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Rapoport N, Pavelchek C, Michelson AP, Shew MA. Artificial Intelligence in Otology and Neurotology. Otolaryngol Clin North Am 2024; 57:791-802. [PMID: 38871535 DOI: 10.1016/j.otc.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.
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Affiliation(s)
- Nicholas Rapoport
- Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, PO Box 8115, St Louis, MO 63110, USA
| | - Cole Pavelchek
- Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098, USA
| | - Andrew P Michelson
- Department of Pulmonary Critical Care, Washington University School of Medicine, 660 South Euclid Avenue, PO Box 8052-43-14, St Louis, MO 63110, USA; Institute for Informatics, Washington University School of Medicine, St Louis, MO, USA
| | - Matthew A Shew
- Otology & Neurotology, Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, PO Box 8115, St Louis, MO 63110, USA.
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Giacobbe DR, Marelli C, Guastavino S, Signori A, Mora S, Rosso N, Campi C, Piana M, Murgia Y, Giacomini M, Bassetti M. Artificial intelligence and prescription of antibiotic therapy: present and future. Expert Rev Anti Infect Ther 2024:1-15. [PMID: 39155449 DOI: 10.1080/14787210.2024.2386669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/28/2024] [Indexed: 08/20/2024]
Abstract
INTRODUCTION In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Marelli
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Alessio Signori
- Section of Biostatistics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola Rosso
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Michele Piana
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Ylenia Murgia
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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Fang TY, Lin TY, Shen CM, Hsu SY, Lin SH, Kuo YJ, Chen MH, Yin TK, Liu CH, Lo MT, Wang PC. Algorithm-Driven Tele-otoscope for Remote Care for Patients With Otitis Media. Otolaryngol Head Neck Surg 2024; 170:1590-1597. [PMID: 38545686 DOI: 10.1002/ohn.738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/05/2024] [Accepted: 02/29/2024] [Indexed: 05/31/2024]
Abstract
OBJECTIVE The COVID-19 pandemic has spurred a growing demand for telemedicine. Artificial intelligence and image processing systems with wireless transmission functionalities can facilitate remote care for otitis media (OM). Accordingly, this study developed and validated an algorithm-driven tele-otoscope system equipped with Wi-Fi transmission and a cloud-based automatic OM diagnostic algorithm. STUDY DESIGN Prospective, cross-sectional, diagnostic study. SETTING Tertiary Academic Medical Center. METHODS We designed a tele-otoscope (Otiscan, SyncVision Technology Corp) equipped with digital imaging and processing modules, Wi-Fi transmission capabilities, and an automatic OM diagnostic algorithm. A total of 1137 otoscopic images, comprising 987 images of normal cases and 150 images of cases of acute OM and OM with effusion, were used as the dataset for image classification. Two convolutional neural network models, trained using our dataset, were used for raw image segmentation and OM classification. RESULTS The tele-otoscope delivered images with a resolution of 1280 × 720 pixels. Our tele-otoscope effectively differentiated OM from normal images, achieving a classification accuracy rate of up to 94% (sensitivity, 80%; specificity, 96%). CONCLUSION Our study demonstrated that the developed tele-otoscope has acceptable accuracy in diagnosing OM. This system can assist health care professionals in early detection and continuous remote monitoring, thus mitigating the consequences of OM.
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Affiliation(s)
- Te-Yung Fang
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Department of Otolaryngology, Sijhih Cathay General Hospital, New Taipei City, Taiwan
| | - Tse-Yu Lin
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Chung-Min Shen
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Department of Pediatric, Cathay General Hospital, Taipei, Taiwan
| | - Su-Yi Hsu
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
| | - Shing-Huey Lin
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Department of Family and Community Medicine, Cathay General Hospital, Taipei, Taiwan
| | - Yu-Jung Kuo
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Ming-Hsu Chen
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan
| | - Tan-Kuei Yin
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan
| | - Chih-Hsien Liu
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Pa-Chun Wang
- Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
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Aghakhani A, Yousefi M, Yekaninejad MS. Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review. Ann Otol Rhinol Laryngol 2024; 133:268-276. [PMID: 37864312 DOI: 10.1177/00034894231206902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
BACKGROUND Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease. Hence, researchers have attempted to utilize ML models to predict the outcome of patients with sudden sensorineural hearing loss. The objectives of this study were to review the performance of these machine learning models and assess their applicability in real-world settings. METHODS A systematic search was conducted in PubMed, Web of Science and Scopus. Only studies that built machine learning prediction models were included, and studies that used algorithms such as logistic regression only for the purpose of adjusting for confounding variables were excluded. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). RESULTS After screening, a total of 7 papers were eligible for synthesis. In total, these studies built 48 ML models. The most common utilized algorithms were Logistic Regression, Support Vector Machine (SVM) and boosting. The area under the curve of the receiver operating characteristic curve ranged between 0.59 and 0.915. All of the included studies had a high risk of bias; hence there are concerns regarding their applicability. CONCLUSION Although these models showed great performance and promising results, future studies are still needed before these models can be applied in a real-world setting. Future studies should employ multiple cohorts, different feature selection methods, and external validation to further validate the models' applicability.
