Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science

Objective and automated facial palsy grading and outcome assessment after facial palsy reanimation surgery - A prospective observational study

BACKGROUND

Facial palsy (FP) is a widespread condition affecting over 3 million people annually, with a complex etiology requiring tailored, multidisciplinary management. Despite advancements, there remains a lack of reliable, automated tools for objective pre- and postoperative assessment, limiting progress in treatment optimization. This study introduces the AI Research Metrics Model (CAARISMA ® ARMM) to evaluate FP severity and outcomes following microsurgical gracilis muscle transfer.

METHODS

We analyzed pre- and postoperative images of 20 FP patients using CAARISMA ® ARMM, which identifies 17 facial landmarks and evaluates 1,030 parameters. CAARISMA ® ARMM calculates three indices: Facial Youthfulness Index (FYI), Facial Aesthetic Index (FAI), and Skin Quality Index (SQI). All surgical procedures were performed by the senior author. Statistical analysis compared preoperative and postoperative scores using independent t-tests and Wilcoxon-Mann-Whitney tests, with significance set at p < 0.05.

RESULTS

Significant improvements were observed in the FAI scores post-surgery (p < 0.001). In contrast, FYI and SQI scores did not show significant postoperative changes (p = 0.39 and p = 0.60, respectively). Significant gender differences emerged: females showed increased FYI scores postoperatively, while males exhibited a decline (p = 0.0065). Age-related variations were also significant, with younger patients showing improved SQI and older patients experiencing declines (p = 0.040).

CONCLUSION

The CAARISMA ® ARMM effectively captures aesthetic improvements post-reanimation. Gender and age significantly influence outcomes, underscoring the key role of personalized and adaptable assessment tools. Future studies should integrate dynamic assessments and validate the CAARISMA ® ARMM across additional patient populations. CAARISMA ® ARMM holds promise as a standardized tool in FP outcome evaluation.

Development and validation of a collaborative framework for assessment of peripheral facial paralysis using facial image regions of interest

BACKGROUND

While accurate evaluation of PFP is crucial for determining optimal treatment strategies, current clinical assessments rely heavily on subjective evaluations, leading to considerable variability between inter- and intra-observer ratings.

OBJECTIVE

This study aimed to develop and validate a collaborative framework for evaluating PFP based on regions of interest in facial images.

METHODS

We developed and tested two approaches: (1) a collaborative framework integrating image interpretation techniques (representation learning via CNN) with predefined handcrafted features based on regions of interest in facial images, and (2) a convolutional neural network (CNN) model trained exclusively on full-face patient images. The diagnostic accuracy of both systems was evaluated using a test set and compared with otologists' assessments.

RESULTS

The collaborative framework achieved a mean Area Under the Curve (AUC) of 0.92 for PFP prediction in the test set, surpassing the 0.76 AUC achieved by the CNN trained on full-face images. The framework's performance matched that of experienced otologists (accuracy: 80.0% vs. 77.2%; sensitivity: 85.3% vs. 77.7%). Moreover, system assistance improved primary clinicians' mean accuracy by 17.7 percentage points.

CONCLUSIONS

These findings demonstrate that our collaborative framework-based automated diagnosis system can effectively assist clinicians in PFP diagnosis.

Turn Your Vision into Reality-AI-Powered Pre-operative Outcome Simulation in Rhinoplasty Surgery

BACKGROUND

The increasing demand and changing trends in rhinoplasty surgery emphasize the need for effective doctor-patient communication, for which Artificial Intelligence (AI) could be a valuable tool in managing patient expectations during pre-operative consultations.

OBJECTIVE

To develop an AI-based model to simulate realistic postoperative rhinoplasty outcomes.

METHODS

We trained a Generative Adversarial Network (GAN) using 3,030 rhinoplasty patients' pre- and postoperative images. One-hundred-one study participants were presented with 30 pre-rhinoplasty patient photographs followed by an image set consisting of the real postoperative versus the GAN-generated image and asked to identify the GAN-generated image.

