Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review

Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter Study

RATIONALE AND OBJECTIVES

Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing between NP and IP.

MATERIALS AND METHODS

A total of 1791 patients with nasal benign tumors from two hospitals were retrospectively enrolled. Patients were divided into training, internal test, and external test sets. DL models (3D ResNet, 3D Xception, and HRNet) were employed to identify NP from IP using computed tomography images. Model performance was evaluated via receiver operating characteristic curve analysis, accuracy, sensitivity, and specificity. The best-performing model was compared with radiologists' interpretations. The potential enhancement of radiologists' diagnostic performance using the optimal DL model was investigated. Additionally, proteomics analysis in 70 patients was conducted to elucidate the biological underpinnings of the DL model.

RESULTS

The 3D Xception model emerged as the best-performing DL model, achieving the highest area under the receiver operating characteristic curve of 0.999 (95% confidence interval [CI]: 0.950-1.000) in the training set, 0.981 (95% CI: 0.950-1.000) in the internal test set, and 0.933 (95% CI: 0.9099-0.9557) in the external test set. The sensitivity and specificity of the optimal DL model surpassed those of the four radiologists. Furthermore, the DL model improved average radiologist sensitivity from 0.845 to 0.884 and specificity from 0.670 to 0.840. Proteomic analysis revealed an association between the model predictions and epithelial cell differentiation.

CONCLUSION

DL based on CT images holds promise for distinguishing between NP and IP lesions, thereby augmenting clinicians' interpretation capabilities.

American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Report on Artificial Intelligence

This report synthesizes the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Task Force's guidance on the integration of artificial intelligence (AI) in otolaryngology-head and neck surgery (OHNS). A comprehensive literature review was conducted, focusing on the applications, benefits, and challenges of AI in OHNS, alongside ethical, legal, and social implications. The Task Force, formulated by otolaryngologist experts in AI, used an iterative approach, adapted from the Delphi method, to prioritize topics for inclusion and to reach a consensus on guiding principles. The Task Force's findings highlight AI's transformative potential for OHNS, offering potential advancements in precision medicine, clinical decision support, operational efficiency, research, and education. However, challenges such as data quality, health equity, privacy concerns, transparency, regulatory gaps, and ethical dilemmas necessitate careful navigation. Incorporating AI into otolaryngology practice in a safe, equitable, and patient-centered manner requires clinician judgment, transparent AI systems, and adherence to ethical and legal standards. The Task Force principles underscore the importance of otolaryngologists' involvement in AI's ethical development, implementation, and regulation to harness benefits while mitigating risks. The proposed principles inform the integration of AI in otolaryngology, aiming to enhance patient outcomes, clinician well-being, and efficiency of health care delivery.

The Future of Odontogenic Sinusitis

This article discusses the exciting future of odontogenic sinusitis (ODS) in the context of recent advancements in ODS understanding. It emphasizes the importance of integrating ODS into the broader framework of sinonasal diseases and highlights the need for multidisciplinary collaboration among otolaryngologists and dental specialists to optimize clinical outcomes, research, and education. Key challenges include refining dental and sinus pathophysiologic understandings, establishing widely accepted diagnostic criteria, and optimizing multidisciplinary treatment pathways. The article provides also some tips for how to develop interdisciplinary networks both to improve clinical care and research endeavors.

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis

PURPOSE

To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.

METHODS

Data from 1539 adult chronic rhinosinusitis (CRS) patients who underwent paranasal sinus computed tomography (CT) were collected. The overall dataset consisted of 254 MFB cases and 1285 non-MFB cases. The CT images were constructed and labeled to form the deep learning models. Seventy percent of the images were used for training the deep-learning models, and 30% were used for testing. Whole image analysis and instance segmentation analysis were performed using three different architectures: MobileNetv3, ResNet50, and ResNet101 for whole image analysis, and YOLOv5X-SEG, YOLOv8X-SEG, and YOLOv9-C-SEG for instance segmentation analysis. The ROC curve was assessed. Accuracy, sensitivity (recall), specificity, precision, and F1-score were compared between the models and a rhinologist. Kappa agreement was evaluated.

