AI in healthcare is becoming more crucial with early detection of various diseases with better accuracy. Cancer is one the widespread deadly disease can be now detected through machine learning and AI-enabled automated machines.
Breast cancer is most common among women worldwide. However, more than 90% of women diagnosed with breast cancer at the earliest stage survive their disease for at least 5 years compared to around 15% for women diagnosed with the most advanced stage of the disease, which is now possible with AI.
Though, AI is already diagnosing breast cancer but owing to accuracy, the reliability among the doctors was less. But now Google has developed an AI system that can detect the early signs of breast cancer better than radiologists.
Research of Google Breast Cancer AI
In the research, the Google’s AI model called DeepMind AI was trained and tuned on a representative data set comprised of de-identified mammograms from more than 76,000 women in the U.K. and more than 15,000 women in the U.S.
The AI model is trained to see if it could learn to spot signs of breast cancer in the scans. The model was then evaluated on a separate de-identified data set of more than 25,000 women in the U.K. and over 3,000 women in the United States.
In this study, researchers conducted a test where Google’s AI system was pitted against six radiologists where the AI managed to outperform all six of them at accurately detecting breast cancer among these women at better accuracy.
Earlier, when a similar study is conducted in which the team first trained AI to scan X-ray images, then looked for signs of breast cancer by identifying changes in the breasts of the 28,000 women. They then checked the computer’s guesses against the women’s’ actual medical outcomes, reducing the reliability of such applications.
But now, the accuracy level has improved and reduced false negatives by 9.4% and cut down false positives by 5.7% for women in the US. In the UK, where two radiologists typically double-check the results, the model cut down false negatives by 2.7% and reduced false positives by 1.2% make AI more reliable.
How Does AI Detect Breast Cancer?
Breast cancer diagnosis is done by oncologist’s human knowledge and intuition of what major risk factors might be, such as age, family history of breast and ovarian cancer, hormonal and reproductive factors, and breast density.
While AI in breast cancer diagnosis, rather than manually identifying the patterns in a mammogram that drives future cancer, the MIT/MGH team trained a deep-learning model to deduce the patterns directly from the medical imaging data.
Video: How AI Model Improves Breast Cancer Detection on Mammograms
And using the information from more than 90,000 mammograms, the model detected patterns too subtle for the human eye to detect the cancer cells. The Google breast cancer AI algorithms are used to learn such patterns and predict.
You can check below an image showing the visualization of tumor growth and metastatic spread in breast cancer with screening to detect breast cancer early, before symptoms develop.
Actually, the demographics of the population studied by the authors are not well defined in the previous AI-based detection. As the performance of AI algorithms can be highly dependent on the population used in the training data sets.
And these training data sets are created by annotating the medical images of breast infected with cancer available in various formats like X-rays, CT Scan and MRI. A huge amount of such labeled data is used to train the AI algorithms.
There are many companies providing healthcare training data with annotated medical imaging to train the AI and ML models with accuracy.
And with the availability of such data, detection of various types of other common cancer through AI will become possible benefiting the humans saving their life from such maladies.
Also Read: How Can Artificial Intelligence Benefit Humans?
What Is The Use And Purpose Of Video Annotation In Deep Learning?
Just like image annotation, video annotation also helps machines to recognize the objects through computer vision. Basically, the main motive of video annotation is detecting the moving objects in the videos and makes it recognizable with frame-to-frame outlining of objects to train the AI models developed with deep learning.
Use of Video Annotation
Apart from, detecting and recognizing the objects, which are also possible through image annotation, there are various reasons video annotation is used in creating the training data set for visual perception based AI models observe varied objects.
Actually, these models get trained through an algorithm to perceive the various types of objects through video annotation service. So, right here, apart from object detection, we will explain what is the use and purpose of video annotation in deep learning.
Frame-by-Frame Objects Detection
The first and most use and purpose of video annotation is capturing the object of interest frame-by-frame and making it recognizable to machines. The moving objects run on the screen annotated using the special tool for precise detection through machine learning algorithms used to train the visual perception based AI models.
Object Localization for Computer Vision
Another use of video annotation is localizing the objects in the video. Actually, there are multiple objects visible in a video and localization helps to locate the main object in an image, means the object mostly visible and focused in the frame. Actually, the main task of object localization is to predict the object in an image with its boundaries.
Object Tracking for Autonomous Vehicle
Another important use of video annotation is help visual perception AI model build for autonomous vehicle is after detecting and recognizing the objects track the varied category of objects like pedestrians, street lights, sign boards, traffic lanes, signals, cyclists and vehicles moving on the road while self-driving cars is running on the street.
