore, during the lockdown, face recognition enabled Drones are also being used to alert or announce warnings to the citizens to not come out of their homes and also advise them to wear a mask and maintain the social distancing.

In China, drones are used to alert people while creating a kind of automated surveillance in infected areas. The drones are also being used to inform people about the areas that could be potentially infected, thanks to the integration of AI in drones.

Autonomous Vehicles to Transport Goods

Though, self-driving cars are not full-fledge in use, but AI-enabled driverless autonomous vehicles are used to deliver essential products. To avoid human contacts at coronavirus hospitals and healthcare centers, autonomous vehicles are proving to be of great utility in delivering essential goods like medicines supplies and various foodstuffs.

Image Courtesy: MIT Technology Review

These driverless vehicles are trained and developed with AI-based technology to detect objects and visualize their surroundings through computer vision and sensor technology to move in the right direction without any collision.

Watch Video: How AI Robots & Drones are used in Coronavirus Control

And some of the autonomous vehicles are also used in China is used to disinfect hospitals. Apollo, a Baidu’s autonomous vehicle platform, working jointly with self-driving Car Company to deliver supplies and food to a big hospital.

Facial Recognition System to Detect Humans

Using computer vision technology, the face recognition system is utilized to detect humans. And AI-enabled security cameras is making security and surveillance system more effective to monitor the human activities around the cities.

Meanwhile, AI companies using big data are also developing a dashboard for face recognition and infrared temperature detection in all the leading cities where mass gathering or movement of people happens every day.

Such AI security camera system also helping healthcare authorities and security agencies to monitor the people quarantined step outside during lockdown. It can detect people wandering in groups or not wearing the facemasks.

Watch Video: In China How Face Recognition is used to Identify People Wearing Mask

Shared Big Data for Analysis & Predictions

The AI and ML-based models are developed with a huge amount of data sets from that particular field. Similarly, in the case of coronavirus outbreak control and treatment, the big data is analyzed to predict the further spread of such a virus.

Social media giant, Facebook is working with researchers at Harvard University’s School of Public Health and the National Tsing Hua University, in Taiwan, sharing anonymized data about people’s movements and high-resolution population density maps, which help them forecast the spread of the virus in other parts of the different nations.

This social media networking website is also helping partners understand how people are talking about the issue online, via tools to aggregate social media posts talking about such an epidemic. Previously, Google search data has been used to track infectious diseases helping healthcare authorities to take preventive measures timely.

Similarly, Smartphone apps are also being used to keep a tab on people’s movements and ascertain whether or not they have been in contact with an infected person. A leading Chinese telecom company China Mobile using to send text messages to state media agencies, informing them about the people who have been infected in the country.

Helping to Discover the Drugs for COVID-19

AI is not only integrated into machines through machine learning but playing a great role in discovering the right drug for COVID-19 like new diseases.

Actually, in drug medicine, AI can help in various ways to combat this deadly disease. AI is helping medical researchers and doctors to rapidly develop antibiotics and vaccines for the COVID-19 virus, scan through existing drugs to see if any could be repurposed and design a drug to fight both the current and future coronavirus outbreaks.

Even, an AI-driven platform for drug discovery has identified nine potential drugs that can relief against COVID-19 and six of them are already approved in many countries and used by doctors to cure patients helping them to recover.

It can identify molecules with potential effects on the coronavirus replication. The fact that this time the potential treatments were found among existing drugs marks a significant improvement over previous efforts to use AI against COVID-19.

Meanwhile, Google-owned AI Company, DeepMind, has used its AlphaFold system to release structure predictions of several proteins associated with the virus. AI is also helping to develop the vaccine which could take 18 to 24 months.

Also Read: Why Vaccine Development Process for New Diseases Like Coronavirus Takes 12 to 18 Months

Doctors using the medical images datasets of coronavirus infected person to understand the complexities of infection and analyze the epidemiologic characteristics, clinical manifestations, chest images, and laboratory findings. And the diagnosis of 2019-nCoV pneumonia can be used to train the AI models to detect similar symptoms among patients.