RESULTS

Whole image analysis showed lower precision, recall, and F1-score compared to instance segmentation. The models exhibited an area under the ROC curve of 0.86 for whole image analysis and 0.88 for instance segmentation. In the testing dataset for whole images, the MobileNet V3 model showed 81.00% accuracy, 47.40% sensitivity, 87.90% specificity, 66.80% precision, and a 67.20% F1 score. Instance segmentation yielded the best evaluation with YOLOv8X-SEG showing 94.10% accuracy, 85.90% sensitivity, 95.80% specificity, 88.90% precision, and an 89.80% F1-score. The rhinologist achieved 93.5% accuracy, 84.6% sensitivity, 95.3% specificity, 78.6% precision, and an 81.5% F1-score.

CONCLUSION

Utilizing paranasal sinus CT imaging with enhanced localization and constructive instance segmentation in deep learning models can be the practical promising deep learning system in assisting physicians for diagnosing maxillary fungal ball.

Efficacy and safety of middle turbinate surgery: a systematic review

PURPOSE

Middle turbinate (MT) surgery is extremely common during endoscopic sinus surgery procedures, though no agreement exists on which techniques provide the best outcomes. This PRISMA-compliant systematic review aims to assess which MT surgery technique yields the least postoperative adverse effects and the best objective and subjective outcomes.

METHODS

A comprehensive search criteria was conducted in multiple databases up to July 3, 2023, to identify studies reporting surgical treatments of the MT. After screening and quality assessment, 14 articles were included for analysis. Data on patients demographics, surgical approaches, postoperative treatment and follow-up, objective and subjective outcomes were extracted and reviewed.

RESULTS

Out of 173 unique papers identified, 14 articles met the inclusion criteria, predominantly randomized controlled trials (n = 9). Antero-inferior middle turbinectomy was the predominant surgical approach. Most studies evaluated results with postoperative endoscopy, a superior outcome was documented in the intervention group (ten out of eleven cases). In four out five studies using the SNOT-22, the treatment group was associated with a statistically significant improvement. Olfactory questionnaires highlighted superior olfactory outcome in two out of three studies. The UPSIT score revealed no significant difference between groups. Objective olfactory assessments favored treatment groups in both studies utilizing olfactometry.

CONCLUSIONS

It seems that a partial MT surgical approach consistently yields subjective and objective improvements compared to conservative measures, also suggesting a positive impact on smell function. Despite it appears that better outcomes with fewer complications are consistently achieved with partial techniques, it remains challenging identifying which partial technique surpasses the others, due to significant heterogeneity among the studies.

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models

OBJECTIVES

Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment.

METHODS

This study collected 210 patients underwent IP resection surgery at a university hospital from January 2010 to December 2023. Six machine learning algorithms including ExtraSurvivalTrees (EST), GradientBoostingSurvivalAnalysis (GBSA), RandomSurvivalForest (RSF), SurvivalSVM, Coxnet and Coxph, were used to construct the prediction models. Shapley Additive Explanations (SHAP) values were used to explain the importance of various features in predicting IP recurrence.

RESULTS

We found that the recurrence rate of IP patients is 20.00%, with a median recurrence time of 35.5 months. Multivariate Cox regression analysis identified mild or moderate dysplasia as an independent risk factor for recurrence. The EST model performs the best in predicting postoperative recurrence of IP, with C-index of 0.968 and 0.878 in the training and testing sets. SHAP emphasizes five important predictive factors for recurrence, including bone defects, orbital involvement, smoking, no processing of tumor attachment sites and drinking.

CONCLUSIONS

To our knowledge, this is the first study to use multiple ML models to predict postoperative recurrence of IP. The EST model has the best predictive performance, with SHAP emphasizing several key predictive factors for IP recurrence. This study emphasizes the practicality of machine learning algorithms in predicting IP clinical outcomes, providing valuable insights into the potential for improving clinical decision-making.

Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study

PURPOSE

Accurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based image analysis system capable of segmenting nasal polyps from nasal endoscopy videos.

METHODS

Recorded nasal videoendoscopies from 52 patients diagnosed with CRSwNP between 2019 and 2022 were retrospectively analyzed. Images extracted were manually segmented on the web application Roboflow. A dataset of 342 images was generated and divided into training (80%), validation (10%), and testing (10%) sets. The Ultralytics YOLOv8.0.28 model was employed for automated segmentation.

