Computer Vision for Better Healthcare and Medication
Although different areas of computer vision have various abilities, a common function is to act as an eye that can recognize and analyze visual data, like images and videos, in much greater detail than human eyes.
This capability brings advantages to different industries, including healthcare, which is one of the sectors that greatly impacts our well-being. By implementing computer vision in modern healthcare, we can significantly improve medication, making it safer and more accessible, ultimately enhancing the lives of all.
In this article, we will take you on a journey to explore the different ways computer vision is used in healthcare, showing how it can improve our well-being.
Computer vision and modern healthcare
With the capabilities of computer vision, especially video analytics, integrating this technology in healthcare enhances the efficiency of medical tasks that require precision and discretion. For example, using video analytics to monitor patients continuously can timely alert medical staff in emergencies. Leveraging video analytics can also improve remote medication by allowing doctors to perform procedures for patients who are not physically present. Additionally, utilizing computer vision for disease diagnosis shows promising potential in increasing treatment success rates.
These solutions not only improve the quality and accuracy of patient care but also save costs and time for healthcare professionals. By using technology to reduce the need for manual work and avoid mistakes, these advancements provide better care for patients while optimizing resources.
Examples of computer vision solutions in healthcare
Monitoring patients during home rehabilitation
During the rehabilitation and physical therapy period, it is crucial to provide close care for patients. However, some individuals may prefer to undergo rehabilitation at home instead of hospital. The field of video analytics in computer vision offers a tool that can support home rehabilitation while ensuring similar levels of care as provided in hospitals. Doctors can monitor and track the progress of recovery by observing patients' movements through a video analytics system. They can also remotely conduct the Timed Up and Go Test (TUG) to evaluate the walking ability and risk of falls. In case of emergencies, we can use a fall detection solution to detect falls and accidents, and timely alerts can be sent to the caretaker. This solution not only offers comprehensive care but also respects patient privacy by eliminating the need for caretakers to be constantly present, which can make patients feel violated and uncomfortable.
In many areas, access to quality healthcare is limited, making efficient Telehealthcare crucial. The field of video analytics in computer vision contributes to making this innovation a reality. Professionals from renowned hospitals can utilize video analytics to analyze patients' video footage and provide remote diagnoses. This enables patients in remote areas to access high-quality medical care. Telehealthcare is particularly beneficial in emergencies, as it allows professionals to perform procedures without the need to transport the patients.
Diagnosing diseases at an early stage
Rapidly spreading diseases like cancer or tumors can have better treatment outcomes and recovery if detected early. Computer vision plays a vital role in diagnosing these diseases in their early stages, which may sometimes go unnoticed by humans. By training computer vision models with extensive sets of X-ray, CT scan, MRI, and ultrasound images from patients with these diseases, the models can learn to differentiate between normal and abnormal images, capturing even the tiniest signals. These models can assist doctors in making diagnoses and increase the chances of detecting disease signals at an early stage, thereby reducing time and workload.
One of the medical advancements that significantly benefits patients is personalized treatments. Computer vision technology plays a crucial role in analyzing CT scans, MRI images, and other X-rays to assess a patient's health and identify any congenital diseases. This analysis helps in designing personalized treatment plans. Additionally, we can implement face recognition, a form of computer vision technology, to quickly identify patients visiting the hospital. This enables healthcare providers to access patient history and treatment guidelines promptly, streamlining the registration process and reducing identification errors.
Enhancing operational efficiency within the hospital
We can leverage video analytics technology to improve operational efficiency within the hospital. For instance, we can utilize AI to monitor the availability of beds in the ward, particularly in the emergency room, and generate alerts when the beds are nearing capacity. This allows for better coordination with other wards to identify solutions, such as patient transfers, ensuring comprehensive care for all patients. Additionally, we can identify any equipment that might impede operations, such as wheelchairs or beds left in the aisles, and promptly notify the staff to remove them, preventing any hindrance in the fast-paced ward. Furthermore, by employing a people density counting solution, we can assess the inflow of visitors in the hospital and allocate space and staff. This enables us to manage the number of people within appropriate limits during each period and effectively plan cleaning activities for high-traffic areas, ensuring optimal hygiene.
We can also make it easier for hospital visitors by integrating face recognition technology with the hospital database. This technology can identify patients through face detection and recognition, which speeds up the registration process and eliminates the need for filling out documents. This allows patients to receive faster and more efficient services, leaving a positive impression.
Benefits of using computer vision in healthcare
Speed up treatments and responses to incidents. Computer vision technology, specifically video analytics, enables hospital professionals to monitor patients in real time and generate immediate alerts during emergencies.
Improve treatment accuracy by leveraging computer vision to enhance diagnoses and assist doctors in identifying previously unnoticed disease symptoms. This enables precise diagnoses and the development of personalized treatment plans, resulting in more effective treatment outcomes.
Address patient misidentification issues. Identification errors occur more frequently in hospitals than commonly believed. Implementing face recognition technology for biometric identification not only solves this problem but also expedites the registration process.
Challenges in implementing computer vision in healthcare
Medical information, including diagnoses, treatment guidelines, and individual health data, is highly sensitive data. Therefore, the primary challenge in implementing computer vision solutions in healthcare is ensuring the security of personal data.
To address this challenge, it is crucial to establish a secure infrastructure and employ robust encryption methods. Choosing a private server or private cloud with identity verification is also essential. Additionally, the system should be transparent and easily monitored to prevent data leakage and vulnerabilities.
Developing a computer vision solution that combines efficiency with maximum safety requires the expertise of professionals. Sertis has a team of experts ready to design tailored computer vision solutions for each hospital and facility that enhance healthcare operations and medication. Our solutions offer a solid and secure infrastructure while remaining cost-effective and delivering tangible medical benefits.
Learn more about computer vision solutions for healthcare from Sertis at https://www.sertiscorp.com/ai-security