Tracking the Human Activity and Poses
Another significant purpose of video annotation is again to train the computer vision based AI or machine learning model track the human activities and estimate the poses. This is mainly done in sports fields to track the actions athletes perform during the competitions and sports events helping machines to estimates the human poses.
These are various use of video annotation, and all these are done for the computer vision to train the visual perception based model through machine learning algorithms. In self-driving cars and autonomous flying drones, video annotation is mainly used to train the model for precise detection, recognition and localization of varied objects.
There are many video annotation companies providing the data labeling service for AI and machine learning. If you need a video annotation for deep learning, you can get in touch with Anolytics, that offers a world-class video annotation service to annotate the object of interest with frame-by-frame annotation at best level of accuracy.
How AI Can Predict Coronavirus like Epidemic Before it Outbreaks?
AI in healthcare is already developed enough to diagnosis various types of critical diseases, but in case of the epidemic it failed and not able to predict timely, that took the life of many people across the world and still spreading further became a health emergency.
I’m talking about “Coronavirus Infection” – that started in mid-December in China and now transmitted to all major countries worldwide. This high contagious infection took lives of more than 800 people and infected over 37000 people globally.
The question arises here, why artificial intelligence has not been used to detect the risk associated with this kind of disease or AI is unable to detect such epidemic with right predictions, so that medical experts can envisage the situation timely.
Artificial Intelligence Coronavirus Prediction
A Canadian based global health monitoring platform – BlueDot, reportedly notified its clients of the outbreak of coronavirus on Dec. 31. But nobody has taken AI prediction seriously and now the situation became out of control in China.
BlueDot is the mastermind of Kamran Khan, who is an infectious disease physician and professor of Medicine and Public Health at the University of Toronto. Keep in mind that he was a frontline healthcare worker during the SARS outbreak.
How BluDot’s works in Epidemic Prediction?
BlueDot’s algorithm uses machine learning (ML) and natural language processing (NLP) technology to detect signs of potential disease outbreaks from the collected information that becomes a training data while developing such AI models.
Video: How BluseDot AI Predicted Coronavirus?
And such AI’s findings are reviewed and verified by human epidemiologists before sending a report to the company’s clients in government, industry and public health, as well as other public health officials, airlines and hospitals in the affected regions.
Data Used in AI Coronavirus Prediction
In the case of the coronavirus outbreak, the algorithm reportedly used airline ticketing information and pick news of such outbreaks like murmuring or forums or blogs or indications of some kind of unusual events going on to accurately predict the virus’ rapid spread from Wuhan, China, to Bangkok, Seoul, Taipei, Tokyo and other nations.
Here, in coronavirus prediction, BlueDot uses an AI-driven algorithm that scours foreign-language news reports, animal and plant disease networks, and official proclamations to give its clients advance warning to avoid danger zones like Wuhan.
Another AI PredictsCoronavirus Could Kill 53 million and infect 2.5 billion
Yes, as per an article published on Forbes, AI predicts coronavirus could infect 2.5 billionand kill 53 million. But doctorssaid that it is not credible.
Actually, since the coronavirus infection transmission started more than 30,000 people infected and died around 600. But conditions of infection are changing, which in turn changes incredibly important factors that the AI isn’t aware of.
To predict this epidemic along with infection and death data, AI neural net using a recurrent neural network (RNN) model and ran the simulation ten million times. That output dictated the forecast for the following day. Once the following day’s output was published, added it to the training data, and re-ran ten million times, the results are shocking.
From 50,000 infections and 1,000 deaths after a week to 208,000 infections and almost 4,400 deaths after two weeks, the numbers keep growing as each infected person infects others in turn. And in 30 days, the AI model says, two million could die and in just 15 more days, the death toll skyrockets enough to eliminate humans in millions.
Artificial Intelligence in Medical Epidemiology Prediction
As per the report AI in medical epidemiology predicted dengue with more than 80 percent accuracy in Malaysia. AI in medical epidemiology predicted the outbreaks of dengue in Penang, Malaysia for 37 locations while the actual outbreak was 30, accounting more than 80% accuracy in prediction, making AI reliable in such epidemic prediction.
Though, scientists are developing ways to use AI to predict the spread of such contagious diseases before they happen. Though, the process is extremely complicated, successful implementation of predictive modeling could represent a major leap forward in the fight to rid the world of some of the most insidious infectious diseases.
How AI Can be Improved to Predict Coronavirus Like Epidemic?
However, as per multiple doctors and medical professionals, there is good news, the model doesn’t know every factor, as conditions and data fed into the neural network are changing and these conditions change, the results will also change.
However, in coronavirus like epidemic AI could predict the number of potential new cases by area and which types of populations will be at risk the most. This type of technology could be used to warn travelers so that vulnerable populations can wear proper medical masks while traveling or take other precautions to prevent such infections.
