Artificial Intelligence in Plastic Surgery: Applications and Challenges

Latent disentanglement in mesh variational autoencoders improves the diagnosis of craniofacial syndromes and aids surgical planning

BACKGROUND AND OBJECTIVE

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level.

METHODS

In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. The model is trained on a dataset of 3D meshes of healthy and syndromic patients which was increased in size with a novel data augmentation technique based on spectral interpolation. Thanks to its semantically meaningful and disentangled latent representation, SD-VAE is used to analyse and generate head shapes while considering the influence of different anatomical sub-units.

RESULTS

Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to approximate the outcome of a range of craniofacial surgical procedures.

CONCLUSION

This work opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes. Our code is available at github.com/simofoti/CraniofacialSD-VAE.

Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Systematic Review

Artificial intelligence (AI) is a technology that is evolving rapidly and is changing the world and medicine as we know it. After reviewing the PROSPERO database of systematic reviews, there is no article related to this topic in facial plastic and reconstructive surgery. The objective of this article was to review the literature regarding AI applications in facial plastic and reconstructive surgery.A systematic review of the literature about AI in facial plastic and reconstructive surgery using the following keywords: Artificial Intelligence, robotics, plastic surgery procedures, and surgery plastic and the following databases: PubMed, SCOPUS, Embase, BVS, and LILACS. The inclusion criteria were articles about AI in facial plastic and reconstructive surgery. Articles written in a language other than English and Spanish were excluded. In total, 17 articles about AI in facial plastic met the inclusion criteria; after eliminating the duplicated papers and applying the exclusion criteria, these articles were reviewed thoroughly. The leading type of AI used in these articles was computer vision, explicitly using models of convolutional neural networks to objectively compare the preoperative with the postoperative state in multiple interventions such as facial lifting and facial transgender surgery.In conclusion, AI is a rapidly evolving technology, and it could significantly impact the treatment of patients in facial plastic and reconstructive surgery. Legislation and regulations are developing slower than this technology. It is imperative to learn about this topic as soon as possible and that all stakeholders proactively promote discussions about ethical and regulatory dilemmas.

Clinical Effect of Dermatologic Trephination Combined With Radiotherapy in the Treatment of Keloids

BACKGROUND

Keloids are excessive formations of scar tissue that develop at the site of a skin injury. Due to their invasive nature, they have a negative impact on the skin's appearance and are prone to recurrence, making them a challenging condition to treat with regard to skin aesthetics.

OBJECTIVES

The objective of this article was to compare the long-term effects of dermatologic trephination with nonsurgical treatments for scars and evaluate the clinical value of the treatments.

METHODS

A retrospective analysis was conducted of 48 patients who received keloid treatment in the Department of Dermatology and Department of Thoracic Surgery at our hospital from January 2021 to October 2023. Twenty-four patients received dermatologic trephination, and 24 patients received nonsurgical treatment. Outcome measures included scar appearance, scar healing time, pain and itching levels, and patient satisfaction.

RESULTS

The healing time of patients receiving dermatologic trephination was significantly shorter than that of patients in the nonsurgical group. The degree of itching in patients undergoing dermatologic trephination was significantly lower than that of patients in the nonsurgical group. The satisfaction of patients who received dermatologic trephination was significantly higher than that of patients in the nonsurgical group.

CONCLUSIONS

In this study we demonstrated that trephination achieves better long-term results in keloid revision, including improved keloid appearance, itching symptoms, and patient satisfaction.

LEVEL OF EVIDENCE: 3

Preoperative Patient Guidance and Education in Aesthetic Breast Plastic Surgery: A Novel Proposed Application of Artificial Intelligence Large Language Models

Background

At a time when Internet and social media use is omnipresent among patients in their self-directed research about their medical or surgical needs, artificial intelligence (AI) large language models (LLMs) are on track to represent hallmark resources in this context.

Objectives

The authors aim to explore and assess the performance of a novel AI LLM in answering questions posed by simulated patients interested in aesthetic breast plastic surgery procedures.

