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Applications of Computer Vision in Healthcare

Computer vision is a field that explores ways to make computers identify useful information from images and videos. Think of it as training computers to see as humans do. While this technology has numerous applications in fields such as autonomous vehicles, retail supermarkets, and agriculture, let’s focus on the ways computer vision can benefit healthcare.

In the present scenario, doctors rely on their educated perception to treat patients. Since doctors are also prone to human error, computer vision can guide them through their diagnosis, and thus increase the treatment quality and the doctor’s focus on the patient. Further, patients can have access to the best healthcare services available, all through the swiftness and accuracy of computer vision. While still in its nascent stage, computer vision has already revealed ways in which it can improve multiple aspects of medicine. Here are a few notable ones:

Swift diagnosis:

Applications of Computer Vision

Many diseases can only be treated if they are diagnosed promptly. Computer vision can identify symptoms of life-threatening diseases early on, saving valuable time during the process of diagnosis. Its ability to recognize detailed patterns can allow doctors to take action swiftly, thus saving countless lives.

A British startup, Babylon Health, has been working to improve the speed of diagnosis using computer vision. To see this goal through, they have developed a chatbot which asks health-related questions to patients, whose responses are then, in turn, sent to a doctor. To pull out useful information from patients, the chatbot employs NLP algorithms.

In another example, scientists at the New York City-based Mount Sinai have developed an artificial intelligence capable of detecting acute neurological illnesses, such as hemorrhages or strokes. Also, the system is capable of detecting a problem from a CT scan in under 1.2 seconds — 150x faster than any human.

To train the deep neural network to detect neurological issues, 37,236 head CT scans were used. The institution has been using NVIDIA’s graphics processing units to improve the functioning and efficiency of their systems. 

Computer vision also allows doctors to spend less time analyzing patient data, and more time with the patients themselves, offering helpful and focused advice. This leads to improved efficiency of healthcare and can help in enabling doctors to treat more patients per year.

Health monitoring:

The human body goes through regular changes, but some of the issues it faces on the surface can, at times, represent symptoms of impending disease. These can often be overlooked through human error. With computer vision, there exists a quick way to access a variety of the patient’s health metrics. This information can help patients make faster health decisions and doctors make more well-informed diagnoses. Surgeries could also benefit from such technology.

For example, let’s consider the case of childbirth, based on the findings of the Orlando Health Winnie Palmer Hospital for Women and Babies. The institute has developed an artificial intelligence tool that employs computer vision to measure the amount of blood women lose during childbirth. Since its usage, they have observed that doctors often overestimate blood loss during delivery. As a result, computer vision allows them to treat women more effectively after childbirth.

There are also efforts such as AiCure, another New York-based startup that uses computer vision to track whether patients undergoing clinical trials are adhering to their prescribed medication using facial recognition technology. The goal behind this project is to reduce the number of people who drop out of clinical trials, aka attrition. This can lead to a better understanding of how medical care affects patients, and why.

Computer vision, paired with deep learning, can also be used to read two-dimensional scans and convert them into interactive 3D models. The models can then be viewed and analyzed by healthcare professionals to gain a more in-depth understanding of the patient’s health. Also, these models can provide more intuitive details than multiple stacked 2D images from a wide variety of angles.

Significant developments have taken place in dermatology. Computers are better than doctors at identifying potential health hazards in human skin. This allows for the early detection of skin diseases and personalized skincare options.

Further, no time is lost laboring over hand-written patient reports, since computer vision is capable of automatically drawing up accurate reports using all of the available patient data.

Precise diagnosis:

 The accuracy that computer vision provides eliminates the risk that comes with human judgment. These reliable systems can quickly detect minute irregularities that even skilled doctors could easily miss. 

When these kinds of symptoms are identified quickly, it saves patients the trouble of dealing with complicated procedures later on. Thus, it has the potential to minimize the need for complex surgical procedures and expensive medication.

One example of this would be computer vision’s use in radiology. Computer vision systems can help doctors take detailed X-rays and CT scans, with minimal opportunity for human error. These AI systems allow doctors to take advantage of the systems’ exposure to thousands of historical cases, which can be helpful in scenarios that doctors might not have come across before. The common uses of computer vision within radiology include detecting fractures and tumors.

Preemptive strategies

Computer Vision In Healthcare

Using machine learning, computer vision systems can sift through hundreds of thousands of images, learning with each scan how to better analyze and detect symptoms, possibly even before they present themselves.

This allows the medical professional to pre-emptively treat patients for symptoms of diseases they could develop in the future. Using input data from thousands of different sources, these AI systems can learn what leads to disease in the first place.

Present barriers

While computer vision is a revolutionary technology that will likely change healthcare as it is known today, there are some notable problems associated with the technology.

Firstly, interoperability. The computer vision AI from one region or hospital may not necessarily yield accurate or reliable results for patients outside of its sample data set. Of course, the machine learns with time, but overcoming this barrier could lead to faster adoption of this ground-breaking technology.

