Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Perceptions of Artificial Intelligence Among Otolaryngologists in Saudi Arabia: A Cross-Sectional Study

Purpose

Otolaryngology has experienced notable advancements and growth in the application of artificial intelligence (AI). However, otolaryngologists' perception of these tools are lacking. This study aims to assess the knowledge and attitudes of otolaryngologists toward AI.

Patients and Methods

A cross-sectional study was conducted among 110 otolaryngologists in the Eastern Province of Saudi Arabia. A piloted questionnaire was used to gather information on knowledge, attitude, and opinions regarding AI. Data analysis was conducted using SPSS version 26.

Results

Of the sample, 60% indicated average perceived knowledge of AI, while approximately 44.5% perceived their AI knowledge in the field of otolaryngology to be below average. A significant positive correlation was identified between knowledge and attitude scores. It was found that a higher knowledge score was more closely associated with seeing more than 15 patients per day, while a higher attitude score was more closely associated with being older, being a consultant, and having more years of professional experience. Of the sample, 38.2% strongly agreed that the application of AI in scientific research should be included in the residency training program.

Conclusion

These findings underscore the importance of incorporating AI tools into certain aspects of the otolaryngology residency training program, highlighting their significance.

Surgical margins in head and neck squamous cell carcinoma: A narrative review

Head and neck squamous cell carcinoma (HNSCC), a prevalent and frequently recurring malignancy, often necessitates surgical intervention. The surgical margin (SM) plays a pivotal role in determining the postoperative treatment strategy and prognostic evaluation of HNSCC. Nonetheless, the process of clinical appraisal and assessment of the SMs remains a complex and indeterminate endeavor, thereby leading to potential difficulties for surgeons in defining the extent of resection. In this regard, we undertake a comprehensive review of the suggested surgical distance in varying circumstances, diverse methods of margin evaluation, and the delicate balance that must be maintained between tissue resection and preservation in head and neck surgical procedures. This review is intended to provide surgeons with pragmatic guidance in selecting the most suitable resection techniques, and in improving patients' quality of life by achieving optimal functional and aesthetic restoration.

Applications and challenges of neural networks in otolaryngology (Review)

Artificial Intelligence (AI) has become a topic of interest that is frequently debated in all research fields. The medical field is no exception, where several unanswered questions remain. When and how this field can benefit from AI support in daily routines are the most frequently asked questions. The present review aims to present the types of neural networks (NNs) available for development, discussing their advantages, disadvantages and how they can be applied practically. In addition, the present review summarizes how NNs (combined with various other features) have already been applied in studies in the ear nose throat research field, from assisting diagnosis to treatment management. Although the answer to this question regarding AI remains elusive, understanding the basics and types of applicable NNs can lead to future studies possibly using more than one type of NN. This approach may bypass the actual limitations in accuracy and relevance of information generated by AI. The proposed studies, the majority of which used convolutional NNs, obtained accuracies varying 70-98%, with a number of studies having the AI trained on a limited number of cases (<100 patients). The lack of standardization in AI protocols for research negatively affects data homogeneity and transparency of databases.

The future of maxillofacial prosthodontics in North America: The role of advanced digital technology and artificial intelligence - A discussion document

STATEMENT OF PROBLEM

Maxillofacial prosthodontists were advanced digital technology (ADT) adopters early in the new Millennium. The past two decades saw a range of digital enablers emerge including digital imaging (internal and surface), digital surgical planning, digital functional assessment, subtractive and additive manufacturing, navigation, and robotics among others. Artificial Intelligence (AI) is the latest ADT arrival that will be a challenging disruptive technology. ADT has served as a profound change agent in maxillofacial prosthodontics. The intent was to explore the process and level of ADT engagement in maxillofacial prosthodontics.

PURPOSE

The purpose was twofold. Firstly, to explore maxillofacial prosthodontic engagement of ADT. Secondly, to develop a discussion document to assist the American Academy of Maxillofacial Prosthetics (AAMP) with establishing a collective awareness and considered opinion on the future of maxillofacial prosthodontics in the digital era.

MATERIAL AND METHODS

AAMP member interest in ADT was assessed through analysis of AAMP annual congress programs and publications in the Journal of Prosthetic Dentistry (JPD). The history of the maxillofacial prosthodontic journey to the digital era was undertaken with a selective literature review. The perceptions maxillofacial prosthodontists hold on ADT engagement was assessed through a survey of AAMP members. Developing an understanding of the influence AI was conducted with a review of pertinent literature.

