Exploring Medical Applications of Computer Vision in Healthcare
Computer Vision (CV) integration is a light of transformative promise in the ever-changing domain of healthcare. Check it out.
People’s health and even lives depend on how well doctors perform their duties in the healthcare industry. Machines can currently manage many such duties better than humans: they never fatigue, and they don’t feel the consequences of pressure throughout even the most difficult processes.
Advances in computer vision in healthcare, particularly in object detection, provide clinicians with the superpowers of unwavering focus and observation. For example, a machine’s error rate is only 3.5% against 5% for humans. Simply said, computer vision object detection and recognition are superior at these tasks.
What is computer vision in healthcare?
Computer vision is a branch of the broader concept of “artificial intelligence.” It is concerned with the analysis of still photos or video streams in order to reach conclusions and take specified actions.
The healthcare software development industry is no stranger to computer vision applications. CT scans, MRIs, and X-rays all use computer vision technologies to some level. Doctors have employed software for years to analyze imaging data, diagnose patients, and recommend therapies. However, generative AI substantially improves computer vision accuracy, enabling use cases previously unthinkable.
Computer vision systems can achieve up to 99% accuracy by training deep-learning AI models with vast amounts of visual data. This is a huge improvement above the 50% accuracy of conventional computer vision systems. The breakthrough gives compelling reasons to investigate new use cases for vision-aided technologies in the healthcare business.
Nvidia has developed Clara-AGX, a scalable computing architecture that supports real-time image processing for medical use cases, in response to the growing relevance of computer vision in medical imaging. Meanwhile, Medtronic offers an AI-enabled tool that allows surgeons to digitally prepare and practice treatments.
Computer vision shapes statistical trends in healthcare
In the coming years, the market for computer vision in healthcare is likely to develop dramatically. In 2022, the Computer Vision in Healthcare Market was worth USD 992 million. It is expected to reach a significant value of USD 22,244 million by 2030, with a spectacular Compound Annual Growth Rate (CAGR) of 47.8% from 2023 to 2030.
The development of new, technologically superior products is gaining acceptance among global computer vision in healthcare market trends. To increase their market position, major companies in the computer vision in healthcare sector are focusing on creating AI-based technology.
For example, IBM will launch Natural Language Processing (NLP) technology for computer vision in July 2020, which is one of the AI-based technologies. This new NLP technology is useful in computer vision for chatbots, spam detection, text summarization, virtual agents, and machine translation.
According to the computer vision in healthcare market analysis, the major players in the market are NVIDIA Corporation, IBM, Google, Microsoft, Intel Corporation, Basier AG, AiCure, iCAD Inc, Xilinx Inc, Arteries, Comofi Medtech Pvt Ltd, SenseTime, Maxicareb Healthcare Corporation, Thunderpod Inc., and Alphabet.
The global computer vision in healthcare market is divided into product, hardware, network, and memory; application, medical imaging and diagnostics, surgeries, clinical trials, patient management, and research; and end-user, healthcare providers and diagnostic centers.
According to the computer vision in healthcare market overview, Europe will be the market’s largest region in 2024. During the projected period, North America will be the fastest-growing region in the worldwide computer vision in healthcare market. The geographies included in the global healthcare computer vision market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.
Computer vision use cases in healthcare
1. Better image analysis
Computer vision can discover patterns and diagnose medical images with considerably higher accuracy, speed, and fewer errors. It has the capability of extracting information from medical photographs that are not visible to the naked eye.
Furthermore, given the shortage of radiologists and MRI technicians in the healthcare industry, computer vision can be regarded as a viable solution.
2. Detecting tumors and cancers
Some cancers are extremely difficult to identify until they have progressed to a later stage. Early detection using AI and machine learning can allow patients to obtain therapy before a more serious prognosis.
For example, medical experts at the NCI’s Center for Cancer Research have built a multiparametric AI model capable of detecting prostate cancer early.
3. X-ray analysis
Computer vision systems learn to recognize attributes via training on image databases. In the medical field, X-ray technology has numerous applications. It aids in the detection of tumors, cancer and the presence of Covid-19. Aside from these applications, X-ray technology is ideal for MRI construction and procedures.
When it comes to dentofacial treatment, current methods rely on manual X-ray scan analysis. Manual efforts are always prone to errors and take a long time. The use of computer vision to automate the process solves both concerns.
4. CT scan and MRI
The power of computer vision to recognize patterns in images assists doctors in ways other than X-rays. It aids in the early diagnosis of various disorders. Physicians can also seek assistance with CT scans and MRI pictures. Deep learning techniques and computer vision, paired with neutral network models, aid in image database classification. The technology can then detect and highlight problems in photos for further action.
5. Smart operating facilities
Computer vision has the potential to automate the recording of surgical procedures that include a variety of repetitive and error-prone processes. Surgeons forget equipment inside patients in approximately 1500 procedures in the United States each year, and computer vision can track surgical tools to avoid this issue.
6. Reduced patient mix-up
In the healthcare industry, patient misidentification is a regular problem. This might have dire repercussions for both the patient and the healthcare professional. A computer vision-enabled facial recognition system can solve this challenge.
7. Increased workplace safety
Surveillance systems powered by computer vision and AI can monitor employees for possible issues and warn appropriate authorities when necessary. They can also monitor whether the personnel are using suitable safety equipment and procedures.
8. Enabling smart surgical theaters
Surgeons who perform surgeries on their patients use visual guidance to reduce procedural risks. In healthcare, computer vision can offer real-time feeds on the afflicted area, minimizing errors such as mistakenly leaving medical items in the patient’s body.
Furthermore, computer-aided technologies enable medical personnel to effortlessly document the procedure. Surgeons and nurses can concentrate on the process instead of manually providing visual data to the EHR platform.
Benefits of using computer vision in healthcare
1. Speed up treatments and responses to incidents
Computer vision technology, specifically video analytics, enables medical professionals to watch patients in real time and produce immediate notifications in the event of an emergency.
2. Improve treatment accuracy
Computer vision helps to increase treatment accuracy by harnessing computer vision to improve diagnoses and assist doctors in finding previously overlooked disease symptoms. This allows for more accurate diagnosis and the creation of tailored treatment plans, resulting in better treatment outcomes.
3. Address patient misidentification issues
Identification errors are more common in hospitals than are usually recognized. Face recognition technology for biometric identification not only overcomes this problem, but it also speeds up the registration procedure.
Final thoughts
In conclusion, the incorporation of computer vision in healthcare has the potential to change patient care, from diagnostic precision to therapy tailoring. Healthcare personnel can use technology to expedite operations, minimize wait times, and diagnose diseases in their early stages.
This not only improves surgical precision and overall patient experience, but it also reduces operational expenses, resulting in a more sustainable healthcare system.
As computer vision technology progresses, we may expect even such innovations that will push the boundaries and improve the future of patient care.
