The use of computer vision in healthcare can give rise to numerous applications that can prove to be life-saving for patients by improving the speed and accuracy of medical diagnoses and assisting medical professionals in critical situations.
After decades of ongoing AI research, we are finally beginning to see the emergence of artificial agents having the ability to perceive, think, or act like humans. These agents, while not being able to perfectly emulate human thought and behavior (yet), are already proving to be revolutionary through numerous applications in different industries. There are numerous examples of AI applications in construction, finance, manufacturing, and retail among others. And one such industry happens to be healthcare. The healthcare industry -- where precision is of the essence and human lives are often at stake -- offers the perfect opportunity to demonstrate the cutting edge in AI and computer vision technology, a subset of AI that gives machines the ability to analyze and interpret the contents of visual data (images and videos).
Computer vision applications, acting as an additional pair of eyes, are revolutionizing the healthcare sector by not only helping medical professionals save time on basic tasks but also in life-saving applications. Read on to know how the use of computer vision in healthcare is revolutionizing the field, making life easier for healthcare administrators, professionals, as well as patients.
The state of the art in AI research has already yielded numerous applications that can process large volumes of data to identify the subtlest of patterns that may elude the human eye. And in the field of healthcare and medicine, even minute details are of great significance and can be the difference between life and death in many situations. Following are a few ways in which the visual pattern recognition capability of AI-driven computer vision technology can make a difference:
Accuracy of diagnoses is of prime importance in the field of healthcare for two reasons. Firstly, most medical procedures, especially in countries without universal healthcare, can be expensive for patients and their families. Hence, it is necessary for patients to be absolutely certain about the diagnosis of a condition before proceeding to pay for surgery or other forms of treatment. False positives for severe, potentially terminal illnesses can not only put unnecessary financial stress on people but also subject them to great mental stress. Accurate diagnoses provided by computer vision systems minimize false positives and can potentially eliminate the need for unnecessary surgical procedures and expensive therapies. False negatives can be as troublesome as false positives and, sometimes, may even be more harmful. Computer vision algorithms trained using a large volume of training data can spot the slightest of hints suggestive of the presence of a condition which may otherwise be missed by human doctors due to their sensory limitations. Thus, the use of computer vision in healthcare diagnosis can ensure significantly high levels of accuracy which may someday go up to 100%. This may potentially save not only thousands of dollars in money but also thousands of lives every year.
Most fatal illnesses like cancer can be treated with a higher likelihood of success if they are diagnosed in their early stages. However, the limitations in most existing ways of diagnosis may make spotting these diseases difficult until it gets too late. The use of computer vision in healthcare can enable the detection of early symptoms with high certainty due to AI’s finely tuned pattern-recognition capability. This can trigger timely treatment and end up saving countless lives in the long term.
Computer vision can considerably minimize the time taken by doctors in analyzing reports and images, giving them more time to spend with patients and offer personalized and helpful advice. While improving the quality of physician-patient interactions, the use of computer vision in healthcare can also help physicians to offer consultation to more and more patients. This will be especially useful in the coming decade, as a shortage of up to 120,000 doctors is estimated by the year 2030 in the US.
Newly emerging computer vision applications are helping doctors and other medical professionals to deliver healthcare services with greater efficiency, increase the accuracy of diagnoses, and raise the survival rates of patients with terminal conditions.
Computer vision technology is being used in healthcare to analyze scan reports and identify patterns that may indicate the possibility of a medical condition. For instance, a New York-based hospital used a computer vision application that could analyze CT scans and determine the presence of neurological illnesses in 1.2 seconds on average. This is 150 times lesser than what human doctors would take for analyzing and making inferences from CT scan images for confirming the presence of such disorders. Such experiments will serve as stepping stones towards the full-scale application of AI and computer vision in healthcare for even more impactful use cases.
Computer vision and deep learning can be used to read and convert 2D scan images into interactive 3D models to enable medical professionals to gain a detailed understanding of a patient’s health condition. 2D images, even if taken multiple times from a wide range of angles, cannot give as comprehensive a view into a person’s brain or heart as an interactive 3D model can. This can enable radiologists to scrutinize scan images deeply and discover even the most subtle indications of disorders easily without spending much time on scanning.
Computer vision technology can analyze health and fitness parameters in an unintrusive manner to help people make faster and better medical decisions. For instance, computer vision is being used by hospitals to measure the blood lost during surgeries, especially during c-section procedures. All the surgeons have to do is hold a blood-stained sponge in front of the scanning device, and the system determines the volume of blood lost based on the state of the sponge. This can help in taking emergency measures if the quantity of blood lost reaches dangerous levels. Similarly, computer vision and deep learning can also be used to measure other parameters like the body fat percentage of people using just images taken from regular cameras, without any device even touching the person undergoing the test.
Computer vision, combined with natural language processing and generation (NLP and NLG) can potentially be used to generate reports from CT, X-Ray, and MRI scan images. These algorithms can be trained using samples of CT and MRI scan images along with the corresponding inferences and analysis reports generated by radiologists and doctors. With enough training data, the system can independently generate reports based on the contents of the images. This can save a lot of time for radiologists and physicians who won’t have to spend a lot of time analyzing images, gather inferences, and then scribble down or type out their findings.
The mainstream use of computer vision in healthcare applications is only beginning and its vast potential still remains unexplored. Further development of advanced computer vision-based healthcare applications can lead to even more non-intrusive testing and diagnostic tools that will not only make medical procedures easier for medical professionals but also much safer for the patients.
Naveen is the Founder and CEO of Allerin, a software solutions provider that delivers innovative and agile solutions that enable to automate, inspire and impress. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. He is currently working on Internet of Things solutions with Big Data Analytics. Naveen completed his programming qualifications in various Indian institutes.