RESULTS

The study sample (48 males, 53 females, mean age of 31.6 ± 9.0 years) correctly identified the GAN-generated images with an accuracy of 52.5 ± 14.3%. Male study participants were more likely to identify the AI-generated images compared with female study participants (55.4% versus 49.6%; p = 0.042).

CONCLUSION

We presented a GAN-based simulator for rhinoplasty outcomes which used pre-operative patient images to predict accurate representations that were not perceived as different from real postoperative outcomes.

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 .

Artificial intelligence in plastic surgery, where do we stand?

Since the pandemic, artificial intelligence (AI) has been integrated into several fields and everyday life as well. Healthcare is not an exception. Plastic surgery is a key focus area of this technological revolution, with hundreds of studies and reviews already published on the use of AI in plastics. This review summarizes the entirety of the available literature from 2020 to provide a comprehensive overview on AI innovation in plastic surgery. A systematic literature review (following the PRISMA guidelines) of all studies and papers that examined the application of AI in plastic surgery was carried out using Medline, Cochrane, Embase, and Google Scholar. Outcomes of interest included the growing role of AI in clinical consultations, diagnosing potentials, surgical planning, intraoperative, and post-operative uses. Ninety-six studies were included in this review; six examined the role of AI in consultations, fifteen used AI in diagnoses and assessments, seventeen involved AI in surgical planning, fifteen reported on AI use in post-operative predictions and management, and nine involved administrations and documentation. This comprehensive review of available literature found AI to be capable of transforming care throughout the entire patient journey. Certain challenges and concerns persist, but a collaborative effort can solve these issues to bring about a new era of medicine, where AI aids doctors in the pursuit of optimal patient care.

Role of Artificial Intelligence and Machine Learning in Facial Aesthetic Surgery: A Systematic Review

Objective: To analyze the quality of artificial intelligence (AI) and machine learning (ML) tools developed for facial aesthetic surgery. Data Sources: Medline, Embase, CINAHL, Central, Scopus, and Web of Science databases were searched in February 2024. Study Selection: All original research in adults undergoing facial aesthetic surgery was included. Pilot reports, case reports, case series (n < 5), conference proceedings, letters (except research letters and brief reports), and editorials were excluded. Main Outcomes and Measures: Facial aesthetic surgery procedures employing AI and ML tools to measure improvements in diagnostic accuracy, predictive outcomes, precision patient counseling, and the scope of facial aesthetic surgery procedures where these tools have been implemented. Results: Out of 494 initial studies, 66 were included in the qualitative analysis. Of these, 42 (63.6%) were of "good" quality, 20 (30.3%) were of "fair" quality, and 4 (6.1%) were of "poor" quality. Conclusion: AI improves diagnostic accuracy, predictive capabilities, patient counseling, and facial aesthetic surgery treatment planning.

Truth Lies in the Depths: Novel Insights into Facial Aesthetic Measurements from a U.S. Survey Panel

BACKGROUND

Aesthetic facial bone surgery and facial implantology expand the boundaries of conventional facial surgery that focus on facial soft tissue. This study aimed to reveal novel aesthetic facial measurements to provide tailored treatment concepts and advance patient care.

METHODS

A total of n=101 study participants (46 females and 55 males) were presented with 120 patient portraits (frontal images in natural head posture; 60 females and 60 males) and asked to assess the facial attractiveness (scale 0-10; "How attractive do you find the person in the image?") and the model capability score (MCS; scale 0-10; "How likely do you think the person in the image could pursue a modelling career?"). For each frontal photograph, defined facial measurements and ratios were taken to analyse their relationship with the perception of facial attractiveness and MCS.

RESULTS

The overall attractiveness rating was 4.3 ± 1.1, while the mean MCS was 3.4 ± 1.1. In young males, there was a significant correlation between attractiveness and the zygoma-mandible angle (ZMA)2 (r= - 0.553; p= 0.011). In young and middle-aged females, MCS was significantly correlated with facial width (FW)1-FW2 ratio (r= 0.475; p= 0.034). For all male individuals, a ZMA1 value of 171.79 degrees (Y= 0.313; p= 0.024) was the most robust cut-off to determine facial attractiveness. The majority of human evaluators (n=62; 51.7%) considered facial implants a potential treatment to improve the patient's facial attractiveness.