Summing-up

Overall, AI integrated into the various systems, machines and devices are making the healthcare system more automated with an acceptable level of accuracy. And if machine learning engineers and data scientists use more quality healthcare training data to develop such AI models, it would become easier to predict or combat with such deadly diseases.

Companies are providing the healthcare training data in the form of annotated images of radiology scans like MRI, CT Scans that are used to train the computer vision-based AI models. They can annotate all medical imaging to provide algorithm training datasets using various popular image annotation techniques like semantic segmentation and polygon image annotation for organ segmentation and disease diagnosis with the best level of accuracy.

Also Read: How Does AI Work in Radiology: Applications and Use Cases

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Artificial Intelligence

What is the Difference Between AI, Machine Learning & Deep Learning?

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ai vs machine learning vs deep learning

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are the most widely used interchangeable words creating confusion among many people globally.

Although, these three terminologies are typically used interchangeably, but they all are different from each other especially in terms of their applications, capabilities, and results.                 

Understanding the difference between AI, ML, and deep learning is important to utilize the precise applications of these jargons and take the right decision while dealing with AI, ML, or DL related projects.

Before we start, I would like to show you few images (see below) that will give an overview, how AI, ML, and DL are different from each other or how these three terminologies are related to each other.

The easiest way to understand their relationship is to visualize them as concentric circles with AI – which is a broader area, then ML – which is the branch or subset of AI, and finally deep learning – which is a part of the subset of ML, fitting inside both or you can say – DL is driving today’s AI explosion due to more complex inputs and outputs.

I think these highly illustrative images cleared some doubts and misconceptions about these jargons. But you need to go through more definitions with a few sets of useful examples and use cases that will help you understand these concepts better.

What is Artificial Intelligence?

As the name denotes, AI is a broader concept used to create an intelligent system that can act like human intelligence.  The terms – “Artificial” and “intelligence” means “a human-made thinking power”.

Basically, AI is the field of computer science used to incorporate human intelligence into machines, so that such machines or systems can think (not exactly) and take sensible decisions like humans. 

Also Read: Where Is Artificial Intelligence Used: Areas Where AI Can Be Used

And such AI-enabled machines can perform specific tasks very well and sometimes even better than humans – though they are limited in scope. And to develop such machines AI training data sets are processed through machine learning algorithms. 

To be more precise, AI-enabled systems don’t need to be pre-programmed, instead such algorithms are used, that can work with their own intelligence. And machine learning algorithms such as reinforcement learning algorithms and deep learning neural networks are used to create such systems.

Example of AI in Daily Life

Smart Home Devices, automated mail filters in our Gmail, Self-driving cars, Chatbots, AI Robots, Drones and AI Security Cameras are the popular examples where AI in integrated. Though, there are many more other applications, devices, systems and machines works on AI principles helping humans in various areas across the globe.

Also Read: How Can Artificial Intelligence Benefit Humans

What is Machine Learning?

As the name suggests, machine learning empowers the computer system to learn from past experiences earned through training data. As of now, you got to know machine learning is the subset of artificial intelligence, in fact, it is the technique used to develop AI-enabled models.

What is Machine Learning

Machine Learning is used to create various types of AI models that learn by themselves. And as much as it gets more data, it gets better at learning and gives more accurate results.

Let’s take an example of how machine learning and algorithms work while making predictions. ML is actually a process of training the algorithms to learn and make the decisions as per the learning.

While training an ML-based model, we need certain machine learning training data sets to feed into the algorithm allowing it to learn more about the processed information.        

Today, machine learning is being utilized in a wide range of sectors including healthcare, agriculture, retail, automotive, finance and so many more.

Machine Learning Examples in Real Life

Recommendation on your Mobile or Desktop based on your web search history, Virtual Assistance, Face & Speech Recognition, Tag or Face Suggestion on Social Media Platforms, Fraud Detection, Spam Email Filtering, are the major examples of machine learning in our daily life. Most of the AI devices are developed through machine learning training.    

