Revamping Healthcare with Computer Vision | Labellerr — Labelling Made Easy | Blog
By Rohit Kumar
Computer Vision has come a long way in the last decade. Because of its importance in healthcare applications, medical imaging has received a lot of attention. Computer Vision algorithms, which work similarly to the human eye, detect patterns and irregularities in pictures to make a diagnosis. Computer vision recognizes, assesses, and analyzes pictures using an iterative learning process helped by neural networks. A markets and markets report highlights: “The computer vision in healthcare market is projected to reach USD 1,457 million by 2023 from USD 210 million in 2018, at a CAGR of 47.2% during the forecast period.”
Computer vision is one of the most promising technologies in healthcare, with enormous potential. One of the key advantages of Artificial Intelligence and persistent demand in healthcare is analysing challenging circumstances, making predictions, and identifying trends. AI algorithms that are well-executed have the potential to save lives by pointing out errors and improving therapy.
AI is already being used in the healthcare industry. Now that computer vision is being used in this industry, it can promote the emergence of numerous applications that can prove to be life-saving for patients. This AI-powered technology is supporting more doctors in better diagnosing their patients, prescribing appropriate medications, and tracking the progression of various ailments. Computer vision not only saves medical workers time on routine chores, but it also saves patient’s lives. The uses of technology for medical use must perform by expanding on present methods of usage and adding a layer of creativity and imagination. Currently, computer vision is being used in numerous fields in healthcare to help medical practitioners better diagnose patients, including medical imaging analysis, predictive analysis, and health monitoring, among others. Below are some of the ways computer vision technology may help health care systems.
Clinical trial retention management
A computer vision program can assist researchers in determining if a patient is adhering to a given treatment plan. This can aid in the reduction of medical attrition, or the number of patients who drop out of clinical studies. The program works with a phone app to monitor patients as they follow a treatment plan. Users are urged to take the medications in front of their phone’s camera, and the program detects if the patient has taken the recommended prescription by utilizing face recognition technology. The software’s algorithm has been trained by analyzing thousands of videos of individuals taking medication, and it thus interprets the sequences in a way that humans would see as a video of someone taking medication.
Technology for Surgical Assistance
Machine learning models are being used by scientists to increase surgical precision and accuracy. During difficult surgical operations, these algorithms have assisted surgeons in making appropriate judgments. In healthcare, computer vision can be utilized for surgical simulation and assistance. The technology can assist surgeons in making difficult decisions, particularly during laparoscopic surgery, where physicians can only depend on cameras. A wonderful example of this is Touch Surgery’s smartphone app, which allows anybody to learn about and prepare for procedures. With over 100 surgical simulations spanning fourteen specialities, the software makes augmented reality surgical training even more realistic.
Trend analysis and research
Drug companies, medical institutes, and device makers can use scans of previous patients to find trends in their study. They can save time in the clinical trial phase of research by evaluating these images. Through these scans, computer vision can uncover trends and draw correlations that human researchers might struggle to notice. Identifying patterns in illness progression aids in the discovery of solutions as well as methods to prevent them entirely. Most lethal disorders, such as cancer, must be identified in their early stages. Because of its finely honed pattern recognition capacity, computer vision can identify early symptoms with high confidence. This can help with prompt treatment and save numerous lives in the long run.
Disease Diagnostics
Computer vision is being employed in equipment that can assist doctors in detecting unusual irregularities in brain scans, resulting in immediate therapy recommendations. This is advantageous for stroke victims because it allows them to obtain therapy sooner, resulting in a speedier recovery. Based on a speedy diagnosis, physicians can deliver the appropriate therapy. The software is utilized on brain scans, and the scan is uploaded into the system, where a trained algorithm evaluates healthy brain regions vs those that may correspond to abnormalities. The program warnings graphically highlight the abnormal areas of the scan. Computer vision may also aid in the visual identification of potential cancers and other irregularities in X-rays. Three-dimensional scans may be uploaded into the program, which can subsequently provide area measurements for various organ components in the image. The program then highlights the locations where it suspects tumors or other irregularities exist. The doctor can give more attention to these regions.
Leverage Labellerr
The use cases are marvellous but one bottleneck remains. The high quality labelled dataset to train your deep learning models to adhere to any one of the above-mentioned use-cases.
Are you encountering a similar problem? Training a Computer vision model requires a voluminous amount of high quality labelled data. Which in turn needs a sophisticated enterprise-scale, cloud-based annotation system. Where you can plug your data stream from the cloud/ on-prem based databases and get labelled data with confidence scores for training the computer vision models and accounting for a cent per cent data privacy.
If yes. I got your back. Let me introduce you to a one-stop solution to all your machine learning needs. Labellerr
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For your, AI needs Labellerr provides pre-trained models and products that can deliver Plug n-Play APIs with basic functionality to reach clients and can be modified on request easily and swiftly.
Leverage the auto-label feature on Labellerr to annotate your data with 10x speed and save crucial man-hours.
Visit our website and mention your use case in brief and our customer engineer will contact you and help you prepare the plan and get you running on a trial with us to validate.
Originally published at https://blog.labellerr.com on May 25, 2021.