Also, there are privacy concerns around the digitization of patient medical data and its provision to artificial intelligence systems. This data vault needs to be stored in secure storage which can be easily accessed by the system, to avoid users with malicious intent.

And these systems aren’t perfect. Even the smallest margin of error cannot be tolerated in this space, because the consequences for wrong diagnoses are very real. These are human lives being dealt with, and the artificial intelligence systems aren’t responsible for providing treatment, only suggesting it. 

Also, there may be cases where the healthcare provider comes up with a diagnosis that conflicts with the computer vision system, leaving patients with a tough decision to make, and the doctors with all the responsibility.

Conclusion:

When computer vision is employed effectively in healthcare, it truly holds the potential to improve diagnoses and the standard of healthcare worldwide. This makes sense because doctors rely on images, scans, patient symptoms, and reports to make health-related decisions for their patients. The sheer abundance of use cases employed by computer vision systems make their analysis accurate. Thus, it allows doctors to make these crucial decisions with confidence.

Computer vision systems also allow for quality-of-life improvements, such as less time spent drafting reports, analyzing scans and procuring data. These systems could even be deployed remotely, enabling patients to receive professional medical attention from areas that don’t have easy access to healthcare services. All this lets doctors spend more time with patients, which is what healthcare should be about.

Computer Vision Advances and Challenges

Computer Vision is a field of computer science using the technology of artificial intelligence. A part of robotics as artificial visual systems automatically processes images and videos. AI training lets the computers understand, identify, classify and interpret the digital images. Response from the machines to the images relies on the understanding of computer vision. The purpose of this technology is to automate the tasks consisting of human visual aspects.

Machines obtain information from images with computer vision technology. The input data processed by the vision sensor enables it to perform actions using high-level information. Machines can gain an understanding of the situations. AI uses pattern recognition and machine learning techniques that ease decision-making.
Computer Vision technology is now accessible and affordable for industries to adopt changes and extract benefits.

History:

Experimentation on computer vision began in the1950s and by 1970s; it could distinguish handwritten and typed text with optical character recognition. In 1966, a summer vision project to build a system that can analyze the scene and identify objects commenced at MIT. Initially, the project looked simple but to be decoded. The computer vision market is all set to reach a valuation of $48.32 billion by 2023. The estimation of the computer vision AI market, in 2019 for the healthcare industry is about $1.6billion.

Reason for popularity:

  1. Creation of a huge amount of visual data
  2. Improvement in mobile technology and computing power add to image data
  3. Its ability to process massive datasets
  4. Recognizing visual inputs faster than humans
  5. Accurate interpretation of images and videos
  6. Quick processing and high demand in robots across industries
  7. Defect detection assists corrective actions
  8. Analyze images on different parameters
  9. Maintain quality and safety
  10. Increases reliability and accuracy
  11. AI Training for computer vision
  12. New hardware and algorithms brought precision
  13. Cost-effective technology compared to other systems prevailing
  14. Automation, quality control, scrutiny is introduced
  15. Eases complicated industrial tasks
  16. Rise in online analysis of images
  17. Industries that widely use computer vision are automotive, aerospace, defense, education, healthcare, pharmaceuticals, food and packaging, beverages, manufacturing, government applications, etc.
Computer Vision

How does it work?

Machines understand process and analyze images with the information it can access on the topic. With the neural networks, the iterative learning process can be set. If you are looking forward to identifying the forest area all over the globe, the datasets used by neural networks require images and videos of green patches and dry patches. Tagged images and metadata helps the machine to reply correctly. Different pieces of image are recognized using pattern recognition by the neural networks.

Mainly the system uses various components of the machine vision system such as lens, image sensors, lighting, vision processing, and communication devices. Computers assemble visual images in bits like a puzzle put together. The pieces assembled into an image makes filtering and processing speedy. In the above example of identifying forests, the machines are not trained to see different tree types and leaves instead they are trained to recognize the green patches on earth. The training lets it create an image of the forest and match it with the data.

Deep Learning learns from large amounts of data and its algorithms are inspired by a human brain to result most accurately. This subset of machine learning can identify objects, people, tag friends, translate photos, translate voice, and translates text in multiple languages. Deep learning has transformed computer vision with its high level of accuracy that is beyond human capacity.

Difference between Computer Vision and Machine Learning:

Machine learning helps the computer to understand what they see and computer vision determines how they see. Machine learning is where the systems teach themselves based on the continuously populating data. CV requires artificial intelligence to train the system in performing varied tasks. CV does not learn from the training data available but makes data patterns to find relations between data and understand it for a visual representation of a preset result.
Computer vision is progressing towards replacing human vision that assists in complicated tasks. This requires intelligent algorithms and robust systems.

