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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.

9 ways artificial intelligence is transforming healthcare

Artificial Intelligence and Machine Learning is transforming business operations across industries. From autonomous vehicles to financial services, AI has successfully found multiple use cases across virtually every space. For this piece, let’s focus on AI’s influence on healthcare. 

The healthcare industry has a variety of use cases for AI. With its ability to assist medical professionals with diagnoses and drug research, the healthcare community has welcomed AI and ML with open arms. Doctors can now track symptoms faster and effectively, while researchers can locate vaccine raw materials with minimum manual procedures. Hospitals and medical research centers have adopted AI into the heart of their operations. Here’s how AI’s contributions are transforming healthcare:

Improved decision making

Medical professionals have the responsibility to suggest treatment alternatives to patients. With the assistance of AI, doctors can make such decisions a lot faster and more accurately. For example, doctors treating cancer patients can make use of Machine Learning algorithms that can detect cancer cells and their potential spread and impact. Using such algorithms, doctors can choose between various treatment methods available, from basic medication to extensive surgical procedures.

Healthy lifestyle management

Everyone wants to be healthy, and AI is making it easier than ever to stay so. By providing information on daily eating, sleeping, and fitness habits, AI-inspired interfaces can predict the health impact of a user’s lifestyle and suggest quick and long-term fixes.

Health assistant chatbots

Chatbots are the rage today in the customer service space. People love interacting with chatbots to solve queries and receive answers. The healthcare space is taking advantage of chatbots too. Health assistant chatbots can perform simple diagnoses for patients, and accordingly recommend whether the patient needs to visit a hospital or not. Advanced chatbots could also suggest off the shelf medication and dietary suggestions. During difficult times such as the coronavirus outbreak, people are making use of such chatbots to reduce the load on hospitals.

Health monitoring

Patients admitted to hospitals need their parameters monitored constantly. AI/ML models can study a patient’s health parameters and alarm surgeons and physicians regarding high-risk situations. For example, during child-birth, delivering mothers lose a lot of blood, and doctors can effectively measure the amount lost, and accordingly provide the required medical assistance.

Medical imaging

The healthcare community has adopted computer vision to study medical images and provide insights for physicians. In radiology, AI models can locate tumors and predict their development. Dermatology also makes use of computer vision by studying various skin disease cases and identifying the ones at hand. With such technology, dermatologists can assist patients (such as the ones suffering from eczema) with more accurate treatment options. 

Early symptom identification

With AI-inspired health monitoring equipment, doctors can identify potential threats to a patient’s health. Health conditions such as diabetes and heart disease can be addressed in advance and treated, thus eliminating the chances of a condition getting more complicated.

Epidemic spread

If an epidemic’s spread can be analyzed with high precision, populations can mitigate a virus outbreak by adopting healthy practices. For example, during this coronavirus outbreak, understanding the virus’s spread has helped people practice social distancing and regular hand-washing. Two effective ways to tackle COVID-19.

Vaccine research

The coronavirus outbreak has forced pathologists to search for suitable vaccine raw materials. Machine learning can help researchers locate protein structures and eliminate futile alternatives. Businesses across the globe are looking for ways to use AI for vaccine research and identification.

End of life care

With every decade, people’s lifespans have increased, and AI is poised to increase that even further. Conditions such as dementia and osteoporosis are common health issues faced by the elderly. AI models, coupled with a humanoid design, can interact with people suffering from such issues, to keep themselves distracted, and their minds active.

Conclusion

The healthcare field, as displayed, is filled with AI/ML use cases. New generation AI tools, and models, are helping doctors understand their patients’ conditions better, and provide advanced treatment solutions. While implementation still has a long way to go, AI in healthcare has started on the right footing; with technology that promotes quality diagnoses and maintains the importance of medical professionals.

The future of medicine is here, with AI paving its path.