Methods

A publicly available AI LLM was queried using simulated interactions from the perspective of patients interested in breast augmentation, mastopexy, and breast reduction. Questions posed were standardized and categorized under aesthetic needs inquiries and awareness of appropriate procedures; patient candidacy and indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; postprocedure instructions and recovery; and procedure cost and surgeon recommendations. Using standardized Likert scales ranging from 1 to 10, 4 expert breast plastic surgeons evaluated responses provided by AI. A postparticipation survey assessed expert evaluators' experience with LLM technology, perceived utility, and limitations.

Results

The overall performance across all question categories, assessment criteria, and procedures examined was 7.3/10 ± 0.5. Overall accuracy of information shared was scored at 7.1/10 ± 0.5; comprehensiveness at 7.0/10 ± 0.6; objectivity at 7.5/10 ± 0.4; safety at 7.5/10 ± 0.4; communication clarity at 7.3/10 ± 0.2; and acknowledgment of limitations at 7.7/10 ± 0.2. With regards to performance on procedures examined, the model's overall score was 7.0/10 ± 0.8 for breast augmentation; 7.6/10 ± 0.5 for mastopexy; and 7.4/10 ± 0.5 for breast reduction. The score on breast implant-specific knowledge was 6.7/10 ± 0.6.

Conclusions

Albeit not without limitations, AI LLMs represent promising resources for patient guidance and patient education. The technology's machine learning capabilities may explain its improved performance efficiency.

Level of Evidence 4

Fat or fillers: The dilemma in eyelid surgery

The aging of the periocular region has always aroused great interest. A fresh, young, and attractive sight determined an ever-greater attention to surgical and non-surgical techniques to obtain this result. In particular, the change in the concept of a young look, considered then "full", led to the increasing use of surgical (fat grafting) or medical (hyaluronic acid) filling techniques. Eyelid rejuvenation became increasingly popular in the field of cosmetic treatments, with a focus on achieving a youthful and refreshed appearance. Among the various techniques available, the choice between using fat grafting or fillers presented a clinical dilemma. In particular, what surgery considered of fundamental importance was a long-lasting result over time. On the other hand, aesthetic medicine considered it fundamental not to have to resort to invasive treatments. But what was the reality? Was there one path better than the other, and above all, was there a better path for patients? The minireview aims to explore the physiopathology, diagnosis, treatment options, prognosis, and future studies regarding this dilemma. We analyzed the literature produced in the last 20 years comparing the two techniques. Current literature reveals advancements in biomaterials, stem cell research and tissue engineering held promise for further enhancing the field of eyelid rejuvenation. The choice between fat grafting and fillers in eyelid cosmetic treatments presented a clinical dilemma. Understanding physiopathology, accurately diagnosing eyelid aging, exploring treatment options, assessing prognosis, and conducting future studies were essential for providing optimal care to patients seeking eyelid rejuvenation.

Ethics for AI in Plastic Surgery: Guidelines and Review

INTRODUCTION

Artificial intelligence (AI) holds the potential to revolutionize medicine, offering vast improvements for plastic surgery. While human physicians are limited to one lifetime of experience, AI is poised to soon surpass human capabilities, as it draws on limitless information and continuous learning abilities. Nevertheless, as AI becomes increasingly prevalent in this domain, it gives rise to critical ethical considerations that must be addressed by professionals.

MATERIALS AND METHODS

This work reviews the literature referring to the ethical challenges brought on by the ever-expanding use of AI in plastic surgery and offers guidelines for its application.

RESULTS

Ethical challenges include the disclosure of use of AI by caregivers, validation of decision-making, data privacy, informed consent and autonomy, potential biases in AI systems, the opaque nature of AI models, questions of liability, and the need for regulations.

CONCLUSIONS

There is a lack of consensus for the ethical use of AI in plastic surgery. Guidelines, such as those presented in this work, are needed within each discipline of medicine to respond to important ethical considerations for the safe use of AI.

