Every surgeon sees a limited number of cases in their lifetime. As it turns out, artificial intelligence (AI) may change all that, whereas the age of machine learning, a subset of AI, is here. Today’s machines are refined enough to extract hidden insights from intricate imagery, a perspective that would alternatively circumvent even the most experienced surgeons. They will improve patient care by recognizing patterns in clinical data. Intelligent machines will make better diagnoses and predictions.
With the help of machines and through proper practice, human medical practitioners will be able to see and know things they cannot see in other circumstances. For instance, how long will it take for a burn to heal? Before, such thing was determined with very crude estimation, which was not even precise. Yet, today, it can be noticeable to a machine that’s drawing information from a considerable dataset.
Help in Key Clinical Decision-Making
In the fields of reconstructive and plastic surgery, the machine learning is likely to become a very powerful tool. It will allow surgeons to access complex clinical data, which will then help them in key clinical decision-making and with that will significantly contribute to progress in these medical areas.
During the process, the gathered historical data will show patterns and conditions, that may not be visible in the analysis of smaller datasets or in unreliable prior experience, which will thereafter be analyzed by machines in order to develop algorithms with the ability to acquire and then transfer the knowledge to the surgeon. The Tracking Operations and Outcomes for Plastic Surgeons (TOPS) database has a similar concept and is already available.
Benefits of Machine Learning Systems
There are a lot more benefits of machine learning systems such as faster detection of a problem, precise diagnoses when a patient addresses a problem, treatment prediction and monitoring of the treatment’s progress. Cosmetic surgery will also benefit from this technology in terms of predicting and stimulating forthcoming consequences of reconstructive breast surgery or cosmetic facial surgery. It a general opinion among cosmetic and plastic surgery professionals that the artificially intelligent brain can be of a great assistance in when planning aesthetic interventions and in guiding patients’ choice of procedure.
So far, there are five specific areas that show distinct promise in improving effectiveness and results in the industry by the use of machine learning. Besides already mentioned aesthetic surgery, there are:
Based on smartphone photographs an application for postoperative microsurgery was developed to oversee the circulation of tissue flaps. In the future, an algorithm for aiding in the best reconstructive surgery for a single patient may be developed.
A machine learning algorithm can not only meticulously evaluate the total percentage of body surface area burned, which is essential for surgical planning and proper treatment and a crucial piece of information for patient revitalization, it can also prognosticate whether a burn will heal even without a surgery. This approach showed as an effective tool for assessing the burn depth and has already been developed to predict the healing time of burns.
Some babies are born with an inborn condition known as craniosynostosis, which creates an unnatural display of the skull and enhanced brain pressure. Craniofacial surgery involves moving the muscles, bones and the skin of the skull. To better catch, the early signs of these congenital condition researchers trained an AI to classify the shape of the infant’s skull. With this technology, children can be screened and help reduce the number of CT scans or harmful X-rays need for diagnosis.
Peripheral nerve and hand surgery
A hand transplant or a prosthetic limb are two choices that amputees generally have today. Many criterions are involved in ascertaining which of the two options is suitable. With recent progress in transplant surgery and robotics, the possibility of making the choice is available to a considerable number of amputees. The design of a variety of neuroprostheses with automated controllers was a result of the use of artificial neural networks, including those passed down to restore wrist control and hand grasp.
In the next decade, the line between the physical world and digital technologies will become almost invisible. Using conventional techniques in an effective processing of big data is becoming challenging as the complexity and volume of biomedical data grow. Physicians and scientists need to be able to look beyond usual methods to obtain clinically significant information. However, the human intervention will still be needed to ensure the best possible result as the computer-generated algorithms are not yet able to replace the trained human eye.