CONCLUSION

This study introduced novel metrics of facial attractiveness, focusing on the facial skeleton. Our findings emphasized the significance of zygomatic measurements and mandibular projections for facial aesthetics, with FI representing a promising surgical approach to optimize facial aesthetics.

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-Based Facial Palsy Evaluation: A Survey

Facial palsy evaluation (FPE) aims to assess facial palsy severity of patients, which plays a vital role in facial functional treatment and rehabilitation. The traditional manners of FPE are based on subjective judgment by clinicians, which may ultimately depend on individual experience. Compared with subjective and manual evaluation, objective and automated evaluation using artificial intelligence (AI) has shown great promise in improving traditional manners and recently received significant attention. The motivation of this survey paper is mainly to provide a systemic review that would guide researchers in conducting their future research work and thus make automatic FPE applicable in real-life situations. In this survey, we comprehensively review the state-of-the-art development of AI-based FPE. First, we summarize the general pipeline of FPE systems with the related background introduction. Following this pipeline, we introduce the existing public databases and give the widely used objective evaluation metrics of FPE. In addition, the preprocessing methods in FPE are described. Then, we provide an overview of selected key publications from 2008 and summarize the state-of-the-art methods of FPE that are designed based on AI techniques. Finally, we extensively discuss the current research challenges faced by FPE and provide insights about potential future directions for advancing state-of-the-art research in this field.

Applications of artificial intelligence in facial plastic and reconstructive surgery: a systematic review

PURPOSE OF REVIEW

Arguably one of the most disruptive innovations in medicine of the past decade, artificial intelligence is dramatically changing how healthcare is practiced today. A systematic review of the most recent artificial intelligence advances in facial plastic surgery is presented for surgeons to stay abreast of the latest in our field.

RECENT FINDINGS

Artificial intelligence applications developed for use in perioperative patient evaluation and management, education, and research in facial plastic surgery are highlighted. Selected themes include automated facial analysis with landmark detection, automated facial palsy grading and emotional assessment, generation of artificial facial profiles for testing and model training, automated postoperative patient communications, and improving ethnicity-sensitive facial morphometry norms. Inherent bias can exist in artificial intelligence models, and care must be taken to utilize algorithms trained with diverse datasets.

SUMMARY

Artificial intelligence tools are helping clinicians provide more standardized, objective, and efficient care to their patients. Increasing surgeon awareness of available tools, and their widespread implementation into clinical workflows are the next frontier. Ethical considerations must also shape the adoption of any artificial intelligence functionality. As artificial intelligence applications become a fixture in medicine, surgeons must employ them effectively to stay at the vanguard of modern medicine.

The law code of ChatGPT and artificial intelligence-how to shield plastic surgeons and reconstructive surgeons against Justitia's sword

Large Language Models (LLMs) like ChatGPT 4 (OpenAI), Claude 2 (Anthropic), and Llama 2 (Meta AI) have emerged as novel technologies to integrate artificial intelligence (AI) into everyday work. LLMs in particular, and AI in general, carry infinite potential to streamline clinical workflows, outsource resource-intensive tasks, and disburden the healthcare system. While a plethora of trials is elucidating the untapped capabilities of this technology, the sheer pace of scientific progress also takes its toll. Legal guidelines hold a key role in regulating upcoming technologies, safeguarding patients, and determining individual and institutional liabilities. To date, there is a paucity of research work delineating the legal regulations of Language Models and AI for clinical scenarios in plastic and reconstructive surgery. This knowledge gap poses the risk of lawsuits and penalties against plastic surgeons. Thus, we aim to provide the first overview of legal guidelines and pitfalls of LLMs and AI for plastic surgeons. Our analysis encompasses models like ChatGPT, Claude 2, and Llama 2, among others, regardless of their closed or open-source nature. Ultimately, this line of research may help clarify the legal responsibilities of plastic surgeons and seamlessly integrate such cutting-edge technologies into the field of PRS.