LEVEL OF EVIDENCE V

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Unveiling the Potential of AI in Plastic Surgery Education: A Comparative Study of Leading AI Platforms' Performance on In-training Examinations

Background

Within the last few years, artificial intelligence (AI) chatbots have sparked fascination for their potential as an educational tool. Although it has been documented that one such chatbot, ChatGPT, is capable of performing at a moderate level on plastic surgery examinations and has the capacity to become a beneficial educational tool, the potential of other chatbots remains unexplored.

Methods

To investigate the efficacy of AI chatbots in plastic surgery education, performance on the 2019-2023 Plastic Surgery In-service Training Examination (PSITE) was compared among seven popular AI platforms: ChatGPT-3.5, ChatGPT-4.0, Google Bard, Google PaLM, Microsoft Bing AI, Claude, and My AI by Snapchat. Answers were evaluated for accuracy and incorrect responses were characterized by question category and error type.

Results

ChatGPT-4.0 outperformed the other platforms, reaching accuracy rates up to 79%. On the 2023 PSITE, ChatGPT-4.0 ranked in the 95th percentile of first-year residents; however, relative performance worsened when compared with upper-level residents, with the platform ranking in the 12th percentile of sixth-year residents. The performance among other chatbots was comparable, with their average PSITE score (2019-2023) ranging from 48.6% to 57.0%.

Conclusions

Results of our study indicate that ChatGPT-4.0 has potential as an educational tool in the field of plastic surgery; however, given their poor performance on the PSITE, the use of other chatbots should be cautioned against at this time. To our knowledge, this is the first article comparing the performance of multiple AI chatbots within the realm of plastic surgery education.

Quality of the Information provided by ChatGPT for Patients in Breast Plastic Surgery: Are we already in the future?

Introduction

In recent years, artificial intelligence (AI) has gained popularity, even in the field of plastic surgery. It is increasingly common for patients to use the internet to gather information about plastic surgery, and AI-based chatbots, such as ChatGPT, could be employed to answer patients' questions.The aim of this study was to evaluate the quality of medical information provided by ChatGPT regarding three of the most common procedures in breast plastic surgery: breast reconstruction, breast reduction, and augmentation mammaplasty.

Methods

The quality of information was evaluated through the expanded EQIP scale. Responses were collected from a pool made by ten resident doctors in plastic surgery and then processed by SPSS software ver. 28.0.

Results

The analysis of the contents provided by ChatGPT revealed sufficient quality of information across all selected topics, with a high bias in terms of distribution of the score between the different items. There was a critical lack in the "Information data field" (0/6 score in all the 3 investigations) but a very high overall evaluation concerning the "Structure data" (>7/11 in all the 3 investigations).

Conclusion

Currently, AI serves as a valuable tool for patients; however, engineers and developers must address certain critical issues. It is possible that models like ChatGPT will play an important role in improving patient's consciousness about medical procedures and surgical interventions in the future, but their role must be considered ancillary to that of surgeons.

A Novel Artificial Intelligence Model for Symmetry Evaluation in Breast Cancer Patients

INTRODUCTION

Artificial intelligence (AI) is a milestone for human technology. In medicine, AI is set to play an important role as we progress into a new era. In plastic surgery, AI can participate in breast symmetry assessment, which until now has been mainly subjective, allowing for inconsistencies. This study aims to improve this evaluation process by integrating a novel trained neural network with the breast symmetry calculator, BAS-Calc.

MATERIALS AND METHODS

We combined the BAS-Calc tool with a custom-made neural network trained to automatically detect key features of the breast. This integrated system was tested on 81 images of patients who had undergone breast reconstruction post-breast cancer treatment. Its performance was evaluated against two human observers using statistical analysis.

RESULTS

Our model successfully detected 399/405 (98.51%) of landmarks. Spearman and Pearson correlation indicated a strong positive relationship while Cohen's kappa demonstrated moderate to strong agreement between human observers and AI model. Notably, the average calculation time for the AI was 0.92 seconds, 16 times faster than the 14.09 seconds for humans.

