Computer vision applications in healthcare - Diagnostic applications, medical imaging, Clinical trial attrition reduction, Surgery accuracy improvement and more will be discussed in this article.
Computer vision and image processing have made great progress in the past decade. Medical imaging has gained a lot of attention due to its important role in healthcare applications. Operating in a similar way to a human 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.
Here, we discuss the current applications of computer vision in healthcare and what business leaders in the industry can expect in the near future.
Computer Vision in Healthcare Applications
Computer vision is one of the leading technologies that probably has the greatest potential in healthcare. Analyzing difficult situations, making predictions, and determining patterns is one of the main features of Artificial Intelligence and a constant requirement in healthcare. Well executed AI algorithms are capable of saving lives by pointing out inconsistencies and improving treatment.
Computer vision applications are growing rapidly in the healthcare industry and are now a part of everyday life. Healthcare depends mainly on images for the diagnosis, this is where Computer vision comes into use. The images are hard to understand and require comprehending definite patterns. Machine learning models can exceed human capabilities in image analysis, therefore, making doctors and researchers get accurate results.
Table of Contents
Computer Vision in Healthcare Applications
1.Managing clinical trial retention
A computer vision software can help researchers monitor whether a patient is following a prescribed treatment. This can help with medical attrition, i.e. the number of people who drop out of clinical trials.
The software works with the help of a phone app and monitors patients as they go through a treatment plan. Users are instructed to take the drugs in front of their phone’s camera, and by using facial recognition technology the software identifies if the patient has ingested the prescribed medication.
The software’s algorithm is trained through thousands of footage showing people taking medication, and therefore, understands the sequences in a way that humans would perceive as a video of someone ingesting a medicine.
2. Computer vision in Surgery
Computer vision in healthcare can be used for surgical simulation and surgical assistance. The technology can help surgeons with complicated decisions, especially during laparoscopic surgeries where surgeons can only rely on cameras.
A great example of this is a mobile app by a company called Touch Surgery, which allows anyone to learn and prepare for surgeries. With more than 100 surgical simulations across fourteen specialties, the app enables surgical training through augmented reality to become even more immersive.
As Computer vision’s recognition capacity increases, surgeons may be able to use augmented reality in real-life surgeries. It can provide warnings, guidance, and updates based on what the algorithm sees in the operating room.
3. Faster Lab test results and treatment
Computer vision applications enable remote diagnosis and faster test results. Faster diagnosis helps in taking preventive measures towards diseases.
A British health service provider called Babylon Health has developed an app with NLP algorithms where a Chatbot asks patients questions similar to what a doctor will ask during an examination. The app uses speech and language processing to get the symptoms and forwards the information to a doctor. The information from the app is then used to provide faster diagnosis.
Another company Medopad, along with the partnership with a Chinese company Tencent, uses computer vision applications to identify and diagnose Parkinson’s symptoms using user photos. The Markerless Motion Capture and Analysis System (MMCAS) identifies the frequency and intensity of joint movements and offers an accurate, real-time assessment.
4. Research and identifying trends
Scans of past patients can be used for research by drug companies, medical institutions, and devise manufacturers for identifying trends. By analyzing these scans, they can save time in the clinical trial phase of research. Computer vision can identify trends and make connections through these scans, that would be almost impossible to detect by human researchers. Identifying patterns in disease progression helps discover means to solve them as well as ways to avoid them altogether.
5. Providing an accurate measurement tool
Computer vision is being used as a tool for measuring blood loss during childbirth. Orlando Health Winnie Palmer Hospital is using an AI tool developed by Gauss Surgical, is using CV to judge the amount of blood loss during childbirth. Using pictures taken with an iPad, the tool scans images of surgical sponges and suction canisters.
With CV, it is no longer a guessing game and the chances of overestimation and underestimation have reduced drastically. It is now possible to judge the amount of blood loss accurately enabling doctors to provide more accurate treatment.
6. Diagnostic applications
Computer vision is being used in devices that can help doctors find rare anomalies in brain scans, resulting in quick treatment suggestions. This is helpful with stroke patients, as they can receive treatment sooner, resulting in faster recovery. Physicians can administer the targeted treatment based on a quick diagnosis. The software is used on brain scans where the scan is uploaded in the system, and a trained algorithm determines healthy regions of the brain compared to those areas that might correlate to anomalies. The software alerts visually point out the anomalous areas in the scan.
Computer vision can also help visually identify possible tumors and other anomalies in X-rays. Three-dimensional scans can be uploaded into the software, which can then generate area measurements for different parts of the organ in the scan. The software then highlights the areas it believes to have tumors or other anomalies. The physician can pay closer attention to these areas.
Here are some more image based diagnostic applications of computer vision:
a. Automatic analysis of 3D radiological images
Computer vision can help in the automatic depiction of tumors and healthy anatomy in 3D radiological images. Project InnerEye, an AI-powered tool by Microsoft Research, is an example of this. The product aims at improving the performance of radiologists, oncologists, and surgeons while working with radiological images. The software uses multi-dimensional images to identify tumors and anomalies.
b. Finding Anomalies in the heart, lungs, and liver
Computer vision software is being trained to focus on finding abnormalities in the heart, lungs, and liver. This reduces the time spent by radiologists in scanning patients.
It is possible to determine whether a scan is of a healthy or dysfunctional heart with the help of a software called 4D Flow. The software allows radiologists to see three-dimensional images of an MRI scan on the computer screen and the anomalies are then identified on the dashboard. These scans allow radiologists to get a better understanding without getting into a time-consuming and invasive surgery. The software has been developed by running millions of MRI scans through the algorithm – indicating both healthy and dysfunctional hearts.
c. Cancer Diagnosis using computer vision
One of the most important applications of computer vision is locating cancerous cells and tumors from biopsy results. Computer vision can identify potential skin cancer tumors more effectively than dermatologists. Similarly, lung cancer can be identified through lung CT scan images.
Computer vision has had great success in visual object detection and recognition, and these applications can assist radiologists in improving the accuracy of mammography screening. Breast cancer screenings through computer vision give better results as it analyzes mammogram images more accurately to identify tumors. This breakthrough is especially positive for healthcare since biopsy results can be given on the spot.
Rework from DeepMind Health, in collaboration from University College London Hospital, has been able to identify neck and head cancer through deep learning algorithms. They have been able to identify head and neck cancer with similar accuracy as a doctor in a much lesser amount of time.
d. Pneumonia detection
Machine Learning models can be trained to classify and detect the presence of pneumonia using X-ray images. Convolutional Neural Network models can identify features from X-ray images to analyze and decide if a person has pneumonia. These models reduce the unreliability issues faced while dealing with medical imagery. Using a Machine Learning model for predictions can highly improve a patient’s chances of survival due to early and more accurate detection of the disease.
Here is an example of Pneumonia detection in X-rays through Image Classification by Skyl.ai.
Skyl.ai enables analysis and categorization of images in an effective and scalable manner with state-of-the-art computer vision. We offer templatized and guided Machine Learning workflows that are easy to understand and follow.
To conclude, there is great potential for computer vision in healthcare as it can improve the quality and standard of living around the world. Doctors rely on images and scans regularly to make their diagnoses, which need to be faster and more accurate for better quality healthcare, and computer vision applications help with that.