RESULTS

The YOLOv8s-seg model consisted of 195 layers and required 42.4 GFLOPs for operation. When tested against the validation set, the algorithm achieved a precision of 0.91, recall of 0.839, and mean average precision at 50% IoU (mAP50) of 0.949. For the segmentation task, similar metrics were observed, including a mAP ranging from 0.675 to 0.679 for IoUs between 50% and 95%.

CONCLUSIONS

The study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.

Machine Learning Model Predicts Postoperative Outcomes in Chronic Rhinosinusitis With Nasal Polyps

OBJECTIVE

Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence.

METHODS

We prospectively included patients undergoing first endoscopic sinus surgery (ESS) for nasal polyposis. Preoperative (demographic data, blood eosinophiles, endoscopy, Lund-Mackay, SNOT-22 and depression PHQ scores) and follow-up data was standardly collected. Outcome measures included SNOT-22, PHQ-9 and endoscopy perioperative sinus endoscopy (POSE) scores and two different microRNAs (miR-125b, miR-203a-3p) from polyp tissue. Based on POSE score, three labels were created (controlled: 0-7; partial control: 8-15; or relapse: 16-32). Patients were divided into train and test groups and using Random Forest, we developed algorithms for predicting ESS related outcomes.

RESULTS

Based on data collected from 85 patients, the proposed Machine Learning-approach predicted whether the patient would present control, partial control or relapse of nasal polyposis at 18 months following ESS. The algorithm predicted ESS outcomes with an accuracy between 69.23% (for non-invasive input parameters) and 84.62% (when microRNAs were also included). Additionally, miR-125b significantly improved the algorithm's accuracy and ranked as one of the most important algorithm variables.

CONCLUSION

We propose a Machine Learning algorithm which could change the prediction of disease course in CRSwNP.

Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence

This systematic literature review aims to study the role and impact of artificial intelligence (AI) in transforming Ear, Nose, and Throat (ENT) healthcare. It aims to compare and analyse literature that applied AI algorithms for ENT disease prediction and detection based on their effectiveness, methods, dataset, and performance. We have also discussed ENT specialists' challenges and AI's role in solving them. This review also discusses the challenges faced by AI researchers. This systematic review was completed using PRISMA guidelines. Data was extracted from several reputable digital databases, including PubMed, Medline, SpringerLink, Elsevier, Google Scholar, ScienceDirect, and IEEExplore. The search criteria included studies recently published between 2018 and 2024 related to the application of AI for ENT healthcare. After removing duplicate studies and quality assessments, we reviewed eligible articles and responded to the research questions. This review aims to provide a comprehensive overview of the current state of AI applications in ENT healthcare. Among the 3257 unique studies, 27 were selected as primary studies. About 62.5% of the included studies were effective in providing disease predictions. We found that Pretrained DL models are more in application than CNN algorithms when employed for ENT disease predictions. The accuracy of models ranged between 75 and 97%. We also observed the effectiveness of conversational AI models such as ChatGPT in the ENT discipline. The research in AI for ENT is advancing rapidly. Most of the models have achieved accuracy above 90%. However, the lack of good-quality data and data variability limits the overall ability of AI models to perform better for ENT disease prediction. Further research needs to be conducted while considering factors such as external validation and the issue of class imbalance.

Multiple instance learning for eosinophil quantification of sinonasal histopathology images: A hierarchical determination on whole slide images

KEY POINTS

We proposed a hierarchical framework including an unsupervised candidate image selection and a weakly supervised patch image detection based on multiple instance learning (MIL) to effectively estimate eosinophil quantities in tissue samples from whole slide images. MIL is an innovative approach that can help deal with the variability in cell distribution detection and enable automated eosinophil quantification from sinonasal histopathological images with a high degree of accuracy. The study lays the foundation for further research and development in the field of automated histopathological image analysis, and validation on more extensive and diverse datasets will contribute to real-world application.

Reliability of large language models for advanced head and neck malignancies management: a comparison between ChatGPT 4 and Gemini Advanced

PURPOSE

This study evaluates the efficacy of two advanced Large Language Models (LLMs), OpenAI's ChatGPT 4 and Google's Gemini Advanced, in providing treatment recommendations for head and neck oncology cases. The aim is to assess their utility in supporting multidisciplinary oncological evaluations and decision-making processes.

