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All the technological advances developed in healthcare allowed us to understand the anatomy and physiology of the different organs that structure the human body more precisely in recent decades. With the development of computer vision and artificial intelligence, it is relatively easy to identify the diseases from early phases to begin the treatment on time.
The advanced applications provide the ability to process, analyze, and understand complex information for early diagnoses. From a bird’s eye view, a complex interaction of SuperData, same as quality data, machine learning, and analytics applicable to detection, speech recognition, computer vision, etc., offers potential outcomes.
Computer vision developments promise to make healthcare more precise and accessible to all. Here, we focused on presenting the game-changing technology of computer vision in healthcare, by connecting biology aspects with artificial intelligence and computer vision for precise treatment.
The role of computer vision in healthcare
Computer vision encompasses training a model with the right data to help the model detect objects and draw conclusions. It automates, comprehends, and imitates human visual data systems to predict desirable outcomes. Computer vision functions include:
- Obtaining the images,
- Transforming the data into images,
- Separating multidimensional data from them.
Providing insights, this analyzed data is HIPAA-compliant and helps to identify patterns, objects, and trends in labeled data. Technological advances in computer vision provide the following benefits to the healthcare industry:
- Rapid medical research
- More accurate and coherent imaging analysis
- Decreased labor expenses and lower insurance costs
- Better patient identification in the cases of mistaken identity
- Consistent and accurate results
- Smart operating rooms eliminating manual efforts
- Safety equipment usage and identifying sterile processing failures
The top applications of computer vision in healthcare
Computer vision applications in healthcare sector and increasing, yet a few common examples include:
Due to the COVID-19 pandemic, the healthcare system is constantly facing challenges in terms of supporting critical patients and medical costs on time. Computer vision and artificial intelligence widely utilized for COVID-19 prediction and analysis.
Computed tomography (CT) images, Magnetic Resonance Imaging (MRI), and clinical data provide helpful input to AI algorithms, scanning different human body sections to identify the diagnosis. Training steps and higher resolution raise the model performance, allowing higher hyperparameters on the results, not to mention the importance of having a reliable annotation tool at hand.
For better clinical decision-making, visualization tools contribute to high-quality 3D images in the domain of COVID-19 detection. Here is more on COVID-19 diagnosis applications:
- X-ray radiography (CXR) — It assists radiologists in correctly recognizing lung infection and producing quantitative analysis and diagnoses.
- Disease progression score — It has become essential to analyze disease progression to recognize patients with a high risk to develop severe COVID-19 infection before it’s too late.
- Masked face recognition — One of the successful ways to prevent COVID-19 from spreading is wearing masks in public gatherings. Detection and recognition of masked faces tremendously benefit in large public settings.
With technological advances, it is now possible to detect cancer in earlier stages. Since some symptoms of skin problems are similar, it’s difficult to identify forms of cancer including but not limited to skin, bone, breast cancer.
The trial of trained models over 1.2 million skin cancer images has shown successful results, the same as certified professionals. Cancer detection as well as cell classification applications cover the following:
- Semantic segmentation — The most challenging part of medical image analysis is to identify the pixels of organs or lesions from background medical images, including MRI or CT images. Semantic segmentation helps to classify each pixel belonging to a specific label.
- EEG analysis — The Electroencephalogram analysis mainly used for neural dysfunction including cancer detection, brain tumor, has great potential to bring forward advancement in accuracy and treatment of neural dysfunctions.
- Cell classification — Cell classification creates a visualization of specific tissues and organs to predict more accurate diagnoses. Cell classification applications include comprehending the consequences of drugs and genes in screening experiments, localization of various proteins, as well as diagnoses of cancer from images acquired using different approaches.
- Drug discovery — It incorporates many levels from recognition of targeted symptoms and diseases, implementation of experiments, and formation of chemical compounds outlined to transition in regions prompting the underlying disease.
- Cell biology — Researching the particular structure and attributes of an organism’s genetic fabrication can provide deeper analysis into development status. This outlook helps with cell image classification, where most of the time biological processes are hardly seen by the human eye.
- Digital pathology — Allowing patient tissue samples along with digital whole slide images (WSIs) to be distributed on an international scale for diagnostic, educating, and research purposes.
This measuring method is used in computer vision and image processing to identify movement. The purpose of movement analysis is to sense motion in an image, trace and identify an object’s motion in a particular time frame, class of objects that make a move together, and detect the direction of motion.
Gathering information helps healthcare providers trace muscle activity, perform gait analysis and diagnose possible mobility issues. For patients with cerebral palsy, joint issues, muscular dystrophy, and such diagnoses, movement analysis tests are required in a certified laboratory.
A variety of movement analysis systems allow movement to be captured in different settings, which can broadly be classified into devices interconnected to the body and video-based techniques. While some methods require to be captured in diverse environments, others can be distinguished in specific settings.
Approximately 70% of tumors are detected in the later stages of the disease when it is challenging to treat, which defines the low survival rate. Computer-aided diagnostic tools assist radiologists to spot malignant tumors in earlier phases. Deep learning systems predict what tumor is from the basis of large real-world data sets and examples.
Applying trained algorithms to evaluate images detects the most subtle tumor pattern within seconds, facilitating a supplementary resource for physicians. These applications are useful to save patients’ lives and diagnose with accurate treatment before any harmful stage. New techniques are developed to better the precision of tumor diagnosis.
Computer vision in healthcare has evolved substantially over recent times, and various technological research has contributed a vast amount of important information to the industry. Computer vision has the probability to be applied across diverse environments and disciplines. The requirements and priorities for a computer vision in the healthcare sector heavily depend on the unique capture environment and research area across different disciplines. Computer vision in healthcare is advancing further from the newer implementations in technology. The applications are in development phases with the potential to improve medical procedures around the world. Helping doctors to reduce the time and efforts required in predicting health conditions or results of medication, these applications benefit healthcare providers.
Melanie Johnson, AI and computer vision enthusiast with a wealth of experience in technical writing. Passionate about innovation and AI-powered solutions. Loves sharing expert insights and educating individuals on tech.