CONCLUSIONS

Our AI model successfully calculated breast symmetry from images of patients who had undergone reconstructive oncological breast surgery, demonstrating high correlation with human assessments and a markedly reduced processing time. As AI continues to evolve, it is poised to become a pivotal tool in Medicine. Therefore, it is crucial for medical professionals to proactively engage in implementing AI technologies safely and effectively. Further studies are required to broaden our understanding and maximize the potential benefits in this area. Takeaway bullet points Artificial intelligence (AI) is an upcoming force to be reckoned with. AI should find its way into practical applications in plastic surgery. AI can be applied to improve patient care and evaluate aesthetic results. In this work, we present a novel AI model that automatically evaluates breast symmetry.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Artificial Intelligence in Plastic Surgery: Insights from Plastic Surgeons, Education Integration, ChatGPT's Survey Predictions, and the Path Forward

Background

Artificial intelligence (AI) is emerging as a transformative technology with potential applications in various plastic surgery procedures and plastic surgery education. This article examines the views of plastic surgeons and residents on the role of AI in the field of plastic surgery.

Methods

A 34-question survey on AI's role in plastic surgery was distributed to 564 plastic surgeons worldwide, and we received responses from 153 (26.77%) with the majority from Latin America. The survey explored various aspects such as current AI experience, attitudes toward AI, data sources, ethical considerations, and future prospects of AI in plastic surgery and education. Predictions from AI using ChatGPT for each question were compared with the actual survey responses.

Results

The study found that most participants had little or no prior AI experience. Although some believed AI could enhance accuracy and visualization, opinions on its impact on surgical time, patient recovery, and satisfaction were mixed. Concerns included patient privacy, data security, costs, and informed consent. Valuable AI training data sources were identified, and there was agreement on the importance of standards and transparency. Respondents expected AI's increasing role in reconstructive and aesthetic surgery, suggesting its integration into residency programs, addressing administrative challenges, and patient complications. Confidence in the enduring importance of human professionals was expressed, with interest in further AI research.

Conclusion

The survey's findings underscore the need to harness AI's potential while preserving human professionals' roles through informed consent, standardization, and AI education in plastic surgery.

A Triple Combination Therapy Using 2-mm Biopsy Punch for the Treatment of Multifocal Keloids

BACKGROUND

Keloid treatment is challenging. The surgical approach can be divided into complete excision versus partial excision.

OBJECTIVE

The current study aims to introduce our novel surgical approach of partial excision using a 2-mm punch biopsy device to treat refractory multifocal keloids in the trunk.

MATERIALS AND METHODS

This is a case series of 30 patients with refractory multifocal keloids treated with a triple combination therapy consisting of a punch-assisted partial excision and intralesional triamcinolone injections followed by immediate single fractional electron beam radiotherapy within 8 hours, postoperatively. The follow-up period was 12 months. The primary outcome was recorded as recurrence versus nonrecurrence or aggravation versus remission . The secondary outcome was patient satisfaction as assessed by the POSAS.

RESULTS

The recurrence or aggravation of keloid was not found without complications. Scores obtained from the POSAS patient scale showed that pain, itchiness, color, stiffness, thickness, and irregularity significantly improved.

CONCLUSION

Our novel surgical approach using a 2-mm punch biopsy device effectively treats refractory multifocal keloids once considered intractable. Triple combination therapy of partial excision using a 2-mm punch biopsy device, intralesional triamcinolone injections, followed by immediate single fractional electron beam radiotherapy, is a safe, efficacious, and more convenient protocol to treat this condition.

A Narrative Review of Artificial Intelligence (AI) for Objective Assessment of Aesthetic Endpoints in Plastic Surgery

