Are computer vision and machine learning the future of medicine?

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In this blog, we will discuss how machine learning and computer vision will become the future of medicine.

What is computer vision in medicine?

Medicine offers many opportunities for computer vision. Computer vision in medicine aims to achieve characteristics and behaviors of the human body by acquiring and interpreting medical data from different sources. One of the most promising applications for computer vision is medical imaging.

According to Grand View Research, the global medical imaging market was worth USD 15.9 billion in 2020 and is approximated to increase at a CAGR of 5.2 percent from 2021 to 2028.

Medical imaging creates a visualization of tissues and organs to improve?

accurate diagnosis. With medical imaging, doctors better look at the patient's internal organs and detect any problems or defects. Medical imaging includes endoscopy, MRI, ultrasound-ray radiography.

How does computer vision advances medical diagnosis?

Companies have already started integrating deep learning to predict heartbeat to estimate blood loss during childbirth.

More machines in medicine will allow clinicians to focus more on patients. Using computer vision in healthcare could reduce costs by transferring time-consuming and repetitive tasks to devices to help doctors provide better patient care.

Computer Cision in AI

Orlando Health?Winnie Palmer Hospital ?for Women & Babies uses computer vision via an artificial intelligence technology developed by Gauss Surgical to quantify blood loss during childbirth an instance of computer vision possibilities in healthcare.

One of the vital causes of death in childbirth is postpartum hemorrhage. The artificial intelligence system analyses photographs of surgical sponges and suction canisters obtained using an iPad device.

Before using AI at the hospital doctors discovered that they frequently overestimate the blood women lose after delivery. This hospital delivers 14,000 infants each year. They can better understand blood volume with computer vision enabling them to treat women adequately.

Computer vision in healthcare for fast resolution

For diagnosing acute neurological illnesses,?mount sinai ?has developed a supervised learning model. They used 37,236 head CT scans from Mount Sinai Health System for training a deep neural network to evaluate if an image indicated an acute neurological illness. While creating this model they tested it in a virtual medical environment in a blind randomized controlled experiment.

Mount sinai has invested in Nvidia graphics processing units for AISINAI to create the requisite infrastructure. In addition, Nvidia has teamed up with?scripps research ?Translational Institute to create a hub for AI in genomics and digital sensors. Which will focus on developing biomedical research infrastructure, tools, best practices.

Machine learning for clinical trails

Clinical trials are a principal part of medical research and recruiting patients is challenging. Surprisingly, patient signup difficulties are the primary reasons for trial terminations. Machine learning assists medical researchers in finding a solution to this problem. Australia's commonwealth scientific and industrial research organization scientists created a?machine learning ?approach that searches patient data for suitable candidates for specific trials. This is only an example of how machine learning is being used to improve clinical studies.

Conclusion

Computer vision and machine learning are a magical combination to change healthcare. These technologies are stepping beyond innovative technologies and developing solutions to improve healthcare for all of us. For example, your doctor might talk to you about your symptoms perform light medical checks in the future. Make recommendations on stopping the recurrence of health problems based on computer vision and machine learning.

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