DOI: 10.7910/dvn/0618j6
title: How Artificial Intelligence Is Going To Revolutionize The Plastic Surgery Of The Future.

description: Plastic surgery is a rapidly evolving field that uses different technologies to help many patients achieve the wonderful results they desire. As a specialty, it has come a long way, and so have the different cosmetic and plastic surgical techniques used to make people look good. All across the world, many of the aesthetic procedures that the top plastic surgeons perform today are a far cry from those practiced even just a decade ago. However, old meets new when it comes to the applications of artificial intelligence in plastic surgery of the future. The Burning Questions In this article, we will try to glean into the future while standing on the firm ground of what has been achieved already in the immediate past. The questions that we asked ourselves and have tried to answer are: • What are some common artificial intelligence technologies currently available? • How can they benefit individuals who want plastic surgery? • How does artificial intelligence for plastic surgery work today? • Will AI become more important in the future? Natural Language Processing The use of NLP (Natural Language Processing) has increased in recent years with applications such as spelling and grammar checking on word processors, autocorrections in text messages, and predictive texts. About a decade ago, researchers developed a system for extracting data from medical records that anchored their text to the medical record. Recent applications of NLP in plastic surgery have shown that public opinion is increasing in this area, and a large amount of data could be found within Twitter's microblogging site. It was seen that the top aesthetic plastic surgeons have increasingly used social media platforms to market their practices and provide patient education at the same time. Some researchers attempted to quantify the public perception of plastic surgery. They employed a text analysis technique called ‘hedonometry’ to study tweets related to plastic surgery that occurred for 5 continuous years. Hedonometry is an awesome machine learning technique that uses an algorithm to analyze words in the context of their surrounding words. A dataset of 10,000 words was collected from Amazon. After analyzing over a million relevant tweets, the analyzed data showed that “plastic” is the most popular term, but it had the lowest positivity score in the minds of the people. The terms “aesthetic”, “cosmetic” and “reconstructive” were certainly less popular. However, people considered them to be more positive than other words used to describe plastic surgery such as “plastic surgery” and “nip and tuck”. In another study by the researchers, the results corroborated the same facts. It was suggested that the results could be used to inform decisions about the title of an aesthetic or cosmetic surgeon. It also emphasized the potential for such applications to influence marketing strategies and public perception of plastic surgery. We all are aware of the benefits of a smartphone in answering frequently asked questions. Scientists developed an application that would give answers to questions about patient topics. Their application was trained to answer questions within 10 selected topics that were frequently asked by the patients before their surgery. Then they conducted a study by interviewing the participants to see if they understood how their technology worked. The results showed that the participants were able to accurately determine the applications of each of the selected topics 92% of the time. In addition, about 83% of the time they were able to find an adequate response. The results fuelled a hope that this amazing technology could be integrated into clinical practice to improve patient support and free up precious time for the surgeons. Facial Recognition Commercial use of facial recognition technology is increasing because more smartphone users are taking advantage of this technology. The process begins by creating a pattern that is used to measure the features of a face. The measurements are then analyzed and compared with an existing database of biometric facial measurements to identify a person. Utilizing this technology, a model was used to improve the facial care of patients by enabling it with certain characteristics. Post-operative patients were classified into groups which were beneficial in assessing patient satisfaction and setting realistic expectations before operating on them. This greatly enhanced doctor-patient communication, leading to more happy and satisfied patients. Additional applications with this technology show promise in the diagnosis of developmental disorders that have certain facial characteristics, as well as in assessing the success of complicated craniofacial surgeries. Deep Learning Over the last two decades, research in deep learning for plastic surgery has progressed smoothly because of the abundance of unstructured data collected through widely used technologies. Aesthetic plastic surgeons regularly collect images before and after their plastic surgery procedures that are subsequently made available to the national database, creating a large data source. A study conducted on facial plastic surgery found that an ANN (artificial neural network) could correctly classify the status of nose correction surgeries, also called rhinoplasty, in 85% of the tested images. The results had a level of sensitivity and specificity that was almost comparable to that of the residents and treating doctors of that institute. In another Deep Learning application developed for the recognition of Melanoma, ANN has been used to identify melanomas in the images of biopsied lesions. The images were obtained through smartphones attached with a dermoscopic lens. The ANN could identify characteristics of malignant melanoma learning directly from the data it was receiving. This same application was again tested by providing it with high-quality images published in dermatology journals. The probability of this application in diagnosing melanoma through the images was evaluated and found to have an equivalent accuracy to that of the treating clinicians. This study showed the Deep Learning application’s potential as a diagnostic and decision-making tool for physicians, which could help treat cancer in the early stages and prevent the dreaded metastases of skin cancer. Conclusion In the future, AI will be able to assess the appearance of a face and body with unparalleled precision and accuracy. Although it is still early in development, this technology has been shown to help surgeons accurately predict what type of treatment an individual requires.