Notoriously characterized by subjectivity and lack of solid scientific validation, reporting aesthetic outcome in plastic surgery is usually based on ill-defined end points and subjective measures very often from the patients' and/or providers' perspective. With the tremendous increase in demand for all types of aesthetic procedures, there is an urgent need for better understanding of aesthetics and beauty in addition to reliable and objective outcome measures to quantitate what is perceived as beautiful and attractive. In an era of evidence-based medicine, recognition of the importance of science with evidence-based approach to aesthetic surgery is long overdue. View the many limitations of conventional outcome evaluation tools of aesthetic interventions, objective outcome analysis provided by tools described to be reliable is being investigated such as advanced artificial intelligence (AI). The current review is intended to analyze available evidence regarding advantages as well as limitations of this technology in objectively documenting outcome of aesthetic interventions. It has shown that some AI applications such as facial emotions recognition systems are capable of objectively measuring and quantitating patients' reported outcomes and defining aesthetic interventions success from the patients' perspective. Though not reported yet, observers' satisfaction with the results and their appreciation of aesthetic attributes may also be measured in the same manner.Level of Evidence III This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

"Facekit"-Toward an Automated Facial Analysis App Using a Machine Learning-Derived Facial Recognition Algorithm

Introduction: Multiple tools have been developed for facial feature measurements and analysis using facial recognition machine learning techniques. However, several challenges remain before these will be useful in the clinical context for reconstructive and aesthetic plastic surgery. Smartphone-based applications utilizing open-access machine learning tools can be rapidly developed, deployed, and tested for use in clinical settings. This research compares a smartphone-based facial recognition algorithm to direct and digital measurement performance for use in facial analysis. Methods: Facekit is a camera application developed for Android that utilizes ML Kit, an open-access computer vision Application Programing Interface developed by Google. Using the facial landmark module, we measured 4 facial proportions in 15 healthy subjects and compared them to direct surface and digital measurements using intraclass correlation (ICC) and Pearson correlation. Results: Measurement of the naso-facial proportion achieved the highest ICC of 0.321, where ICC > 0.75 is considered an excellent agreement between methods. Repeated measures analysis of variance of proportion measurements between ML Kit, direct and digital methods, were significantly different (F[2,14] = 6-26, P<<.05). Facekit measurements of orbital, orbitonasal, naso-oral, and naso-facial ratios had overall low correlation and agreement to both direct and digital measurements (R<<0.5, ICC<<0.75). Conclusion: Facekit is a smartphone camera application for rapid facial feature analysis. Agreement between Facekit's machine learning measurements and direct and digital measurements was low. We conclude that the chosen pretrained facial recognition software is not accurate enough for conducting a clinically useful facial analysis. Custom models trained on accurate and clinically relevant landmarks may provide better performance.

Man, or Machine? Artificial Intelligence Language Systems in Plastic Surgery

Artificial intelligence (AI) language models are computer programs trained to understand and generate human-like text. The latest AI language models available to the public have impressive language generation capability with immediate applications in both academia and private practice. Plastic surgeons can immediately leverage this technology to more efficiently allocate valuable human capital to higher-yield tasks. This can ultimately translate to higher patient volume, higher research output, and improved patient communication. Commercially available models offer business solutions that should not be ignored by plastic surgeons hoping to establish, optimize, or grow their practices. In this paper, the authors review the current state of AI language systems, discuss potential applications, and explore the risks and limitations of this technology.

Artificial Intelligence in Facial Plastic Surgery: A Review of Current Applications, Future Applications, and Ethical Considerations

From virtual chat assistants to self-driving cars, artificial intelligence (AI) is often heralded as the technology that has and will continue to transform this generation. Among widely adopted applications in other industries, its potential use in medicine is being increasingly explored, where the vast amounts of data present in electronic health records and need for continuous improvements in patient care and workflow efficiency present many opportunities for AI implementation. Indeed, AI has already demonstrated capabilities for assisting in tasks such as documentation, image classification, and surgical outcome prediction. More specifically, this technology can be harnessed in facial plastic surgery, where the unique characteristics of the field lends itself well to specific applications. AI is not without its limitations, however, and the further adoption of AI in medicine and facial plastic surgery must necessarily be accompanied by discussion on the ethical implications and proper usage of AI in healthcare. In this article, we review current and potential uses of AI in facial plastic surgery, as well as its ethical ramifications.

Art and Safety of Gluteal Augmentation: Future Directions

Gluteal augmentation is a quickly evolving field that continues to grow in the realms of patient safety, surgical education, and technological advancement. This article discusses innovation in gluteal augmentation and suggests potential new pathways for developing the practice of gluteal augmentation.

Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review

OBJECTIVE

To review the application of machine learning (ML) in the facial cosmetic surgeries and procedures METHODS AND MATERIALS: Electronic search was conducted in PubMed, Scopus, Embase, Web of Science, ArXiv and Cochrane databases for the studies published until August 2022. Studies that reported the application of ML in various fields of facial cosmetic surgeries were included. The studies' risk of bias (ROB) was assessed using the QUADAS-2 tool and NIH tool for before and after studies.

RESULTS

From 848 studies, a total of 29 studies were included and categorized in five groups based on the aim of the studies: outcome evaluation (n = 8), face recognition (n = 7), outcome prediction (n = 7), patient concern evaluation (n = 4) and diagnosis (n = 3). Total of 16 studies used public data sets. ROB assessment using QUADAS-2 tool revealed that six studies were at low ROB, five studies were at high ROB, and others had moderate ROB. All studies assessed with NIH tool showed fair quality. In general, all studies showed that using ML in the facial cosmetic surgeries is accurate enough to benefit both surgeons and patients.

CONCLUSION

Using ML in the field of facial cosmetic surgery is a novel method and needs further studies, especially in the fields of diagnosis and treatment planning. Due to the small number of articles and the qualitative analysis conducted, we cannot draw a general conclusion about the impact of ML in the sphere of facial cosmetic surgery.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A Machine Learning Approach to Identify Previously Unconsidered Causes for Complications in Aesthetic Breast Augmentation

INTRODUCTION

Primary breast augmentation is one of the most commonly requested aesthetic procedures. Considering the large number of procedures performed in connection with a high demand, it is crucial to prevent complications. For this reason, finding and avoiding possible sources of complications is decisive.

METHODS

Between January 2010 and December 2021, 1625 female patients underwent an aesthetic breast augmentation performed by a single surgeon. The data collected were analyzed through a machine learning technique for binary recursive partitioning. This made it possible to detect unknown sources of a complication and determine a vertex for the various features.

RESULTS

When analyzing the data, for most features a high importance score with low entropy was achieved, concluding a high significance. In addition, reproducibility was demonstrated through detailed testing and training accuracies in the algorithm. With this procedure, in addition to known risks such as a high BMI and round implant shape, a larger than A preoperative bra-cup size (OR: 2.7) and a taller body could also be identified as most significant influencing factors for complications.

DISCUSSION

Preoperative breast size plays an exceptionally important role in the occurrence of complications and should be a factor held in a surgeon's considerations. In addition, this study shows ways to transfer artificial intelligence into plastic surgery to increase medical quality.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Artificial Intelligence Confirming Treatment Success: The Role of Gender- and Age-Specific Scales in Performance Evaluation

In plastic surgery and cosmetic dermatology, photographic data are an invaluable element of research and clinical practice. Additionally, the use of before and after images is a standard documentation method for procedures, and these images are particularly useful in consultations for effective communication with the patient. An artificial intelligence (AI)-based approach has been proven to have significant results in medical dermatology, plastic surgery, and antiaging procedures in recent years, with applications ranging from skin cancer screening to 3D face reconstructions, the prediction of biological age and perceived age. The increasing use of AI and computer vision methods is due to their noninvasive nature and their potential to provide remote diagnostics. This is especially helpful in instances where traveling to a physical office is complicated, as we have experienced in recent years with the global coronavirus pandemic. However, one question remains: how should the results of AI-based analysis be presented to enable personalization? In this paper, the author investigates the benefit of using gender- and age-specific scales to present skin parameter scores calculated using AI-based systems when analyzing image data.

Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms

BACKGROUND

Microvascular tissue reconstruction is a well-established, commonly used technique for a wide variety of the tissue defects. However, flap failure is associated with an additional hospital stay, medical cost burden, and mental stress. Therefore, understanding of the risk factors associated with this event is of utmost importance.

AIM

To develop machine learning-based predictive models for flap failure to identify the potential factors and screen out high-risk patients.