Machine learning is most prevalent in the development of fraud detection software. It has made the job of fraud analysts more efficient because it allows them to devote their time to more strategic tasks. If you want to learn more about the companies that offer Fraud detection software in your country, do a quick Google search for ‘Internal Fraud Prevention in New York‘ or whatever geographic region you are in.

What is Deep Learning?

It is the subset of machine learning that allows computers to solve more complex problems to get more accurate results by far out of any type of machine learning. 

Deep learning uses the Neural Network to learn, understand, interpret and solve crucial problems with a higher level of accuracy.

What is Deep Learning

DL algorithm-based neural networks are roughly inspired by the information processing patterns that are mainly found in the human brain. 

While learning, understanding, and predicting just like we use our brains to recognize and understand certain patterns to classify various types of information, deep learning algorithms are mainly used to train machines for performing such crucial tasks easily.

Whenever we try to perceive new information, the brain tries to compare it with the items known to the brain before making sense of it. In deep learning – neural network algorithms employ to perceive new information and give results accordingly.

Actually, the brain usually tries to decode the information it receives and archives this through classification and assigning the items into various categories.

Let’s take an example – As we know DL uses a neural network which is a type of algorithms aiming to emulate the way human brains make decisions.

The notable difference between machine learning and deep learning is that the later can help you to understand the subtle differences. Because DL can automatically determine the features to be used for classification, while ML needs to make understandable these features manually. 

Finally, the point is compared to ML, DL requires high-end machines and a substantially huge amount of deep learning training data to give more accurate results.

Deep Learning Examples in Real Life

Automated Translation, Customers Shopping Experience, Language Recognition, Autonomous Vehicles, Sentiment Analysis, Automatic Image Caption Generation & Medical Imaging Analysis are the leading examples of deep learning in our daily life.           

Summing-up 

Machine learning is already being used in various areas, sectors, and systems but deep learning is more indispensable for the healthcare sector where the accuracy of results can save the lives of humans. Though, countless opportunities lie for machine learning and deep learning to make the machines more intelligent and contribute to developing a feasible AI model.

In the healthcare and medical field, AI can diagnosis disease using the medical imaging data that are fed into deep learning algorithms to learn the tumors or other life-threatening diseases. Now deep learning is giving excellent results, even performing better than radiologists

Finally, in all types of AI, ML or DL models working on computer vision-based technology needs a huge amount of training data for object detection. These datasets help them to learn the patterns and utilize similar information for predicting the results when used in real-life.

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Artificial Intelligence in Robotics: How AI is Used in Robotics?

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AI in Robotics

Robots were the first-known automated type machines people got to know. There was a time when robots were developed for performing specific tasks, yes such machines were earlier developed without any artificial intelligence (AI) to perform only repetitive tasks.

But now the scenarios are different, AI in getting integrated into robots to develop the advanced level of robotics that can perform multiple tasks, and also learn new things with a better perception of the environment. AI in robotics helps robots perform the crucial tasks with a human-like vision to detect or recognize the various objects.            

Nowadays, robots are developed through machine learning training. A huge amount of datasets is used to train the computer vision model, so that robotics can recognize the various objects and carry out the actions accordingly with right results.       

And, further, day-by-day, with more quality and precise machine learning processes, robotics performance is getting improved. So, right here we are discussing the machine learning in robotics and types of datasets used to train the AI model developed for robots.

How AI is Used in Robotics?

The AI in robotics not only helps to learn the model to perform certain tasks but also makes machines more intelligent to act in different scenarios. There are various functions integrated into robots like computer vision, motion control, grasping the objects, and training data to understand physical and logistical data patterns and act accordingly.    

And to understand the scenarios or recognize the various objects, labeled training data is used to train the AI model through machine learning algorithms. Here, image annotation plays a key role in creating a huge amount of datasets helping the robotics to recognize and grasp different types of objects or perform the desired action in the right manner making AI successful in the robotics.     

Application of Sensors in Robotics

The sensor helps the robots to sense the surroundings or perceive the visuals of the environment. Just like five key sensors of human beings, combinations of various sensing technologies are used in the robotics. From motion sensors to computer vision for object detection, there are multiple sensors providing a sensing technology into changing and uncontrolled environments making the AI possible in the robotics. 