Examples of Computer Vision Applications:

Applications of Computer Vision

Augmented Reality:

  1. Geo Travel: Augmented Reality Geo Travel can be your travel guide, GPS enabled application gives you information on your exact location. Plan a trip for you using your searched data on the city with the result of Wikipedia pages that you can save for easy travel. Find a car with a car finder that saves your parking position for you to get back to your car easily.
  2. Web: The Augmented Web combines HTML5, Web Audio, WebGL, and WebRTC to improve the user experience when they visit existing pages. Image search, Google photos use face recognition, object recognition, scene recognition, geolocalization, Facebook takes care of image captioning, Google maps use aerial imaging and YouTube does content categorization with help of computer vision.

Automotive: In this field can save millions of people from tragic traffic accidents. Human error is possible due to multitasking, overthinking, tension and negligence. Self-driving cars are loaded with multiple cameras, radar, ultrasonic sensors and technology that detect 360-degree movement, developed by Google Labs. Tesla car warns drivers to take control of the steering wheel. The error proofing, presence, and absence of objects, responsible control on the machine all is possible with computer vision. Technology takes control by detecting objects, marks lanes, catches signs and understands traffic signals for us to drive safely.

Agriculture: Computer vision can check the quality of grain, identify weeds, and take actions to save crops by sprinkling herbicides on weeds using AI technology. It helps in the packaging of agricultural produce and products.

Healthcare and Medical Imaging: This technology helps healthcare professionals inaccurate presentation of data, reports, and illness-related information. It can save patients from getting improper treatments, study their medical data, which is image-based such as X-Rays, CT scans, sonography, mammography, and other monitoring activities of patients. Augmented Reality assisted surgery ensures better results than surgeries with human surveillance.

Get assistance in surgery from the analysis of various images with computer vision technology. Gauss Surgical is a blood monitoring solution that closely watches blood loss in real-time. It can save patients’ life during critical operations, facilitate blood transfusions, and make out hemorrhage. The images captured with help of iPad or Triton, processed by cloud-based computer vision and it estimates blood loss through intelligent machine learning algorithms. Computer vision can improve diagnosis ad automate pathology.

Smartphones: These handy tools for perfect pictures and AI are transforming the arena of development in computer vision. It scans QR codes, has portrait and panorama modes of photography. The face and smile detection, anti-blur technology is computer vision.

Insurance: It will compare the images of patients, reports and insurance forms to settle claims of hospitalization. In case of car or property insurance, this technology can analyze the damage, inspect the property and process claims. Automation in the insurance sector can result in speedy resolution of queries and settlements.

Manufacturing: Computer vision can predict the equipment maintenance, quality issues of product, monitor the production line and product quality to reduce the defects in manufacturing.

Google Translate App: Need to learn a foreign language just to travel for pleasure and leisure is eliminated with the introduction of computer vision. Pointing to a text or sign translates the foreign language in the selected output language. The accurate recognition of any sign is possible due to optical character recognition and augmented reality for exact translation.

Challenges of Computer Vision:

Challenges of Computer Vision
  1. The human visual system is too good to be simulated. The capacity of the human eye and brain in coordination with each other can recognize things, people and places are better. Computer systems can fail to recognize the faces with a variety of expressions or variant lighting.
  2. Initial research for industry-specific tasks can be expensive. The technology is changing rapidly but the complexities of integrating computer vision systems are a higher-level challenge.
  3. Face recognition is an annoyance and breach of privacy and business ethics in the hospitality, finance and banking industry. Multiple and adverse uses of technology are a threat and San Francisco has banned facial recognition.
    The algorithms for each talk about a particular industry may not be accurate or updated and the results may not match the preordained results.
  4. The misuse of computer vision is the result of faulty inputs or intentionally tampered images to form flawed patterns that harm the learning models.
  5. Object classification is challenging as the label is assigned to the entire image for classification. Handwritten documents are difficult for computer vision, due to a variety of handwriting styles, curves and shapes formed while writing for each alphabet.
  6. Object Detection is more complicated than image classification as there can be multiple objects in an image and the request can be for single objects or combinations.

Insufficient visual data sets or image reconstruction used to fill in for the missing parts of the image damages or corrupts the versions of photos.

Supposition:

Computer vision technology of Artificial Intelligence (AI) is witnessing a global rise in market revenues from $1700 million in 2015 to $5500 in 2019.

Image processing a subset of computer vision that performs to imitate the human vision and goes beyond human accuracy. It can enhance images by processing and making them identifiable for future use. Defect-free manufacturing, automotive, pharmaceuticals, overall many industries, products, and services is achievable. Increased adoption of computer vision AI-based technology is facilitating market growth.

The future of computer vision is accelerating and the image, photo and video data are growing enormously. The data upload, download and access are opening new opportunities for computer vision-based solutions.

Scope to improve performance and create a better user experience is a source of innovation towards the problem-solving capabilities of systems. The food industry will demonstrate the highest growth rate by applying computer vision technology in manufacturing and packaging operations.

The relationship of images and users is changing and the equation of visual data and its processing is harmonizing.