ChatGPT vs UpToDate: comparative study of usefulness and reliability of Chatbot in common clinical presentations of otorhinolaryngology-head and neck surgery

PURPOSE

The usage of Chatbots as a kind of Artificial Intelligence in medicine is getting to increase in recent years. UpToDate® is another well-known search tool established on evidence-based knowledge and is used daily by doctors worldwide. In this study, we aimed to investigate the usefulness and reliability of ChatGPT compared to UpToDate in Otorhinolaryngology and Head and Neck Surgery (ORL-HNS).

MATERIALS AND METHODS

ChatGPT-3.5 and UpToDate were interrogated for the management of 25 common clinical case scenarios (13 males/12 females) recruited from literature considering the daily observation at the Department of Otorhinolaryngology of Ege University Faculty of Medicine. Scientific references for the management were requested for each clinical case. The accuracy of the references in the ChatGPT answers was assessed on a 0-2 scale and the usefulness of the ChatGPT and UpToDate answers was assessed with 1-3 scores by reviewers. UpToDate and ChatGPT 3.5 responses were compared.

RESULTS

ChatGPT did not give references in some questions in contrast to UpToDate. Information on the ChatGPT was limited to 2021. UpToDate supported the paper with subheadings, tables, figures, and algorithms. The mean accuracy score of references in ChatGPT answers was 0.25-weak/unrelated. The median (Q1-Q3) was 1.00 (1.25-2.00) for ChatGPT and 2.63 (2.75-3.00) for UpToDate, the difference was statistically significant (p < 0.001). UpToDate was observed more useful and reliable than ChatGPT.

CONCLUSIONS

ChatGPT has the potential to support the physicians to find out the information but our results suggest that ChatGPT needs to be improved to increase the usefulness and reliability of medical evidence-based knowledge.

Pure Wisdom or Potemkin Villages? A Comparison of ChatGPT 3.5 and ChatGPT 4 on USMLE Step 3 Style Questions: Quantitative Analysis

BACKGROUND

The United States Medical Licensing Examination (USMLE) has been critical in medical education since 1992, testing various aspects of a medical student's knowledge and skills through different steps, based on their training level. Artificial intelligence (AI) tools, including chatbots like ChatGPT, are emerging technologies with potential applications in medicine. However, comprehensive studies analyzing ChatGPT's performance on USMLE Step 3 in large-scale scenarios and comparing different versions of ChatGPT are limited.

OBJECTIVE

This paper aimed to analyze ChatGPT's performance on USMLE Step 3 practice test questions to better elucidate the strengths and weaknesses of AI use in medical education and deduce evidence-based strategies to counteract AI cheating.

METHODS

A total of 2069 USMLE Step 3 practice questions were extracted from the AMBOSS study platform. After including 229 image-based questions, a total of 1840 text-based questions were further categorized and entered into ChatGPT 3.5, while a subset of 229 questions were entered into ChatGPT 4. Responses were recorded, and the accuracy of ChatGPT answers as well as its performance in different test question categories and for different difficulty levels were compared between both versions.

RESULTS

Overall, ChatGPT 4 demonstrated a statistically significant superior performance compared to ChatGPT 3.5, achieving an accuracy of 84.7% (194/229) and 56.9% (1047/1840), respectively. A noteworthy correlation was observed between the length of test questions and the performance of ChatGPT 3.5 (ρ=-0.069; P=.003), which was absent in ChatGPT 4 (P=.87). Additionally, the difficulty of test questions, as categorized by AMBOSS hammer ratings, showed a statistically significant correlation with performance for both ChatGPT versions, with ρ=-0.289 for ChatGPT 3.5 and ρ=-0.344 for ChatGPT 4. ChatGPT 4 surpassed ChatGPT 3.5 in all levels of test question difficulty, except for the 2 highest difficulty tiers (4 and 5 hammers), where statistical significance was not reached.

