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Enhancing healthcare systems with computer vision

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Enhancing healthcare systems with computer vision

Remote Rehabilitation and Monitoring

Computer vision is the ability of computers to interpret and analyze visual data from the world around them, using algorithms and machine learning techniques. We have already integrated multiple deployments of Computer Vision into lives. There have been successful deployments of computer vision in multiple fields.

Here, we aim to understand avenues for computer vision deployments in healthcare. In a general sense, Computer Vision has the potential to revolutionize the way healthcare is delivered by enabling more accurate and efficient diagnoses, treatments, and surgeries. In addition, computer vision can be used to automate routine tasks, such as monitoring vital signs or detecting changes in a patient’s condition, freeing healthcare providers to focus on more complex tasks.

Medical image analysis

Medical images such as X-rays, CT scans, and MRIs are essential tools for diagnosing and monitoring a wide range of medical conditions. However, interpreting these images can be time-consuming and subject to human error. By using computer vision algorithms to analyze medical images, healthcare professionals can more quickly and accurately detect abnormalities and identify patterns that may indicate the presence of a particular condition. For instance, there has been research noting the effectiveness of CV in breast cancer detection. When trained efficiently, it can help healthcare providers to identify patterns and trends that may not be immediately apparent to the human eye. This allows for more accurate diagnoses and treatment plans. In addition to reducing the time consumed, such tech will also increase the scope of telemedicine services.

Surgical assistance

Computer vision can be used to help surgeons navigate complex procedures by providing real-time visual guidance. For example, computer vision algorithms can be used to track the position of surgical instruments and overlay this information onto a live video feed, helping surgeons identify the location of critical structures and avoid damaging them during surgery. The quality of the surgery can also be evaluated using computer vision by monitoring activity levels, spotting frantic activity, and other anomalies in the body cavity of the patient.

Telemedicine

The COVID-19 pandemic has highlighted the need for remote healthcare services, and computer vision can play a key role in this area. By using video conferencing software equipped with computer vision algorithms, healthcare professionals can remotely assess a patient’s condition and provide a diagnosis or treatment plan. In addition, computer vision can help doctors to use imaging diagnostics remotely. This can be useful in less accessible areas. If primary imaging and diagnostic infrastructure are installed in these areas, the results from these can be remotely received by the doctor. X-rays, MRIs and other imaging services have immense capability to exploit such benefits of computer vision. Computer Vision enabled technology can also be useful in treating dermatological problems, and avoiding unnecessary hospital visits.

Remote Rehabilitation and Monitoring

Many times, patients will need to go through considerable periods of rehabilitation following treatment for a diagnosis. Rehabilitation at home helps patients to recuperate in more familiar surroundings while reducing the risk of infection from being around sick patients. In addition, this may be economically and physically easier on the patient.

Computer vision technologies can be used during this process to aid in recovery. This is useful especially for people with limited range of motion and elderly people, as such tech would eliminate the need for them to travel. Such systems can also be used to assess mobility, healing of incisions, and so on.

Public health

Computer vision can also be used to help public health officials track and respond to outbreaks of infectious diseases. Computer Vision integrated public surveillance systems would have been the most useful during the outbreak of COVID-19, especially to track patients and create containment arenas. Computer vision algorithms can analyze data from surveillance cameras and other sources to identify patterns and track the spread of disease, helping officials to respond more quickly and effectively. Beyond the pandemic, it can be used inside hospitals to identify individuals who interacted with patients suffering from infectious diseases and so on.

Challenges

With all the benefits, there are a string of challenges we need to overcome when we start relying on Computer Vision as a diagnostic tool. The major setback is the requirement of quality training data. To train a model that will be effective in a real-life scenario, a model would need a humongous pool of medical data, which is difficult to obtain due to the high level of privacy associated with medical histories. There are similar ethical dilemmas (about consent; for instance) that shall need to be resolved before training and implementing such technologies. In addition, a small margin of error may prove to be of huge consequence for the patient.

Bottom Line

Computer vision has the potential to revolutionize healthcare by providing faster, more accurate diagnoses, improving surgical outcomes, and enabling remote healthcare services. As technology continues to evolve, we can expect to see even more innovative applications of computer vision in the healthcare industry. Nevertheless, we must be extremely cautious when training such models and implementing them, especially in the early stages. Yet, it all points to show that when trained and used efficiently, Computer Vision and Artificial intelligence can prove to be valuable assets in improving the healthcare system in general and in increasing access to these facilities.