RESULTS

From 2011-2020, an annual mean of 38% of papers published in the JPD involved clinical use of ADT. From 2017-2019, 44% of invited presentations at AAMP annual congresses included clinical use of ADT. The journey to the digital era distinguished three periods with formative and consolidation periods influencing the innovation digital era. The AAMP member survey had a 59% response rate and studied 10 domains through 31 questions. Of the respondents, 89% thought ADT important to the future of maxillofacial prosthodontics.

CONCLUSIONS

The discussion document will assist the AAMP in developing a collective consciousness and considered opinion on ADT in the future of maxillofacial prosthodontics. Members of the AAMP have a developed interest in clinical applications of ADT. A great challenge is that no formal education, training, or clinical competency requirements for ADT could be identified. Clinical competency requirements are important to prepare maxillofacial prosthodontics for the inevitability of a digital era future. The discussion document poses the fundamental question of whether maxillofacial prosthodontists will remain as passive end users of ADT and AI or will they become engaged knowledge workers that have determined clinical competency in ADT and AI in patient care. Without this knowledge worker role, maxillofacial prosthodontists may experience difficulty being part of the inevitable ADT-AI driven future.

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

The Integration and Impact of Artificial Intelligence in Otolaryngology-Head and Neck Surgery: Navigating the Last Mile

Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. Success depends on interdisciplinary cooperation, consistent innovation, and regulatory compliance to improve clinical outcomes, provider experience, and operational effectiveness.

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.

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.

Artificial intelligence and ChatGPT: An otolaryngology patient's ally or foe?

BACKGROUND

As artificial intelligence (AI) is integrating into the healthcare sphere, there is a need to evaluate its effectiveness in the various subspecialties of medicine, including otolaryngology. Our study intends to provide a cursory review of ChatGPT's diagnostic capability, ability to convey pathophysiology in simple terms, accuracy in providing management recommendations, and appropriateness in follow up and post-operative recommendations in common otolaryngologic conditions.

METHODS

Adenotonsillectomy (T&A), tympanoplasty (TP), endoscopic sinus surgery (ESS), parotidectomy (PT), and total laryngectomy (TL) were substituted for the word procedure in the following five questions and input into ChatGPT version 3.5: "How do I know if I need (procedure)," "What are treatment alternatives to (procedure)," "What are the risks of (procedure)," "How is a (procedure) performed," and "What is the recovery process for (procedure)?" Two independent study members analyzed the output and discrepancies were reviewed, discussed, and reconciled between study members.

RESULTS

In terms of management recommendations, ChatGPT was able to give generalized statements of evaluation, need for intervention, and the basics of the procedure without major aberrant errors or risks of safety. ChatGPT was successful in providing appropriate treatment alternatives in all procedures tested. When queried for methodology, risks, and procedural steps, ChatGPT lacked precision in the description of procedural steps, missed key surgical details, and did not accurately provide all major risks of each procedure. In terms of the recovery process, ChatGPT showed promise in T&A, TP, ESS, and PT but struggled in the complexity of TL, stating the patient could speak immediately after surgery without speech therapy.

CONCLUSIONS

ChatGPT accurately demonstrated the need for intervention, management recommendations, and treatment alternatives in common ENT procedures. However, ChatGPT was not able to replace an otolaryngologist's clinical reasoning necessary to discuss procedural methodology, risks, and the recovery process in complex procedures. As AI becomes further integrated into healthcare, there is a need to continue to explore its indications, evaluate its limits, and refine its use to the otolaryngologist's advantage.

