Role of Computer Vision in Healthcare

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.

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Bridged.co

Bridged is striving to improve the efficiency of clients in the artificial intelligence sector through the use of training data powered by human intel. Since 2018, Bridged has delivered 50M+ datasets by deploying its 13,000+ Bridged-qualified crowd-force.

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