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AI's weak point when it comes to effectively preventing and treating orthopedic ailments.

AI's weak point when it comes to effectively preventing and treating orthopedic ailments.

The health problems our society is currently dealing with are numerous. There are significant worries developing as our lifestyles change. Our musculoskeletal system suffers as a result of sedentary occupations, bad diets, and rising stress.

Joint dislocations, ligament and tendon injuries, and sprains. While they might not seem as terrifying as cancer, they can still result in chronic discomfort and have a significant influence on the affected person's quality of life. Let's look at the lives of Brad and Tom to see how.

Brad, who is 35, takes good care of himself. He maintains a good diet, works out regularly at the gym, and lives and works in the city's heart. He works in an office, but whenever he has a client meeting, he intentionally walks across town. In an effort to shed the few pounds he gained over the holidays, he recently made the decision to sign up for a weekend soccer league.

Tom is 50 years old and fairly obese. He has a desk job, commutes to work by car, orders takeout four or five times per week, and lives in the suburbs. Most evenings, he goes outside to walk his dog around the neighbourhood.

Based on the above information, who do you believe is more likely to sustain an injury? Right, Tom?

It's actually both of them.

The extra strain Brad puts on his knees when jogging around the soccer field has the same potential to injure his ACL as Tom's ankle sprain while walking his dog on an uneven surface. All lifestyle styles are experiencing an increase in these injuries.

What then can we do?

One of the biggest healthcare issues of the upcoming decades may be discovering ways to prevent and treat these injuries. Recent epidemiological research demonstrate that prevalent lifestyle disorders like ACL tears and Achilles tears are developing from injuries, placing an increasing cost on the global economy and quality of life.

In order to develop a solution, we must first understand the situation's history better. Scientists have been tackling this issue for years using cutting-edge novel ways. Utilizing Artificial Intelligence (AI) techniques to improve understanding of issues, particularly those of a medical nature, has shown to be one of the most effective methods in many situations. We are developing better tools for both prevention and reaction now more than ever. We can tell that AI-based medicine is about to become widely used just by counting the number of startups in this field.

But getting the right information is the first step in applying AI to better prevent and treat injuries. The second step involves a special fusion of AI expertise with industry-specific medical knowledge. Let's discuss them both together.


The algorithms in AI must be able to learn from a vast number of examples in order for them to function properly.

Source: stock.adobe.com / Liuzishan

Look inside a human body and record changes in the soft tissues using medical imaging tools like MRI or ultrasound. But only professional athletes can likely afford to develop methods based on extensive patient monitoring due to a global shortage of experienced radiologists.MedsIT Nexus medical coding services comprises a process of accurately transcribing the clinical data, diagnosis, and procedures performed into codes. We deal in almost all kind of medical specialities enabling them to complete the billing cycle and collections

Additionally, we don't spend a lot of time managing our health in a medical environment. However, in many instances, symptoms of a disease in the early stages can manifest and be seen in real-world environments. An alluring source of data in this area are the new wearable gadgets employed by businesses like Fathom AI or workout tracking apps like Kaia Health or Hinge Health.

In order to save time and money, we can use such data in conjunction with contemporary AI to develop new prevention and rehabilitation tools that are trained on a fusion of life and medical data. Soon, artificial intelligence (AI) may act as a second or third opinion, compare various training and rehabilitation methods, and recommend treatments for patients.

Is there room for artificial intelligence to play a bigger part?

"You need not be afraid of the outcome of a hundred fights if you know the opponent and know yourself."

Innovative tools can be used to provide individualised solutions to musculoskeletal-related issues.

Elnur's stock.Adobe.com is the source.

Let's use Brad's torn ACL and Tom's damaged ankle as an example. Typically, the recovery process for these kinds of injuries is divided into three stages of soft tissue healing (inflammatory, proliferative and remodeling). A unique set of medical techniques is required for each step. The duration of each phase will vary depending on the kind of injury a patient suffers. When to move on to the next is something we must understand. Thus:

We must first determine the characteristics that set apart the three primary stages of healing and how they overlap.

Second, we can classify each patient according to the appropriate stage using them.

Finally, we must categorise the appropriate treatments according to the level of rehabilitation.

This can be accomplished utilising an AI method known as Deep Learning with a large amount of accurately labelled data in a rather straightforward manner (DL). Deep learning (DL) got its name from the many layers that deep neural networks (DNNs) utilise. This complexity yields outcomes that may be superior to those of earlier methods. The majority of successful cases employ what is known as a "supervised method," in which carefully labelled data is fed into the DNN so that it can learn to reduce prediction error (e.g. healthy or not).

How could DL improve injury therapy and patient monitoring?

Patients are periodically checked on for injuries like Brad and Tom's, and a semi-structured report outlining their condition is appended to the image data. Additionally, there is no uniform, quantitative description of the healing process that can be seen on an MRI or US. The supervised method previously mentioned is challenging to use in these circumstances.

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