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

Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images

Laryngeal cancer poses a significant global health burden, with late-stage diagnoses contributing to reduced survival rates. This study explores the application of deep convolutional neural networks (DCNNs), specifically the Densenet201 architecture, in the computer-aided diagnosis of laryngeal cancer using laryngoscopic images. Our dataset comprised images from two medical centers, including benign and malignant cases, and was divided into training, internal validation, and external validation groups. We compared the performance of Densenet201 with other commonly used DCNN models and clinical assessments by experienced clinicians. Densenet201 exhibited outstanding performance, with an accuracy of 98.5% in the training cohort, 92.0% in the internal validation cohort, and 86.3% in the external validation cohort. The area under the curve (AUC) values consistently exceeded 92%, signifying robust discriminatory ability. Remarkably, Densenet201 achieved high sensitivity (98.9%) and specificity (98.2%) in the training cohort, ensuring accurate detection of both positive and negative cases. In contrast, other DCNN models displayed varying degrees of performance degradation in the external validation cohort, indicating the superiority of Densenet201. Moreover, Densenet201's performance was comparable to that of an experienced clinician (Clinician A) and outperformed another clinician (Clinician B), particularly in the external validation cohort. Statistical analysis, including the DeLong test, confirmed the significance of these performance differences. Our study demonstrates that Densenet201 is a highly accurate and reliable tool for the computer-aided diagnosis of laryngeal cancer based on laryngoscopic images. The findings underscore the potential of deep learning as a complementary tool for clinicians and the importance of incorporating advanced technology in improving diagnostic accuracy and patient care in laryngeal cancer diagnosis. Future work will involve expanding the dataset and further optimizing the deep learning model.

Accurate Segmentation and Tracking of Chorda Tympani in Endoscopic Middle Ear Surgery with Artificial Intelligence

Objective: We introduce a novel endoscopic middle ear surgery dataset specifically designed for evaluating deep learning (DL)-based semantic segmentation of chorda tympani. Methods: We curated a dataset comprising 8240 images from 25 patients, divided into a training set (20%, 1648 images), validation set (5%, 412 images), and test set (75%, 6180 images). We employed data enhancement techniques to expand the picture size of the training and validation sets by 5 times (training set: 8240 images, verification set: 2060 images). Subsequently, we employed a multistage transfer learning training method to establish, train, and validate various convolutional neural networks. Results: On the validation set of 2060 labeled images, our proposed network achieved good results, with the U-net exhibiting the highest effectiveness (mIOU = 0.8737, mPA = 0.9263). Furthermore, when applied to the test dataset of 6180 raw images and contrasted with the prediction of otologists, the overall performance of the U-net was excellent (accuracy = 0.911, precision = 0.9823, sensitivity = 0.8777, specificity = 0.9714). Conclusions: Our findings demonstrate that DL can be successfully employed for automatic segmentation of chorda tympani in endoscopic middle ear surgery, yielding high-performance results. This study validates the potential feasibility of future intelligent navigation technologies to assist in endoscopic middle ear surgery.

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.

Diagnosis of Salivary Gland Tumors Using Transfer Learning with Fine-Tuning and Gradual Unfreezing

Ultrasound is the primary tool for evaluating salivary gland tumors (SGTs); however, tumor diagnosis currently relies on subjective features. This study aimed to establish an objective ultrasound diagnostic method using deep learning. We collected 446 benign and 223 malignant SGT ultrasound images in the training/validation set and 119 benign and 44 malignant SGT ultrasound images in the testing set. We trained convolutional neural network (CNN) models from scratch and employed transfer learning (TL) with fine-tuning and gradual unfreezing to classify malignant and benign SGTs. The diagnostic performances of these models were compared. By utilizing the pretrained ResNet50V2 with fine-tuning and gradual unfreezing, we achieved a 5-fold average validation accuracy of 0.920. The diagnostic performance on the testing set demonstrated an accuracy of 89.0%, a sensitivity of 81.8%, a specificity of 91.6%, a positive predictive value of 78.3%, and a negative predictive value of 93.2%. This performance surpasses that of other models in our study. The corresponding Grad-CAM visualizations were also presented to provide explanations for the diagnosis. This study presents an effective and objective ultrasound method for distinguishing between malignant and benign SGTs, which could assist in preoperative evaluation.

Developing and validating a multivariable machine learning model for the preoperative prediction of lateral lymph node metastasis of papillary thyroid cancer

Background

At present, preoperative diagnosis of lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) mostly depends on the training and expertise of ultrasound doctors. A machine-learning model for predicting LLNM accurately before PTC surgery may help to determine the scope of surgery and reduce unnecessary surgical trauma.

Methods

The data of patients with primary PTC who underwent thyroidectomy with lateral cervical lymph node surgery at Beijing Tongren Hospital between July 2009 and June 2021 were retrospectively analyzed. All patients had complete ultrasonic examination, clinical data, and definite pathology diagnosis of lymph nodes. LLNM was confirmed by postoperative pathology. The patients were randomly divided into a training set (155 cases) and a test set (98 cases) at a ratio of 6:4. Eleven parameters, including patient demographics, ultrasound results, and tumor-related conditions, were collected, and a prediction model was established using the support vector machine (SVM) algorithm. Several other machine-learning algorithms were also used to establish models for comparison. The accuracy, precision, recall, F1-score, sensitivity, specificity, Cohen's kappa value, and area under the receiver operating characteristic curve (AUC) were used to evaluate model performance.