Video: How AI Can Help to Control Coronavirus or Other Deadly Diseases?
Earlier researchers have built AI-based models that can predict outbreaks of the Zika virus in real time, which can inform how doctors respond to potential crises. AI could also be used to guide how public health officials distribute resources during a crisis. That will effectively work like AI stands to be a new first line of defense against such diseases.
AI in healthcare is already playing a vital role in assisting with researching new drugs, tackling rare diseases, and detecting breast cancer. AI was even used to identify insects that spread Chagas, an incurable and potentially deadly disease that has infected an estimated 8 million people in Mexico and Central and South America.
And now AI increasing interest in using non-health data like social media posts helping health policymakers and drug companies understand the breadth of a health crisis. For instance, AI that can mine social media posts to track illegal opioid sales, and keep public health officials informed about these controlled substances spread.
The Uncertainty Factor While Relying on AI
One of the core strengths of AI is while identifying and limiting the effects of virus outbreaks is its incredibly insistent nature. The machine never tire, can sift through enormous amounts of healthcare data, and identify possible correlations and causations that humans can’t in a fast manner and if the amount of data is huge or very complex to analyze.
While on the other hand, there are limitations of AI – the ability to both identify virus outbreaks and predict how they will spread. Let’s take the best-known example comes from the neighboring field of big data analytics.
At its launch, Google Flu Trends was heralded as a great leap forward in relation to identifying and estimating the spread of the flu — until it underestimated the 2013 flu season by a whopping 140 percent and was quietly put to rest.
Data Quality is Important for AI-based Predictions
Poor data quality was identified as one of the main reasons Google Flu Trends failed. Unreliable or faulty data can create confusion on AI-based prediction. In our increasingly interconnected world, tracking the movements of potentially infected individuals (by car, trains, buses, or planes) is just one vector surrounded by a lot of uncertainty.
But, BlueDot was able to correctly identify the coronavirus, in part dueto its AI technology, illustrates that smart computer systems canbe incredibly useful in helping us navigate these uncertainties.
And most importantly, it is not the same as AI being at a point where it precisely does so on its own – and that is the reason BlueDot employs only human experts to validate the AI’s findings.
Nevertheless, to ensure the accuracy of AI-based predictions for such an epidemic, a quality and reliable source of training data is necessary for supervised machine learning.
Hospitals, medical centers and healthcare organizations need to share the labeled medical images of such infected people to AI developers, so that they utilize the same and help the medical science and AI engineers develop a reliable AI model.
So that symptoms could be identified by the doctors and annotated to make it recognizable to computer vision through machine learning algorithms. In this case, the radiologist described Novel Coronavirus (2019-nCoV) Pneumonia through CT Imaging.
And when a huge amount of such CT scan images are manually annotated by experienced radiologists, it is used as a training data for machine learning AI models, that can in future detect such infections if similar symptoms are visible among the people. And as much as data used in the algorithm training, the accuracy of prediction by the model would be high.
How AI Based Drone Works: Artificial Intelligence Drone Use Cases
Autonomous flying machines or drones use the computer vision technology to hover in the air avoiding the objects to keep moving on the right path. And now artificial intelligence (AI) is used in drones to make this flying machine smarter.
From security surveillance to aerial view monitoring, AI drone is now used by online retail giant Amazon to deliver the products at customer’s doorstep revolutionizing the transportation and delivery system by logistics and supply chain companies.
Computer Vision in Drone Technology
Computer vision is playing a key role in detecting the various types of objects while flying in midair. A high-performance onboard image processing and a drone neural network are used for object detection, classification, and tracking while flying into the air.
The neural network in drones helps to detect the various types of objects like vehicles, foothills, buildings, trees, objects on or near the surface of the water, as well as diverse terrain.
Computer vision also helps detect livening beings like humans, whales, ground animals and other marine mammals with a high level of accuracy.
How Drone Technology Works?
A self-flying drone is built with various in-built with computerized programming and using the technology like propulsion and navigation systems, GPS, sensors and cameras, programmable controllers as well as equipment for automated flights.
Video: Drone Detecting Object Through Computer Vision
Drone used to capture the data using the camera and sensors, which is later analyzed to extract useful information to utilize for a specific purpose.
This process is known as computer vision and related to the automatic extraction, analysis and understanding of meaningful information through one or more images processed through computer vision technology.
Machine Learning & Deep Learning for Computer Vision in Drones
Computer vision now backed with machine learning and deep learning algorithms is making a drastic change in the drone industry. It helps algorithms to learn from captured images of various objects that come while using the drones for various purposes.
The objects are annotated to make it recognizable to drones through computer vision. And a wide variety of entities are labeled to make sure drone can detect and decide its direction and control to fly safely avoiding the obstacles in the path.