CONCLUSIONS

In this study, ChatGPT 4 demonstrated remarkable proficiency in taking the USMLE Step 3, with an accuracy rate of 84.7% (194/229), outshining ChatGPT 3.5 with an accuracy rate of 56.9% (1047/1840). Although ChatGPT 4 performed exceptionally, it encountered difficulties in questions requiring the application of theoretical concepts, particularly in cardiology and neurology. These insights are pivotal for the development of examination strategies that are resilient to AI and underline the promising role of AI in the realm of medical education and diagnostics.

An ACS-NSQIP Data Analysis of 30-Day Outcomes Following Surgery for Bell's Palsy

BACKGROUND

There exists a paucity of large-scale, multi-institutional studies that investigate the outcomes of surgery for Bell's palsy (BP). Here, we utilize a large, multi-institutional database to study the risk factors and early-stage outcomes following surgical procedures in BP.

METHODS

We reviewed the American College of Surgeons National Surgical Quality Improvement Program database (2008-2019) to identify patients who underwent surgery for the diagnosis of BP. We extracted data on comorbidities and preoperative blood values, and 30-day postoperative outcomes.

RESULTS

Two hundred fifty-seven patients who underwent surgery for BP symptoms over the 12-year review period were identified. Muscle grafts (n=50; 19%) and fascial grafts (n=48; 19%) accounted for the majority of procedures. The most common comorbidities were hypertension (n=89; 35%) and obesity (n=79; 31%). Complications occurred in 26 (10.1%) cases. Additionally, length of hospital stay was significantly associated with both surgical and medical complications (3.9±4.7 versus 1.5±2.0; P <0.01) and (3.2±3.8 versus 1.4±2.0; P <0.01), respectively. Preoperative creatinine, blood urea nitrogen, and alkaline phosphatase were identified as potential predictors of poor postoperative outcomes.

CONCLUSION

Based on multi-institutional analysis, complication rates following surgery for BP were found to be overall low and seen to correlate with length of hospital stay. Reoperations and readmissions were the most frequent complications after surgery for BP. The preoperative evaluation of routine laboratory values may help refine patient eligibility and risk stratification. In addition, our findings call for future large-scale prospective studies in the field of facial palsy surgery to further improve the quality of care and optimize perioperative protocols.

Diagnosing lagophthalmos using artificial intelligence

Lagophthalmos is the incomplete closure of the eyelids posing the risk of corneal ulceration and blindness. Lagophthalmos is a common symptom of various pathologies. We aimed to program a convolutional neural network to automatize lagophthalmos diagnosis. From June 2019 to May 2021, prospective data acquisition was performed on 30 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany (IRB reference number: 20-2081-101). In addition, comparative data were gathered from 10 healthy patients as the control group. The training set comprised 826 images, while the validation and testing sets consisted of 91 patient images each. Validation accuracy was 97.8% over the span of 64 epochs. The model was trained for 17.3 min. For training and validation, an average loss of 0.304 and 0.358 and a final loss of 0.276 and 0.157 were noted. The testing accuracy was observed to be 93.41% with a loss of 0.221. This study proposes a novel application for rapid and reliable lagophthalmos diagnosis. Our CNN-based approach combines effective anti-overfitting strategies, short training times, and high accuracy levels. Ultimately, this tool carries high translational potential to facilitate the physician's workflow and improve overall lagophthalmos patient care.

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 .

Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery

Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.

Deep Learning for the Assessment of Facial Nerve Palsy: Opportunities and Challenges

Automated evaluation of facial palsy using machine learning offers a promising solution to the limitations of current assessment methods, which can be time-consuming, labor-intensive, and subject to clinician bias. Deep learning-driven systems have the potential to rapidly triage patients with varying levels of palsy severity and accurately track recovery over time. However, developing a clinically usable tool faces several challenges, such as data quality, inherent biases in machine learning algorithms, and explainability of decision-making processes. The development of the eFACE scale and its associated software has improved clinician scoring of facial palsy. Additionally, Emotrics is a semiautomated tool that provides quantitative data of facial landmarks on patient photographs. The ideal artificial intelligence (AI)-enabled system would analyze patient videos in real time, extracting anatomic landmark data to quantify symmetry and movement, and estimate clinical eFACE scores. This would not replace clinician eFACE scoring but would offer a rapid automated estimate of both anatomic data, similar to Emotrics, and clinical severity, similar to the eFACE. This review explores the current state of facial palsy assessment, recent advancements in AI, and the opportunities and challenges in developing an AI-driven solution.

