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Applications of Computer Vision In Healthcare

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:

COVID-19 diagnosis

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.

Cancer detection

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.

Movement Analysis

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.

Tumor Detection

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.

Summing up

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.

August 2021 Digital Health Roundup

There is an app for everything, and healthcare is no exception. Digital health apps are big business, but maybe they could be better utilized. The way we use telehealth is evolving rapidly, and it varies from state to state, but could that change at some point? Getting information about your health is important, but in many cases when you’re seeking information, you end up finding that there is no shortage of misinformation.

Healthcare Misinformation

Be careful where you get your information about cancer treatments, reports medicalxpress.com. A new study found that misinformation about health conditions is becoming more and more common, especially on social media sites. The study looked at 200 of the most popular cancer treatment articles found on social media, and discovered that one third of them include misinformation, and that those articles get more attention and engagement than the articles with evidence-based information. The misinformation is not only misleading, but it can also potentially be harmful to patients and negatively affect patient outcomes. One way to avoid misinformation is to use trusted sites like Patient Empowerment Network’s powerfulpatients.org, and it’s always important to talk to your trusted care provider about any treatment information you find online. Find out more about the study here.

Access to Telehealth

As special Covid-19 legislation expires in many states, access to telehealth is less certain, reports healthcaredive.com. During the Covid-19 pandemic many states used waivers to allow medical professionals to provide telehealth care to patients in other states. The waivers have already expired in some states like Florida, Alaska, New York, and Minnesota, but other states like Arizona are passing legislation to make the telehealth waivers permanent. Advocates for making permanent changes to telehealth access say it would help with staffing shortages, providing access to healthcare in rural areas, and maintaining doctor/patient relationships. It can also be beneficial to those who need to see specialty providers not available in their home states. However, because medical licensing is regulated at the state level, it’s difficult for doctors who want to practice across state lines. As the lawmakers continue to try to make telemedicine available to those who need it, some are saying a federal medical licensing system might be in order, or a system of reciprocity between states where out-of-state licenses would be automatically recognized. It will be interesting to see what permanent telehealth regulations result. Find out more here.

While many say telehealth increases access to care, there are others who say it could do just the opposite. Check out forbes.com to read an opinion on how to make sure telehealth doesn’t end up being a contributor to health inequities here.

Health Apps

There is no shortage of digital health apps, reports mobihealthnews.com. A recent report discovered that of the 350,000 digital health apps available 47 percent are geared toward monitoring specific diseases or health conditions, such as diabetes. The report also found that the effectiveness of health apps is being studied more often and that 90,000 new health apps were introduced in 2020. Not only are there a lot of digital health apps, but they are a big money industry as well. So far 2021 is showing record-breaking numbers in digital health investment. Learn more here.

While health apps are growing in number and popularity, they aren’t being fully utilized, reports medicalxpress.com. Typically, data and information that users collect and record about their health doesn’t connect to the patient’s medical chart. Healthcare providers then don’t have the opportunity to monitor the information or provide feedback about the data. However, a new study showed that patients with high blood pressure saw health benefits when they monitored their condition using an app that was accessible to their healthcare providers The study found that the typical patient had a reduction in systolic blood pressure. Researchers hope that eventually patients and providers can use mobile apps to better treat chronic health conditions and provide better health outcomes. Read more here.