METHODS

This comparative analysis examined the responses of ChatGPT 4 and Gemini Advanced to five hypothetical cases of head and neck cancer, each representing a different anatomical subsite. The responses were evaluated against the latest National Comprehensive Cancer Network (NCCN) guidelines by two blinded panels using the total disagreement score (TDS) and the artificial intelligence performance instrument (AIPI). Statistical assessments were performed using the Wilcoxon signed-rank test and the Friedman test.

RESULTS

Both LLMs produced relevant treatment recommendations with ChatGPT 4 generally outperforming Gemini Advanced regarding adherence to guidelines and comprehensive treatment planning. ChatGPT 4 showed higher AIPI scores (median 3 [2-4]) compared to Gemini Advanced (median 2 [2-3]), indicating better overall performance. Notably, inconsistencies were observed in the management of induction chemotherapy and surgical decisions, such as neck dissection.

CONCLUSIONS

While both LLMs demonstrated the potential to aid in the multidisciplinary management of head and neck oncology, discrepancies in certain critical areas highlight the need for further refinement. The study supports the growing role of AI in enhancing clinical decision-making but also emphasizes the necessity for continuous updates and validation against current clinical standards to integrate AI into healthcare practices fully.

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

Rhinomanometry: A Comprehensive Review of Its Applications and Advancements in Rhinology Practice

Rhinomanometry is a pivotal diagnostic technique in rhinology, providing a quantitative assessment of nasal airflow and resistance. This review comprehensively examines the historical development, principles and clinical applications of rhinomanometry, emphasising its role in diagnosing nasal obstructions, preoperative evaluations and monitoring therapeutic outcomes. Recent advancements, including the integration with imaging technologies and the application of artificial intelligence (AI), have significantly enhanced the accuracy and utility of rhinomanometry. Despite facing challenges such as technical limitations and the need for standardisation, rhinomanometry remains an invaluable tool in both clinical and research settings. The review also explores future directions, highlighting the potential for device miniaturisation, telemedicine integration, personalised protocols and collaborative research efforts. These advancements will likely expand the accessibility, accuracy and clinical relevance of rhinomanometry, solidifying its importance in the ongoing evolution of rhinology practice.

An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review

PURPOSE

Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools.

METHODS

Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar.

RESULTS

Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability.

CONCLUSIONS

Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial.

LEVEL OF EVIDENCE: 4

Reliability of large language models in managing odontogenic sinusitis clinical scenarios: a preliminary multidisciplinary evaluation

PURPOSE

This study aimed to evaluate the utility of large language model (LLM) artificial intelligence tools, Chat Generative Pre-Trained Transformer (ChatGPT) versions 3.5 and 4, in managing complex otolaryngological clinical scenarios, specifically for the multidisciplinary management of odontogenic sinusitis (ODS).

METHODS

A prospective, structured multidisciplinary specialist evaluation was conducted using five ad hoc designed ODS-related clinical scenarios. LLM responses to these scenarios were critically reviewed by a multidisciplinary panel of eight specialist evaluators (2 ODS experts, 2 rhinologists, 2 general otolaryngologists, and 2 maxillofacial surgeons). Based on the level of disagreement from panel members, a Total Disagreement Score (TDS) was calculated for each LLM response, and TDS comparisons were made between ChatGPT3.5 and ChatGPT4, as well as between different evaluators.

RESULTS

While disagreement to some degree was demonstrated in 73/80 evaluator reviews of LLMs' responses, TDSs were significantly lower for ChatGPT4 compared to ChatGPT3.5. Highest TDSs were found in the case of complicated ODS with orbital abscess, presumably due to increased case complexity with dental, rhinologic, and orbital factors affecting diagnostic and therapeutic options. There were no statistically significant differences in TDSs between evaluators' specialties, though ODS experts and maxillofacial surgeons tended to assign higher TDSs.

CONCLUSIONS

LLMs like ChatGPT, especially newer versions, showed potential for complimenting evidence-based clinical decision-making, but substantial disagreement was still demonstrated between LLMs and clinical specialists across most case examples, suggesting they are not yet optimal in aiding clinical management decisions. Future studies will be important to analyze LLMs' performance as they evolve over time.

Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review

Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords 'otorhinolaryngology', 'imaging', 'computer vision', 'artificial intelligence', and 'deep learning', and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.

