Computer vision is a technology based on image processing and synthesis. It usually involves machine learning and allows AI to simulate human vision.
This technology aims to save time by automating manual image analysis and achieving a higher level of accuracy. For this purpose, an algorithm is fed with a vast amount of representative pictures and trained to detect particular parts of them. Developers currently use computer vision in a wide range of spheres: from Snapchat and Instagram masks to scientific research and medical objectives.
Main Reasons to Use Computer Vision in the Medical Field
🔴 Speed 🔴
With all the hardware, fast internet, and cloud we have nowadays, computers can process images in microseconds. This allows doctors to have AI analyze, let’s say, all the X-ray images while they can focus more on patients. Thus, doctors have a chance to find out more specific details through their soft skills and provide care to more of those who need it.
🔴 Accuracy 🔴
Computer vision excludes the possibility of human error to some degree and serves as an assistant for radiologists. AI can help doctors detect such conditions as cancer, pneumonia, osteoporosis, and many others.
🔴 Urgency 🔴
A combination of speed and accuracy provided by computer vision might be crucial for urgent situations.
Automatic postpartum hemorrhage estimation is an example of the application of computer vision in the medical field. It allows surgeons to understand how much blood a patient has lost.
🔴 Pattern Recognition 🔴
Radiologists may also receive help from computers. You only need to compose a dataset of images with particularly associated diagnoses and train a deep learning model based on that dataset. Then, the AI will start detecting patterns in the images. For example, it can find tumors, pneumonia, or potentially dangerous moles.
Examples of Computer Vision Applications in Healthcare
✔️ Skin Cancer Detection✔️
“1 in 5 people get skin cancer”, according to the website of SkinVision — an app that helps the user detect skin cancer. You need to download, install the app, and take a photo of a mole or spot that concerns you. Then, the app will tell you whether you need to see a dermatologist or not. The software sensitivity is 95% due to the Machine Learning algorithm.
Computer vision for medical imaging is also used for training. CV-empowered training especially applies to surgeons: nowadays, they can master their skills with digital models. The Touch Surgery software allows doctors to go through simulations of different surgeries. Artificial Intelligence creates interactive 3D models of human bodies, allowing surgeons to operate them almost the same as in real life.
A pneumonia detection web app is based on a neural network that was trained on 500 chest X-ray images. The deep learning model achieved 86%+ accuracy. Yet, it is an open-source project which can only be used for research purposes, not clinical ones.
Developers of another pneumonia detection instrument complain that finding labeled data is difficult because only certified doctors can give a diagnosis. They are also unsure that the result will be relevant for other conditions since their database was limited to 1–5-year-old patients from a single hospital.
✔️Value of CV in healthcare - 2022 HIMSS European Health Conference & Exhibition;
✔️Research paper - a study on deep learning-enabled medical computer vision;
✔️Computer Vision in Healthcare Applications
✔️SKIN CANCER CLASSIFICATION NEURAL NETWORK - CASE STUDY
✔️Pneumonia detection from chest radiograph using deep learning
✔️Touch Surgery software - surgery simulator
✔️the website of SkinVision — an app that helps the user detect skin cancer
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