METHODS

Using the data set of 946 consecutive patients, who underwent microvascular tissue reconstruction of free flap reconstruction for head and neck, breast, back, and extremity, we established three machine learning models including random forest classifier, support vector machine, and gradient boosting. Model performances were evaluated by the indicators such as area under the curve of receiver operating characteristic curve, accuracy, precision, recall, and F1 score. A multivariable regression analysis was performed for the most critical variables in the random forest model.

RESULTS

Post-surgery, the flap failure event occurred in 34 patients (3.6%). The machine learning models based on various preoperative and intraoperative variables were successfully developed. Among them, the random forest classifier reached the best performance in receiver operating characteristic curve, with an area under the curve score of 0.770 in the test set. The top 10 variables in the random forest were age, body mass index, ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity. Interestingly, only age, body mass index, and ischemic time were statistically associated with the outcomes.

CONCLUSION

Machine learning-based algorithms, especially the random forest classifier, were very important in categorizing patients at high risk of flap failure. The occurrence of flap failure was a multifactor-driven event and was identified with numerous factors that warrant further investigation. Importantly, the successful application of machine learning models may help the clinician in decision-making, understanding the underlying pathologic mechanisms of the disease, and improving the long-term outcome of patients.

Challenges of Implementing Artificial Intelligence in Interventional Radiology

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.

Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications

Dynamic decision-making was essential in the clinical care of surgical patients. Reinforcement learning (RL) algorithm is a computational method to find sequential optimal decisions among multiple suboptimal options. This review is aimed at introducing RL's basic concepts, including three basic components: the state, the action, and the reward. Most medical studies using reinforcement learning methods were trained on a fixed observational dataset. This paper also reviews the literature of existing practical applications using reinforcement learning methods, which can be further categorized as a statistical RL study and a computational RL study. The review proposes several potential aspects where reinforcement learning can be applied in neurocritical and neurosurgical care. These include sequential treatment strategies of intracranial tumors and traumatic brain injury and intraoperative endoscope motion control. Several limitations of reinforcement learning are representations of basic components, the positivity violation, and validation methods.

Artificial Intelligence in Plastic Surgery: Current Applications, Future Directions, and Ethical Implications

Background:

Artificial intelligence (AI) in healthcare delivery has become an important area of research due to the rapid progression of technology, which has allowed the growth of many processes historically reliant upon human input. AI has become particularly important in plastic surgery in a variety of settings. This article highlights current applications of AI in plastic surgery and discusses future implications. We further detail ethical issues that may arise in the implementation of AI in plastic surgery.

Methods:

We conducted a systematic literature review of all electronically available publications in the PubMed, Scopus, and Web of Science databases as of February 5, 2020. All returned publications regarding the application of AI in plastic surgery were considered for inclusion.

Results:

Of the 89 novel articles returned, 14 satisfied inclusion and exclusion criteria. Articles procured from the references of those of the database search and those pertaining to historical and ethical implications were summarized when relevant.

Conclusions:

Numerous applications of AI exist in plastic surgery. Big data, machine learning, deep learning, natural language processing, and facial recognition are examples of AI-based technology that plastic surgeons may utilize to advance their surgical practice. Like any evolving technology, however, the use of AI in healthcare raises important ethical issues, including patient autonomy and informed consent, confidentiality, and appropriate data use. Such considerations are significant, as high ethical standards are key to appropriate and longstanding implementation of AI.

Role of Artificial Intelligence (AI) in Surgery: Introduction, General Principles, and Potential Applications

AI (Artificial intelligence) is an interdisciplinary field aimed at the development of algorithms to endow machines with the capability of executing cognitive tasks. The number of publications regarding AI and surgery has increased dramatically over the last two decades. This phenomenon can partly be explained by the exponential growth in computing power available to the largest AI training runs. AI can be classified into different sub-domains with extensive potential clinical applications in the surgical setting. AI will increasingly become a major component of clinical practice in surgery. The aim of the present Narrative Review is to give a general introduction and summarized overview of AI, as well as to present additional remarks on potential surgical applications and future perspectives in surgery.