Uses of Types of Sensors in Robotics:

  • Time-of-flight (ToF) Optical Sensors
  • Temperature and Humidity Sensors
  • Ultrasonic Sensors
  • Vibration Sensors
  • Millimeter-wave Sensors

Nowadays a wide range of increasingly more sophisticated and accurate similar sensors, combined with systems that can fuse all of this sensor data together is empowering robots to have increasingly good perception and awareness for the right actions in real-life.  

Application of Machine Learning in Robotics

Basically, machine learning is the process of training an AI model to make it intelligent enough to perform specific tasks or some varied actions. And to feed the ML algorithms, a set of data is used at a large scale to make sure AI models like robotics can perform precisely. As much as training data will be used to train the model, the accuracy would be at the best level. 

In robotics, it is trained to recognize the objects, with the capability to grasp or hold the same object and ability to move from one location to another location. Machine learning mainly helps to recognize the wide-ranging objects visible in different shapes, sizes and various scenarios.

Also Read: Where Is Artificial Intelligence Used: Areas Where AI Can Be Used

And the machine learning process keeping running if robots detect new objects, it can make the new category to detect such objects if visible again in the near future. However, there are different disciplines of teaching a robot through machine learning. And deep learning is also used to train such models with high-quality training data for a more precise machine learning process.  

APPLICATION OF AI IN ROBOTICS

AI in robotics makes such machines more efficient with self-learning ability to recognize the new objects. However, currently, robotics are used at the industrial purpose and in various other fields to perform the various actions with the desired accuracy at higher efficiency, and better than humans.

Video: Most Advance AI Robots

From handling the carton boxes at warehouses, robotics is performing the unbelievable actions making certain tasks easier. Right here we will discuss the application of AI robotics in various fields with types of training data used to train such AI models.    

Robotics in Healthcare

Robotics in healthcare are now playing a big role in providing an automated solution to medicine and other divisions in the industry. AI companies are now using big data and other useful data from the healthcare industry to train robots for different purposes.

AI Robotics in Healthcare

Also Read: How AI Robotics is Used in Healthcare: Types of Medical Robotics

From medical supplies, to sanitization, disinfection and performing the remote surgeries, AI in robotics making such machines become more intelligent learned from the data and performs various crucial tasks without the help of humans.

Robotics in Agriculture

AI Robotics in Agriculture

In the agriculture sector, automation is helping farmers to improve crop yield and boost productivity. And robotics is playing a big role in the cultivation and harvesting the crops with precise detection of plants, vegetables, fruits, and other unwanted floras. In agriculture AI robots can perform the fruits or vegetable plucking, spraying the pesticides, and monitor the health conditions of plants.

Also Read: How AI Can Help In Agriculture: Five Applications and Use Cases

Robotics in Automotive

AI in Robotics in Automotive

The automobile industry moved to the automation that leads to fully-automated assembly lines to assemble the vehicles. Except for a few important tasks, there are many processes performed by robotics to develop cars reducing the cost of manufacturing. Usually, robotics is specially trained to perform certain actions with better accuracy and efficiency.

Robotics at Warehouses

AI Robotics at Warehouses

Warehouse needs manpower to manage the huge amount of inventory kept by mainly eCommerce companies to deliver the products to their customers or move from location to another location. Robotics is trained to handle such inventories with the capability to carefully carry from one place to another place reducing the human workforce in performing such repetitive tasks.

Robotics at Supply Chain

AI Robotics at Supply Chain

Just like inventory handling at warehouses, Robotics at logistics and supply chain plays a crucial role in moving the items transported by the logistic companies. AI model for robotics gets trained through computer vision technology to detect various objects. Such robotics can pick the boxes and kept at the desired place or load and unload the same from the vehicle at faster speed with accuracy.

Training Data for Robotics    

As you already know a huge amount of training data is required to develop such robots. And such data contains the images of annotated objects that help machine learning algorithms learn and recognize the similar objects when visible in the real-life.