Advances in Image-Based Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery: A Systematic Review

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.

Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

Key Clinical Message

Large language models have made artificial intelligence readily available to the general public and potentially have a role in healthcare; however, their use in difficult differential diagnosis is still limited, as demonstrated by a case of necrotizing otitis externa.

Abstract

This case report presents a peculiar case of necrotizing otitis externa (NOE) with skull base involvement which proved diagnostically challenging. The initial patient presentation and the imaging performed on the 78-year-old patient suggested a neoplastic rhinopharyngeal lesion and only after several unsuccessful biopsies the patient was transferred to our unit. Upon re-evaluation of the clinical picture, a clinical hypothesis of NOE with skull base erosion was made and confirmed by identifying Pseudomonas aeruginosa in biopsy specimens of skull base bone and external auditory canal skin. Upon diagnosis confirmation, the patient was treated with culture-oriented long-term antibiotics with complete resolution of the disease. Given the complex clinical presentation, we chose to submit a posteriori this NOE case to two large language models (LLM) to test their ability to handle difficult differential diagnoses. LLMs are easily approachable artificial intelligence tools that enable human-like interaction with the user relying upon large information databases for analyzing queries. The LLMs of choice were ChatGPT-3 and ChatGPT-4 and they were requested to analyze the case being provided with only objective clinical and imaging data.

Otolaryngologist perceptions of AI-based sinus CT interpretation

BACKGROUND

Overcoming non-standardization, vagueness, and subjectivity in sinus CT radiology reports is an ongoing need, particularly in keeping with data-driven healthcare initiatives. Our aim was to explore otolaryngologists' perceptions of quantitative objective disease measures as enabled by AI-based analysis, and determine preferences for sinus CT interpretation.

METHODS

A multi-methods design was used. We administered a survey to American Rhinologic Society members and conducted semi-structured interviews with a purposeful sample of otolaryngologists and rhinologists from varying backgrounds, practice settings and locations during 2020-2021. Interview topics included sinus CT reports, familiarity with AI-based analysis, and potential requisites for its future implementation. Interviews were then coded for content analysis. Differences in survey responses were calculated using Chi-squared test.

RESULTS

120 of 955 surveys were returned, and 19 otolaryngologists (8 rhinologists) were interviewed. Survey data revealed more trust in conventional radiologist reports, but that AI-based reports would be more systematic and comprehensive. Interviews expanded on these results. Interviewees believed that conventional sinus CT reports had limited utility due to inconsistent content. However, they described relying on them for reporting incidental extra-sinus findings. Reporting could be improved with standardization and more detailed anatomical analysis. Interviewees expressed interest in AI-derived analysis given potential for standardization, although they desired evidence of accuracy and reproducibility to gain trust in AI-based reports.

CONCLUSIONS

Sinus CT interpretation has shortcomings in its current state. Standardization and objectivity could be aided with deep learning-enabled quantitative analysis, although clinicians desire thorough validation to gain trust in the technology prior to its implementation.

Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace

BACKGROUND

Otorhinolaryngology diseases are well suited for artificial intelligence (AI)-based interpretation. The use of AI, particularly AI based on deep learning (DL), in the treatment of human diseases is becoming more and more popular. However, there are few bibliometric analyses that have systematically studied this field.

OBJECTIVE

The objective of this study was to visualize the research hot spots and trends of AI and DL in ENT diseases through bibliometric analysis to help researchers understand the future development of basic and clinical research.

METHODS

In all, 232 articles and reviews were retrieved from The Web of Science Core Collection. Using CiteSpace and VOSviewer software, countries, institutions, authors, references, and keywords in the field were visualized and examined.

RESULTS

The majority of these papers came from 44 nations and 498 institutions, with China and the United States leading the way. Common diseases used by AI in ENT include otosclerosis, otitis media, nasal polyps, sinusitis, and so on. In the early years, research focused on the analysis of hearing and articulation disorders, and in recent years mainly on the diagnosis, localization, and grading of diseases.

CONCLUSIONS

The analysis shows the periodical hot spots and development direction of AI and DL application in ENT diseases from the time dimension. The diagnosis and prognosis of otolaryngology diseases and the analysis of otolaryngology endoscopic images have been the focus of current research and the development trend of future.