Computer Vision: The Doctor’s Eye of Healthcare Industry

Computer Vision: The Doctor’s Eye of Healthcare Industry

by November 27, 2020

Computer Vision

Computer vision focuses on training computers to replicate human sight and understand the objects in front of them.

Computer vision is designed to recognize and understand images and data to execute actions that only humans were once thought to be capable of performing. The healthcare industry has already seen a bunch of benefits coming from the rise of Artificial Intelligence (AI) solutions. Computer vision technology is highly contributing to the mechanism, which can potentially support many different applications delivering life-saving functionalities for patients.

The emerging field of computer vision focuses on training computers to replicate human sight and understand the objects in front of them. Big players like Amazon and Facebook are already in the market, investing millions of dollars in the technology. Healthcare is also joining the race. Computer vision applications in healthcare such as diagnostics, medical imaging, clinical trial attrition reduction, surgery accuracy improvement, and much more are making a change in the way patients get treated. Computer vision and image processing have made great progress in the past decade. Operating as a human’s eye, computer vision algorithms find out patterns and anomalies in images to obtain a diagnosis. Through an iterative learning process aided by neural networks, computer vision identifies, evaluates, and interprets images. The goal of computer vision in healthcare is to make a faster and more accurate diagnosis than a physician could make.


Use cases of computer vision in healthcare

Faster diagnosis

Faster diagnosis is one of the greatest features of computer vision that unveils big prospects in healthcare. This helps in taking preventive measures towards diseases. Medapod, in partnership with the Chinese company Tencent, uses a computer vision application to identify and diagnose Parkinson’s symptoms using patient’s photos. The Markerless Motion Capture and Analysis System (MMCAS) identifies the frequency and intensity of joint movements and offers an accurate, real-time assessment. Babylon Health, a UK-based health service provider, has developed an app with Natural Language Processing (NLP) where a chatbot asks patients questions similar to what a doctor asks during an examination. The app uses speech and language processing to get the symptoms and forwards the information to doctors.


Accurate measurement

Computer vision is well-known for its accuracy. Orlando Health Winnie Palmer Hospital for Women and Babies is using a computer vision tool developed by Gauss surgical. The tool measures blood loss during childbirth. Using pictures taken with an iPad, the computer vision tool scans images of surgical sponges and suction canisters. Since implementing the technology at the hospital where 14,000 babies are delivered every year, doctors get to understand the amount of blood loss accurately, allowing them to treat new mothers appropriately.


Detecting illness

Computer vision is capable of detecting illness that is otherwise difficult to identify. At Mount Sinai hospital, physicians are using the AI-powered tool to detect acute neurological illness. The organization used 37,236 head CT scans from across Mount Sinai Health System to train a deep neural network to determine if an image showed an acute neurological illness. The hospital tested their tool in a randomized controlled trial carried out in a simulated clinical environment. To facilitate the technology, Mount Sinai invested in the Nvidia graphics processing units for this.


Cancer screening

Computer vision has shown promising results in detecting precancerous lesions using the minutest details in tissue imagery, increasing the sensitivity and accuracy of cancer screening tests. Diagnosing skin cancer is one of the challenging tasks because of the minute variability in the appearance of cancerous skin changes. Scientists at the Stanford University Artificial Intelligence Lab has used deep convolutional neural networks (CNNs) to create a model that analyses skin images against a dataset of more than 120,000 skin cancer images. The results revealed that this CNN model detects and classifies skin cancer as efficiently as certified dermatologists.


Alarming heart abnormalities

Arterys‘ computer vision software is trained to focus on detecting abnormalities in the heart. The company has said that the software can create three-dimensional models of a patient’s heart on a radiologist’s computer screen. It can reduce the time radiologists spend scanning patients. Artersy’s CardioAI, a software under the ArterysAI umbrella, uses what the company calls 4D Flow. The 4D Flow allows radiologists to see a three-dimensional image of a patient’s heart they can manipulate on a computer screen after an MRI scans a patient. It gives radiologists a genuine understanding of the patient’s heart without requiring time-consuming processes.


Where to from here?

Computer vision technology has made a remarkable stance in the healthcare sector. With the increasing population and rising medical needs, technology is the only futuristic solution that doctors have in hand. In the future, humankind will see an increasing number of healthcare institutions experiment with computer vision to deliver better services.