Results

A total of 87 males and 156 females were included in the study, aged 14-80 years. One hundred and four patients of them had LLNM and 139 did not have LLNM. The pandas Python library was used for the statistical analysis, and the Spearman coefficient was used to analyze the correlation between each parameter and the prediction index. The SVM model performed the best among all the models. Its accuracy, precision, recall, F1-score, sensitivity, specificity, Cohen's kappa value, and AUC were 90.8%, 91.0%, 90.8%, 90.8%, 87.5%, 94.0%, 81.6%, and 91.0%, respectively.

Conclusions

This model can enable surgeons to improve the accuracy of ultrasonography in predicting LLNM without additional examination, thus avoiding missing positive lateral cervical lymph nodes and reducing the sequelae caused by unnecessary lateral neck dissection.

Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features

Nasal polyps are edematous polypoid masses covered by smooth, gray, shiny, soft and gelatinous mucosa. They often pose a threat for the patients to result in allergic rhinitis, sinus infections and asthma. The aim of this paper is to design a reliable rhinology assistance system for recognizing the nasal polyps in endoscopic videos. We introduce NP-80, a novel dataset that contains high-quality endoscopy video-frames of 80 participants with and without nasal polyps (NP). We benchmark vanilla machine learning and deep learning-based classifiers on the proposed dataset with respect to robustness and accuracy. We conduct a series of classification experiments and an exhaustive empirical comparison on handcrafted features (texture features -Local Binary Patterns (LBP) and shape features- Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) features for recognizing nasal polyps automatically. The classification experiments are carried out by K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT) and CNN classifiers. The best obtained precision, recall, and accuracy rates are 99%, 98%, and 98.3%, respectively. The classifier methods built with handcrafted features have shown poor recognition performance (best accuracy of %96.3) from the proposed CNN classifier (best accuracy of %98.3). The empirical results of the proposed learning techniques on NP-80 dataset are promising to support clinical decision systems. We make our dataset publicly available to encourage further research on rhinology experiments. The major research objective accomplished in this study is the creation of a high-accuracy deep learning based nasal polyps classification model using easily obtainable portable rhino fiberoscope images to be integrated into an otolaryngologist decision support system. We conclude from the research that using appropriate image processing techniques along with suitable deep learning networks allow researchers to obtain high accuracy recommendations in identifying nasal polyps. Furthermore, the results from the study encourages us to develop deep learning models for various other medical conditions.

Vestibular Deficits in Deafness: Clinical Presentation, Animal Modeling, and Treatment Solutions

The inner ear is responsible for both hearing and balance. These functions are dependent on the correct functioning of mechanosensitive hair cells, which convert sound- and motion-induced stimuli into electrical signals conveyed to the brain. During evolution of the inner ear, the major changes occurred in the hearing organ, whereas the structure of the vestibular organs remained constant in all vertebrates over the same period. Vestibular deficits are highly prevalent in humans, due to multiple intersecting causes: genetics, environmental factors, ototoxic drugs, infections and aging. Studies of deafness genes associated with balance deficits and their corresponding animal models have shed light on the development and function of these two sensory systems. Bilateral vestibular deficits often impair individual postural control, gaze stabilization, locomotion and spatial orientation. The resulting dizziness, vertigo, and/or falls (frequent in elderly populations) greatly affect patient quality of life. In the absence of treatment, prosthetic devices, such as vestibular implants, providing information about the direction, amplitude and velocity of body movements, are being developed and have given promising results in animal models and humans. Novel methods and techniques have led to major progress in gene therapies targeting the inner ear (gene supplementation and gene editing), 3D inner ear organoids and reprograming protocols for generating hair cell-like cells. These rapid advances in multiscale approaches covering basic research, clinical diagnostics and therapies are fostering interdisciplinary research to develop personalized treatments for vestibular disorders.

Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection

OBJECTIVES

To assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) deep learning convolutional neural network (CNN).

STUDY DESIGN

Experimental study with retrospective data.