Computer Vision in Drones have mainly three Applications:
- Object tracking
- Obstacle detection and collision avoidance technologies
Computer vision in drones helps to track the objects while working for self-navigation and detect the obstacles to avoid a collision from such objects.
While object tracking drone captures the real-time data during the flight, processes it with an on-board intelligence system in real-time, and makes a human-independent decision based on the processed data.
While on the other hand, in self-navigation drones get pre-defined GPS coordinates about departure and destination points, with the capability to find the most optimal way and get there without manual control thanks to AI-enabled computer vision advances.
Similarly, GPS navigation is not enough to solve the problem of collision avoidance. Resulting, drones or autonomous flying objects crash into trees, buildings, high-rise poles, drones and various similar varied types of unlimited objects lying or standing in the natural environment.
Here, the drone needs to be trained with a huge amount of data sets to make it learn and detect a wide variety of objects and obstacles, both static and in motion, and avoid them when moving at a high speed.
And it is possible when right image annotation companies ensure providing the precisely annotated data to train the AI model for autonomous flying.
AI-enabled Commercial Drone Use Cases
Drone in Smart Cities & Urban Management
Smart cities are developed with most advanced features like well-connected home and equipment systems with control on the user’s palm.
Most of the infrastructural amenities are either fully or partially automated with advance security surveillance used in AI-based cameras for quick facial recognition and trace the unwanted objects.
Video: AI Drone in Smart Cities
And in urban management drones help to get the Aerial view mapping of urban houses and the urban landscape design allowing civil engineers or architecture to make the most feasible plan layout.
Moreover, it also plays a crucial role in traffic monitoring and defines the city route for a smooth and trouble-free movement.
Video: AI Drone in Urban Management
Drone in Agriculture & High-tech Farming
AI drones in agriculture are playing a crucial role in monitoring the crops and plants health conditions. Farmers are using drones to determine the best time to plant to applying the right amount of fertilizer at just the right time.
Video: Drone in Agriculture & Farming
Drones are capable to monitor crops, assess soil, check the soil fertility and help crop protection. A drone is also planting trees by shooting the seedpods into the prepared soil helping farmers to save their cost and time allowing them to get in involved in other activities to improve the productivity of entire agricultural sector.
Drone in Real Estate & Construction Industry
As per the construction companies are going to spend more than $11.2 billion on drones between the next five years, allowing the drone industry to get developed into various fields also allowing the manufacturers to make drones.
Video: Drone at Construction Site
AI drones in construction companies can scan or map the terrain of buildings within a few minutes, that human requires several days to complete. And with a bird-eye view, for the construction vehicles during the projects, drones are providing an accurate position and information to create self-guided equipment in the near future.
Video: Drone in Real Estate
Similarly, real estate companies are using drones to get photographs of homes and commercial buildings, as well as aerial maps and local information for homebuyers to provide them a real scenario without physically visiting at the site.
AI Drone in Military and Defense Sector
In military and defense sector drones are becoming popular to develop for the unmanned weapons to combat or bombard on the enemies in the war.
However, right now drone is already used for patrolling on the borders, monitoring security, tracking storms and performing safety inspections. And many of them also used for food supplies.
Video: AI Drone in Military & Defense
Developing drones need high precision computer vision-based navigation and object detection system to work efficiently. Few drones are now capable of intelligence gathering or chemical detection with a high level of accuracy.
Human Tracking and Face Recognition
Drones are also used for tracking humans in societies or in the park from the security perspective. Its camera can be used for face recognition to detect suspicious people or track their face gesture or emotion tracking while flying.
Video: Drone in FaceRecognition
Developing drones for face recognition needs another technology that can train an AI model to detect the people using their facial attributes.
Again annotated images used as training data to create an AI model for drone help such autonomous flying machines to identify people among the huge crowd.
Drones in Security and Surveillance
Making the life of people safer and secured, drones are playing another role in the security and surveillance of people living in unsafe areas or even in habitat environments.
Drones used as a security camera for monitoring the unusual activities outside threats like theft or violence and protest to recognize the people or track their activities.
Video: Drones in Security and Surveillance
Drone applications into various fields are becoming significant and AI-based drones can provide game-changing results to get connected from distant locations providing real-time data for quick action.
However, in upcoming days, with improved computer vision technology and more efficient training of autonomous flying capability, drones will bring benefits to various crucial industries and help to solve humanity’s greatest problems.
To detect the various objects you need training data for a drone. And these training data include the annotated images of various objects that a drone can recognize through computer vision and move avoiding the obstacles into the path.
And there are many companies in image annotation service to annotate the data with an exceptional level of accuracy to make sure drones can easily detect the varied objects and can be used for various purposes benefiting the humans.
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