ChatGPT's quiz skills in different otolaryngology subspecialties: an analysis of 2576 single-choice and multiple-choice board certification preparation questions

PURPOSE

With the increasing adoption of artificial intelligence (AI) in various domains, including healthcare, there is growing acceptance and interest in consulting AI models to provide medical information and advice. This study aimed to evaluate the accuracy of ChatGPT's responses to practice quiz questions designed for otolaryngology board certification and decipher potential performance disparities across different otolaryngology subspecialties.

METHODS

A dataset covering 15 otolaryngology subspecialties was collected from an online learning platform funded by the German Society of Oto-Rhino-Laryngology, Head and Neck Surgery, designed for board certification examination preparation. These questions were entered into ChatGPT, with its responses being analyzed for accuracy and variance in performance.

RESULTS

The dataset included 2576 questions (479 multiple-choice and 2097 single-choice), of which 57% (n = 1475) were answered correctly by ChatGPT. An in-depth analysis of question style revealed that single-choice questions were associated with a significantly higher rate (p < 0.001) of correct responses (n = 1313; 63%) compared to multiple-choice questions (n = 162; 34%). Stratified by question categories, ChatGPT yielded the highest rate of correct responses (n = 151; 72%) in the field of allergology, whereas 7 out of 10 questions (n = 65; 71%) on legal otolaryngology aspects were answered incorrectly.

CONCLUSION

The study reveals ChatGPT's potential as a supplementary tool for otolaryngology board certification preparation. However, its propensity for errors in certain otolaryngology areas calls for further refinement. Future research should address these limitations to improve ChatGPT's educational use. An approach, with expert collaboration, is recommended for the reliable and accurate integration of such AI models.

Histomorphometry of the Sural Nerve for Use as a CFNG in Facial Reanimation Procedures

Facial palsy (FP) is a debilitating nerve pathology. Cross Face Nerve Grafting (CFNG) describes a surgical technique that uses nerve grafts to reanimate the paralyzed face. The sural nerve has been shown to be a reliable nerve graft with little donor side morbidity. Therefore, we aimed to investigate the microanatomy of the sural nerve. Biopsies were obtained from 15 FP patients who underwent CFNG using sural nerve grafts. Histological cross-sections were fixated, stained with PPD, and digitized. Histomorphometry and a validated software-based axon quantification were conducted. The median age of the operated patients was 37 years (5-62 years). There was a significant difference in axonal capacity decrease towards the periphery when comparing proximal vs. distal biopsies (p = 0.047), while the side of nerve harvest showed no significant differences in nerve caliber (proximal p = 0.253, distal p = 0.506) and axonal capacity for proximal and distal biopsies (proximal p = 0.414, distal p = 0.922). Age did not correlate with axonal capacity (proximal: R = -0.201, p = 0.603; distal: R = 0.317, p = 0.292). These novel insights into the microanatomy of the sural nerve may help refine CFNG techniques and individualize FP patient treatment plans, ultimately improving overall patient outcomes.

DeepSmile: Anomaly Detection Software for Facial Movement Assessment

Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. Current methods of assessing facial palsy are either (i) objective but inaccurate, (ii) subjective and, thus, depending on the clinician's level of experience, or (iii) based on static data. To address the aforementioned problems, we implemented a deep learning algorithm to assess facial movements during smiling. Such a model was trained on a dataset that contains healthy smiles only following an anomaly detection strategy. Generally speaking, the degree of anomaly is computed by comparing the model's suggested healthy smile with the person's actual smile. The experimentation showed that the model successfully computed a high degree of anomaly when assessing the patients' smiles. Furthermore, a graphical user interface was developed to test its practical usage in a clinical routine. In conclusion, we present a deep learning model, implemented on open-source software, designed to help clinicians to assess facial movements.