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Affiliation(s)
- Amirhossein Aghakhani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Yousefi
- Department of Audiology, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
| | - Mir Saeed Yekaninejad
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Kurabi A, Dewan K, Kerschner JE, Leichtle A, Li JD, Santa Maria PL, Preciado D. PANEL 3: Otitis media animal models, cell culture, tissue regeneration & pathophysiology. Int J Pediatr Otorhinolaryngol 2024; 176:111814. [PMID: 38101097 DOI: 10.1016/j.ijporl.2023.111814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/13/2023] [Accepted: 11/29/2023] [Indexed: 12/17/2023]
Abstract
OBJECTIVE To review and summarize recently published key articles on the topics of animal models, cell culture studies, tissue biomedical engineering and regeneration, and new models in relation to otitis media (OM). DATA SOURCE Electronic databases: PubMed, National Library of Medicine, Ovid Medline. REVIEW METHODS Key topics were assigned to the panel participants for identification and detailed evaluation. The PubMed reviews were focused on the period from June 2019 to June 2023, in any of the objective subject(s) or keywords listed above, noting the relevant references relating to these advances with a global overview and noting areas of recommendation(s). The final manuscript was prepared with input from all panel members. CONCLUSIONS In conclusion, ex vivo and in vivo OM research models have seen great advancements in the past 4 years. From the usage of novel genetic and molecular tools to the refinement of in vivo inducible and spontaneous mouse models, to the introduction of a wide array of reliable middle ear epithelium (MEE) cell culture systems, the next five years are likely to experience exponential growth in OM pathophysiology discoveries. Moreover, advances in these systems will predictably facilitate rapid means for novel molecular therapeutic studies.
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Affiliation(s)
- Arwa Kurabi
- Department of Otolaryngology, University of California San Diego, School of Medicine, La Jolla, CA, USA.
| | - Kalyan Dewan
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Joseph E Kerschner
- Department of Otolaryngology and Communication Sciences, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anke Leichtle
- Department of Otorhinolaryngology, University of Luebeck, Luebeck, Germany
| | - Jian-Dong Li
- Center for Inflammation, Immunity and Infection, Institute for Biomedical Sciences, Georgia State University, Atlanta, GA, USA
| | - Peter Luke Santa Maria
- Department of Otolaryngology - Head & Neck Surgery, Stanford University, Stanford, CA, USA
| | - Diego Preciado
- Children's National Hospital, Division of Pediatric Otolaryngology, Washington, DC, USA
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Shim JH, Sunwoo W, Choi BY, Kim KG, Kim YJ. Improving the Accuracy of Otitis Media with Effusion Diagnosis in Pediatric Patients Using Deep Learning. Bioengineering (Basel) 2023; 10:1337. [PMID: 38002461 PMCID: PMC10669592 DOI: 10.3390/bioengineering10111337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Otitis media with effusion (OME), primarily seen in children aged 2 years and younger, is characterized by the presence of fluid in the middle ear, often resulting in hearing loss and aural fullness. While deep learning networks have been explored to aid OME diagnosis, prior work did not often specify if pediatric images were used for training, causing uncertainties about their clinical relevance, especially due to important distinctions between the tympanic membranes of small children and adults. We trained cross-validated ResNet50, DenseNet201, InceptionV3, and InceptionResNetV2 models on 1150 pediatric tympanic membrane images from otoendoscopes to classify OME. When assessed using a separate dataset of 100 pediatric tympanic membrane images, the models achieved mean accuracies of 92.9% (ResNet50), 97.2% (DenseNet201), 96.0% (InceptionV3), and 94.8% (InceptionResNetV2), compared to the seven otolaryngologists that achieved accuracies between 84.0% and 69.0%. The results showed that even the worst-performing model trained on fold 3 of InceptionResNetV2 with an accuracy of 88.0% exceeded the accuracy of the highest-performing otolaryngologist at 84.0%. Our findings suggest that these specifically trained deep learning models can potentially enhance the clinical diagnosis of OME using pediatric otoendoscopic tympanic membrane images.
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Affiliation(s)
- Jae-Hyuk Shim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea
| | - Woongsang Sunwoo
- Department of Otorhinolaryngology-Head and Neck Surgery, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea
| | - Byung Yoon Choi
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea
| | - Kwang Gi Kim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea
| | - Young Jae Kim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea
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Tsilivigkos C, Athanasopoulos M, Micco RD, Giotakis A, Mastronikolis NS, Mulita F, Verras GI, Maroulis I, Giotakis E. Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review. J Clin Med 2023; 12:6973. [PMID: 38002588 PMCID: PMC10672270 DOI: 10.3390/jcm12226973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
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.
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Affiliation(s)
- Christos Tsilivigkos
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Michail Athanasopoulos
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Riccardo di Micco
- Department of Otolaryngology and Head and Neck Surgery, Medical School of Hannover, 30625 Hannover, Germany;
| | - Aris Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Nicholas S. Mastronikolis
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Francesk Mulita
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Georgios-Ioannis Verras
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Ioannis Maroulis
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Evangelos Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
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Wu Q, Wang X, Liang G, Luo X, Zhou M, Deng H, Zhang Y, Huang X, Yang Q. Advances in Image-Based Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery: A Systematic Review. Otolaryngol Head Neck Surg 2023; 169:1132-1142. [PMID: 37288505 DOI: 10.1002/ohn.391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/27/2023] [Accepted: 05/13/2023] [Indexed: 06/09/2023]
Abstract
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.
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Affiliation(s)
- Qingwu Wu
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xinyue Wang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guixian Liang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Luo
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Min Zhou
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Huiyi Deng
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yana Zhang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xuekun Huang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qintai Yang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Ma T, Wu Q, Jiang L, Zeng X, Wang Y, Yuan Y, Wang B, Zhang T. Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace. EAR, NOSE & THROAT JOURNAL 2023:1455613231185074. [PMID: 37515527 DOI: 10.1177/01455613231185074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2023] Open
Abstract
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.
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Affiliation(s)
- Tianyu Ma
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qilong Wu
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Li Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoyun Zeng
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuyao Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yi Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Bingxuan Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianhong Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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