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Objective To systematically review the progress in the application of artificial intelligence in the field of pediatric otolaryngology-head and neck surgery at home and abroad， aiming to provide reference for the development and clinical practice in the future. Methods Literature related to the application of artificial intelligence in otolaryngology，head and neck surgery were searched from PubMed， Web of Science and Embase databases in June 2024， regardless of the research type， publication date and language restrictions. Subsequently， using the National Heart， Lung， and Blood Institute Study Quality Assessment Tools （NHI-SQAT） and the Oxford 2011 Levels of Evidence （LOE）， tables were designed to assess the quality of the articles and select authentic and effective literature. Literature content was extracted， and a systematic review of current status and future prospects of artificial intelligence in this field was conducted. Results After preliminary screening of abstracts and titles， full-text reading， supplementary retrieval， and exclusion of literature that did not meet quality standards， a total of 38 articles were finally included. Artificial intelligence has been widely used in the diagnosis and treatment of pediatric otolaryngological diseases in various forms. Significant clinical effects have been achieved through methods such as machine learning and big data processing， enabling surgical assistance and the establishment of diagnostic and treatment models. In recent research， the diagnosis and treatment of pediatric otitis media， assessment of pediatric hearing loss， preoperative planning and postoperative guidance， and telemedicine are becoming the main directions of exploration and application of artificial intelligence. Conclusions Application of artificial intelligence in pediatric otolaryngology head and neck surgery at home and abroad has been gradually extended in recent years. As the use of artificial intelligence in assisted diagnosis and treatment has been gradually accepted by the medical community， the application platform for artificial intelligence will become diversified in the future.

Objective: This study aims to evaluate the accuracy of ChatGPT in answering allergic rhinitis (AR) and chronic rhinosinusitis (CRS) related questions. Study Design: This is a cross‐sectional study. Setting: Each question was inputted as a separate, independent prompt. Methods: Responses to AR (n = 189) and CRS (n = 242) related questions, generated by GPT‐3.5 and GPT‐4, were independently graded for accuracy by 2 senior rhinology professors, with disagreements adjudicated by a third reviewer. Results: Overall, ChatGPT demonstrated a satisfactory performance, accurately answering over 80% of questions across all categories. Specifically, GPT‐4.0's accuracy in responding to AR‐related questions significantly exceeded that of GPT‐3.5, but distinction not evident in CRS‐related questions. Patient‐originated questions had a significantly higher accuracy compared to doctor‐originated questions when utilizing GPT‐4.0 to respond to AR‐related questions. This discrepancy was not observed with GPT‐3.5 or in the context of CRS‐related questions. Across different types of content, ChatGPT excelled in covering basic knowledge, prevention, and emotion for AR and CRS. However, it experienced challenges when addressing questions about recent advancements, a trend consistent across both GPT‐3.5 and GPT‐4.0 iterations. Importantly, the accuracy of responses remained unaffected when questions were posed in Chinese. Conclusion: Our findings suggest ChatGPT's capability to convey accurate information for AR and CRS patients, and offer insights into its performance across various domains, guiding its utilization and improvement. [ABSTRACT FROM AUTHOR]

Introduction: Small airway dysfunction (SAD) is associated with type 2 inflammation in patients who have non-asthmatic chronic rhinosinusitis with nasal polyps (CRSwNPs); however, the risk factors for abnormal small airway function indicators in CRSwNP patients with and without asthma remain unclear. Methods: We retrospectively analyzed 41 asthmatic and 109 non-asthmatic CRSwNP patients. Clinical characteristics were compared between groups, correlations between small airway function and clinical parameters were calculated, and independent risk factors for every small airway indicator were identified in each group. Results: Asthmatic CRSwNP patients had significantly reduced small airway function, and the proportion of patients with SAD was higher in asthmatic CRSwNP patients (65.85%) than in patients without asthma (9.17%). With regard to specific airway function indicators, age and a patient's blood eosinophil (%) were identified as independent risk factors for lower FEF50% %pred and FEF25–75% pred, with age being an independent risk factor for FEF75% %pred in asthmatic CRSwNP patients. In non-asthmatic CRSwNP patients, allergic rhinitis comorbidity was found to be an independent risk factor for FEF50% %pred, FEF75% %pred, and FEF25–75% %pred. Conclusion: Physicians should pay greater attention to risk factors for abnormal small airway function indicators in patients with CRSwNPs to prevent the occurrence of SAD. [ABSTRACT FROM AUTHOR]