METHODS

Recorded videos of LSCC were retrospectively collected from in-office transnasal videoendoscopies and intraoperative rigid endoscopies. LSCC videoframes were extracted for training, validation, and testing of various YOLO models. Different techniques were used to enhance the image analysis: contrast limited adaptive histogram equalization, data augmentation techniques, and test time augmentation (TTA). The best-performing model was used to assess the automatic detection of LSCC in six videolaryngoscopies.

RESULTS

Two hundred and nineteen patients were retrospectively enrolled. A total of 624 LSCC videoframes were extracted. The YOLO models were trained after random distribution of images into a training set (82.6%), validation set (8.2%), and testing set (9.2%). Among the various models, the ensemble algorithm (YOLOv5s with YOLOv5m-TTA) achieved the best LSCC detection results, with performance metrics in par with the results reported by other state-of-the-art detection models: 0.66 Precision (positive predicted value), 0.62 Recall (sensitivity), and 0.63 mean Average Precision at 0.5 intersection over union. Tests on the six videolaryngoscopies demonstrated an average computation time per videoframe of 0.026 seconds. Three demonstration videos are provided.

CONCLUSION

This study identified a suitable CNN model for LSCC detection in WL and NBI videolaryngoscopies. Detection performances are highly promising. The limited complexity and quick computational times for LSCC detection make this model ideal for real-time processing.

LEVEL OF EVIDENCE

3 Laryngoscope, 2021.

[Artificial intelligence in otorhinolaryngology]

Hintergrund

Die fortschreitende Digitalisierung ermöglicht zunehmend den Einsatz von künstlicher Intelligenz (KI). Sie wird Gesellschaft und Medizin in den nächsten Jahren maßgeblich beeinflussen.

Ziel der Arbeit

Darstellung des gegenwärtigen Einsatzspektrums von KI in der Hals-Nasen-Ohren-Heilkunde und Skizzierung zukünftiger Entwicklungen bei der Anwendung dieser Technologie.

Material und Methoden

Es erfolgte die Auswertung und Diskussion wissenschaftlicher Studien und Expertenanalysen.

Ergebnisse

Durch die Verwendung von KI kann der Nutzen herkömmlicher diagnostischer Werkzeuge in der Hals-Nasen-Ohren-Heilkunde gesteigert werden. Zudem kann der Einsatz dieser Technologie die chirurgische Präzision in der Kopf-Hals-Chirurgie weiter erhöhen.

Schlussfolgerungen

KI besitzt ein großes Potenzial zur weiteren Verbesserung diagnostischer und therapeutischer Verfahren in der Hals-Nasen-Ohren-Heilkunde. Allerdings ist die Anwendung dieser Technologie auch mit Herausforderungen verbunden, beispielsweise im Bereich des Datenschutzes.

Managing head and neck cancer patients during the COVID-19 pandemic: the experience of a tertiary referral center in southern Italy

BACKGROUND

The medical community has been deeply involved in fighting the Coronavirus disease 2019 (COVID-19) pandemic and, as a consequence, the care of non-COVID-19 patients has been impacted. However, the treatment of head and neck cancer patients is not deferrable, and an integrated strategy is required. The aim of the current article is to present the experience in the management of head and neck patients during the COVID-19 pandemic at the research hospital "Casa Sollievo della Sofferenza". This review contains replicable and widely usable instructions on how to avoid delays in the diagnosis and treatment of head and neck tumors and to ensure a gradual return to elective procedures.

MAIN TEXT

The Head and Neck Department of the research hospital "Casa Sollievo della Sofferenza" includes an Otolaryngology and a Maxillofacial Surgery Unit, both of which deal with the diagnosis and treatment of benign and malignant pathologies of the head and neck, as well as urgent/emergent consultations and surgical procedures that necessitate time sensitive operative management, such as cochlear implantation (CI). Given these premises and the complexity of the Department, the "COVID-19 organizing protocol" of the research hospital "Casa Sollievo della Sofferenza" was divided into two phases in accordance with the different stages of the pandemic and the priority of treatment. Special attention was given to the medical surveillance of health care workers and hospitalized patients, to the organization of the outpatient clinic and the operating setting as well as to the implementation of telehealth systems.

CONCLUSIONS

The COVID-19 pandemic is going to be a long-term situation with lasting effects on the public health and the entire society. Therefore, an efficient health care system has to adopt a double strategy: always being ready for a "new wave" of the pandemic and not forgetting non-COVID-19 patients, among whom head and neck cancer patients represent a priority. More than 1 year since the first outbreak in Wuhan, this review offers a unique and helpful perspective that incorporates awareness of the disease.