Also Read: Top 5 Applications of Image Annotation in Machine Learning & AI

And to generate a huge amount of such training data, image annotation techniques are used to annotate the different objects to make them recognizable to machines. And Anolytics provides the one-stop data annotation solution to AI companies to render high-quality training data sets for machine learning-based model development.      

Also Read: What Is The Use And Purpose Of Video Annotation In Deep Learning

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Artificial Intelligence in High-Quality Embryo Selection for IVF

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artificial intelligence embryo selection IVF

IVF treatment is becoming a common practice in today’s reality, where 12% of the world population struggle to conceive naturally. But thanks to artificial intelligence in IVF, the whole process is going to help the embryologists to select the best quality embryos for in-vitro fertilization improving the success of conception through artificial insemination.

As per the latest study published in eLife, a deep learning system was able to choose the most high-quality embryos for IVF with 90% accuracy. Compared to trained embryologists, the deep learning model performed with an accuracy of approximately 75% while the embryologists performed with an average accuracy of 67%.

As per the research stated, the average success rate of IVF is 30 percent. The treatment is also expensive, costing patients over $10,000 for each IVF cycle with many patients requiring multiple cycles in order to achieve successful pregnancy.

Risk Factors in IVF Treatment

While multiple factors determine the success of IVF cycles, the challenge of non-invasive selection of the highest available quality embryos from a patient remains one of the most important factors in achieving successful IVF outcomes.

artificial intelligence in ivf

Currently, tools available to embryologists are limited and expensive, leaving most embryologists to rely on their observational skills and expertise. As selection of quality embryo increases the pregnancy rates, that is now possible with AI.

Also Read: How Artificial Intelligence Can Predict Health Risk of Pregnancy

Researchers from Brigham and Women’s Hospital and Massachusetts General Hospital (MGH) set out to develop an assistive tool that can evaluate images captured using microscopes traditionally available at fertility centers.

artificial intelligence embryo selection

There is so much at stake for our patients with each IVF cycle. Embryologists make dozens of critical decisions that impact the success of a patient cycle. With assistance from our AI system, embryologists will be able to select the embryo that will result in a successful pregnancy better than ever before,” said co-lead author Charles Bormann, PhD, MGH IVF Laboratory director.

AI in Embryo Selection through Machine Learning

The team trained the deep learning system (sub branch of machine learning) using images of embryos captured at 113 hours post-insemination. Among 742 embryos, the AI system was 90% accurate in choosing the most high-quality embryos.

ivf machine learning
AIVF’s deep learning and computer vision algorithms applied to time-lapse videos and stills of embryo development with proprietary markers and identifiers. Image Credit

The investigators further assessed the system’s ability to distinguish among high-quality embryos with the normal number of human chromosomes and compared the system’s performance to that of trained embryologists help in healthy baby growth in the womb.

Also Read:  What Causes A Baby To Stop Growing In The Womb During Pregnancy

The results showed that the system was able to differentiate and identify embryos with the highest potential for success significantly better than 15 experienced embryologists from five different fertility centers across the US.

However, the deep learning system is meant to act only as an assistive tool for embryologists to make judgments during embryo selection but going to benefit clinical embryologists and patients. Actually, a major challenge in the field is deciding on the embryos that need to be transferred during IVF and such AI models can make right decisions. 

Machine Learning Training Data for AI Model

The research stated that deep learning model has potential to outperform human clinicians, if algorithms are trained with more qualitative healthcare training datasets. Advances in AI have promoted numerous applications that have the potential to improve standard-of-care in the different fields of medicine.

Though, few other groups use to evaluate different use cases for machine learning in assisted reproductive medicine, this approach is novel in how it used a deep learning system trained on a large dataset to make predictions based on static images.

Such findings could help the couples become parents through IVF with higher chances of conceptions with right embryos selections. And further with more improvement in training development of AI systems will be used in aiding embryologists to select the embryo with the highest implantation potential, especially amongst high-quality embryos.

Watch Video:  Future of AI in Embryo Selection for IVF

Source: Health Analytics

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