Objective: To update the literature and provide a systematic review of image‐based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges. Data Sources: Web of Science, Embase, PubMed, and Cochrane Library. Review Methods: Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies. Results: Overall, 686 studies were identified. After screening titles and abstracts, 325 full‐text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively. Conclusion: This state‐of‐the‐art review aimed to highlight the increasing applications of image‐based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real‐world clinical practice. Future studies should consider 3‐dimensional (3D)‐based AI, such as 3D surgical AI. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the changes in the peripheral specific IgE level, distribution of TCR Vg and Vd subfamily T cells and mRNA expressions of TCR Vg I-III following specific immunotherapy (SIT) with house-dust-mite extract in allergic rhinitis (AR) patients. Ten AR patients undergoing SIT with house-dust-mite extract for 1 year were recruited. Quantitative analysis of immunofluorescence was performed to detect the serum specific IgE (sIgE) level before and after SIT; RT-PCR-genescan analysis was employed to detect the mRNA expressions of TCR Vg (I-III) and Vd (1-8) in the peripheral mononuclear cells followed by analysis of T cell clonality. Real-time quantitative PCR was applied to detect the expressions of TCR Vg I-III genes. Ten healthy volunteers served as controls. For AR patients, SIT treatment could improve the symptoms, but the serum sIgE level was not markedly decreased. Before SIT, the expressions of TCR Vg I-III gene were similar between AR patients and controls (P>0.05) but markedly decreased after SIT in AR patients (P

The present study aimed to investigate the expressions of interleukin-17 (IL-17), IL-21 and IL-23 in the serum of allergic rhinitis (AR) patients and to explore their relationship with special IgE (sFgE) in the serum. AR patients (n=24) and healthy subjects (n=12) were recruited and serum samples were collected. The serum level of IgE specific for inhalant allergens was determined using the automatic quantitative immunofluo rescence analysis system, and the contents of IL-17, IL-21 and IL-23 in the serum were detected using ELISA. The level of serum IgE in the healthy individuals was categorized as grade 0 and that in the AR patients as grade 2-6. The mean contents of IL-17, IL-21 and IL-23 were 164.71 ±39.37 pg/mL, 199±97.86 pg/mL and 78.94±26.33 pg/mL, respectively, in the AR patients, and 67.75±18.24 pg/mL, 7.58± 5.49 pg/mL and 13.58± 3.93 pg/mL, respectively, in the healthy subjects. Statistical analysis showed the serum levels of IL-17 and IL-23 in the AR patients were markedly higher than those in the healthy subjects, however, no significant difference was noted in the content of IL-21. Furthermore, the IL-17 level was positively related to the levels of IL-23 and IgE and the IL-23 level was positively related to the IgE level among AR patients, but no relations were observed between the IL-21 level and levels of IL-17, IL-23 and IgE. Our study indicates IL-17 and IL-23 may play an important role in the pathogenesis of AR and maybe IL-21 is not involved in the occurrence of AR.

Indoor positioning systems are becoming increasing popular recently. While most existing indoor positioning studies focus on improving accuracy, less attention is paid to the latency problem. The traditional fusion algorithm uses the received signal strength‐based (RSS‐Based) positioning result to correct the current position of the system. However, it fails to keep up with the actual moving speed of the user during the navigation. The location retrospective adjustment (LRA) method proposed in this paper uses the RSS‐Based positioning result to correct the past position of the system, which can effectively eliminate positioning delay and improve the real‐time response of navigation. We tested in a 70 m linear promenade and found that the addition of LRA results in a reduction of the positioning error around −0.3 to +0.4 m, which improves 85%. Additionally, the LRA method alleviates the requirements for the immediate response of RSS positioning, and the RSS positioning algorithm can be moved to the cloud. It reduces the download resources and computing load on the mobile phone. The complete indoor navigation application is presented in HTML5 which allows users to navigate without having to download the APP in advance, and it takes only 4–9 s for users to launch the application for the first time. We tested the application in a hospital with a total floor area of 79,000 m2 in 7 buildings. The system achieves an average positioning accuracy of 0.65 m at a long navigation distance of 220 m. To our knowledge, this paper is the first to consider the latency issue in indoor navigation. The proposed LRA approach improves real‐time navigation performance, lightens the computation load on the mobile phone, and allows cloud‐based positioning systems to provide stable and accurate navigation even under poor network quality in crowded areas. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) enables the deployment of distributed machine learning models over the cloud and Edge Devices (EDs) while preserving the privacy of sensitive local data, such as electronic health records. However, despite FL advantages regarding security and flexibility, current constructions still suffer from some limitations. Namely, heavy computation overhead on limited resources EDs, communication overhead in uploading converged local models' parameters to a centralized server for parameters aggregation, and lack of guaranteeing the acquired knowledge preservation in the face of incremental learning over new local data sets. This paper introduces a secure and resource‐friendly protocol for parameters aggregation in federated incremental learning and its applications. In this study, the central server relies on a new method for parameters aggregation called orthogonal gradient aggregation. Such a method assumes constant changes of each local data set and allows updating parameters in the orthogonal direction of previous parameters spaces. As a result, our new construction is robust against catastrophic forgetting, maintains the federated neural network accuracy, and is efficient in computation and communication overhead. Moreover, extensive experiments analysis over several significant data sets for incremental learning demonstrates our new protocol's efficiency, efficacy, and flexibility. [ABSTRACT FROM AUTHOR]