The Emerging Role of Computer Vision in Healthcare Diagnostics
By Umang Dayal
April 8, 2024
Transitioning towards the 21st century, the entire healthcare sector has revolutionized its technological implementations. From the usage of robots in surgeries to AI & ML for the rendering of organs, the applications of computer vision in healthcare diagnostics are significant. Out of these multiple applications, computer vision stands apart, enabling machines and algorithms to interpret, understand, and analyze data.
Computer vision allows machines to see and react based on pre-determined parameters. When this technology is implemented in the healthcare domain, it enables precise disease detection and accurate X-ray, MRI, and CT scan assessments. Now that we have a basic understanding of computer vision, let’s delve deeper into how it is revolutionizing healthcare diagnostics.
Benefits of Using Computer Vision in Healthcare
Improving Safety
We all know that hospitals are the hotspots for various diseases, germs, and infections. In recent scenarios, computer vision has been highly effective in detecting diseases and allowing proactive decisions for safety and hygiene. They can analyze patient rooms and surfaces for cleanliness, detecting dirt, dust, and other forms of contamination that could be harmful to patients and staff. CV can ensure that medical equipment is properly sterilized before use, reducing the risk of disease transmission. Some CV systems can monitor air quality and detect the presence of airborne pathogens in hospital environments.
Enhancing Treatment Procedure
Computer vision helps in rectifying human error when it comes to the identification of anomalies in medical imaging—in turn causing a domino effect by reducing medical costs, targeting treatment, and improving the way of life. The same has been confirmed by a study that was conducted on a deep learning algorithm which was effective in detecting conditions such as diabetic retinopathy from retinal fundus photographs.
Streamlined Resource Allocation
Hospitals need to ensure that all departments work in harmony to provide timely and appropriate treatment to each patient. However, computer vision takes it to a whole new level wherein it ensures that all assets are optimally distributed based on demand and supply. Thus, resulting in reduced wait time and optimal use of medical resources.
Automating Tasks
Automation of data entry tasks can be easily replaced by AI and ML models, computer vision expands the probability of solving challenging tasks, which include counting cells in a test tube sample, storing or processing images for better understanding, identifying and measuring tumors, and much more. Additionally, computer vision has enabled healthcare operations to enhance accuracy and reduce human errors by diminishing labor dependency.
Learn more: The Evolving Landscape of Computer Vision and Its Business Implications
Enhancing Patient Experience
Computer vision in healthcare assists in the identification of medical emergencies, by processing data faster leading to shorter wait times. In turn, this leads to better personalized medical care and an immediate call to action during underlying conditions. This results in improved patient care, higher retention rates, more referrals, and better growth opportunities for treatment centers.
Improved Patient Identification
With advancements in facial recognition software, medical computer vision systems can seamlessly locate records and verify patient identity. While wide-scale implementation has not been achieved, a study revealed 100% success in making predictions for patient identification, paving the way for correct treatment and medication for the right patient.
Learn more: Deep Learning in Computer Vision: A Game Changer for Industries
Computer Vision Utilized in Healthcare Diagnostics
We have provided a comprehensive overview of different segments where computer vision excels in replacing traditional methodologies in treatment and healthcare diagnostics.
Radiology
Radiology has been one of the first departments in healthcare to adopt computer vision. Radiologists depend on DICOM medical imaging data which is the culmination of data & information coming from multiple sources, and the use of computer vision takes innovation to another level. The implementation of advanced algorithms to detect objects has made image analysis seamless and efficient for medical experts. With the increased adoption of technological innovations, doctors and radiologists can determine new tissue formations, identify microscopic bone fractures, and monitor long-term treatment results.
Dermatology
Through the implementation of advanced AI and ML models enhanced using computer vision technology, doctors can accurately diagnose patients for their skin conditions. By training AI models over a sequence of images and videos to accurately predict skin conditions and even detect cancer & benign formations. Furthermore, computer vision is being utilized in dermatology for the detection of skin diseases at an early stage and formulation of a personalized skincare routine based on skin types.
Cardiology
Computer vision helps doctors understand and monitor congenital heart diseases and detect any kind of heart anomalies. During surgeries, doctors can use dedicated CV models to visualize blood flow in arteries and approximate blood loss.
Orthopedics
Orthopedics utilizes computer vision technology on a wide spectrum covering preoperative, intraoperative, and postoperative areas. The application of CV models, helps surgeons to plan operations effectively, plan MRI-based arthroplasty, and even integrate robotic surgery to get the best result during treatment.
Ophthalmology
Computer vision helps in the detection of early-stage eye abnormalities, analysis of the retina, eyesight tracking for accurate eye correction, and pre-operative planning for patients. There are plenty of CV applications that leverage computer vision using mobile phone cameras to detect early-stage eye diseases in children and adults.
Future of Computer Vision in Healthcare Diagnostics
Computer vision is still in its nascent stages and the growth potential is huge. Adjusting treatment in real-time and monitoring patient care around the clock would soon become a reality. Thus, taking personalized care to a whole new level. Computer vision will become much smarter and more efficient in its output because of cleaner and better quality data sets and ever-evolving advanced algorithms.
Conclusion
Healthcare diagnostics is just one of the many fields that have witnessed radical developments made by computer vision. It has paved the way for exceptional capabilities in diagnostics such as abnormality detection, surgery assistance, improved eye correction, and much more. It has transformed how surgeries are conducted and medical processes are executed while improving the chance of success.
Computer vision has helped in the disruption of several traditional practices and paved the way for unparalleled automation and efficacy in healthcare. However, the success of computer vision implementation depends on the machine learning model and the data set it was trained upon.
At DDD, we specialize in delivering precise and comprehensive data preparation solutions. Our human-in-the-loop approach enhances AI and ML models, ensuring they offer robust support for healthcare diagnostics.
