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Halmágyi GM, Akdal G, Welgampola MS, Wang C. Neurological update: neuro-otology 2023. J Neurol 2023; 270:6170-6192. [PMID: 37592138 PMCID: PMC10632253 DOI: 10.1007/s00415-023-11922-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 08/01/2023] [Indexed: 08/19/2023]
Abstract
Much has changed since our last review of recent advances in neuro-otology 7 years ago. Unfortunately there are still not many practising neuro-otologists, so that most patients with vestibular problems need, in the first instance, to be evaluated and treated by neurologists whose special expertise is not neuro-otology. The areas we consider here are mostly those that almost any neurologist should be able to start managing: acute spontaneous vertigo in the Emergency Room-is it vestibular neuritis or posterior circulation stroke; recurrent spontaneous vertigo in the office-is it vestibular migraine or Meniere's disease and the most common vestibular problem of all-benign positional vertigo. Finally we consider the future: long-term vestibular monitoring and the impact of machine learning on vestibular diagnosis.
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Affiliation(s)
- Gábor M Halmágyi
- Neurology Department, Royal Prince Alfred Hospital, Sydney, Australia.
- Central Clinical School, University of Sydney, Sydney, Australia.
| | - Gülden Akdal
- Neurology Department, Dokuz Eylül University Hospital, Izmir, Turkey
- Neurosciences Department, Dokuz Eylül University Hospital, Izmir, Turkey
| | - Miriam S Welgampola
- Neurology Department, Royal Prince Alfred Hospital, Sydney, Australia
- Central Clinical School, University of Sydney, Sydney, Australia
| | - Chao Wang
- Neurology Department, Royal Prince Alfred Hospital, Sydney, Australia
- Central Clinical School, University of Sydney, Sydney, Australia
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Machine Learning in the Management of Lateral Skull Base Tumors: A Systematic Review. JOURNAL OF OTORHINOLARYNGOLOGY, HEARING AND BALANCE MEDICINE 2022. [DOI: 10.3390/ohbm3040007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The application of machine learning (ML) techniques to otolaryngology remains a topic of interest and prevalence in the literature, though no previous articles have summarized the current state of ML application to management and the diagnosis of lateral skull base (LSB) tumors. Subsequently, we present a systematic overview of previous applications of ML techniques to the management of LSB tumors. Independent searches were conducted on PubMed and Web of Science between August 2020 and February 2021 to identify the literature pertaining to the use of ML techniques in LSB tumor surgery written in the English language. All articles were assessed in regard to their application task, ML methodology, and their outcomes. A total of 32 articles were examined. The number of articles involving applications of ML techniques to LSB tumor surgeries has significantly increased since the first article relevant to this field was published in 1994. The most commonly employed ML category was tree-based algorithms. Most articles were included in the category of surgical management (13; 40.6%), followed by those in disease classification (8; 25%). Overall, the application of ML techniques to the management of LSB tumor has evolved rapidly over the past two decades, and the anticipated growth in the future could significantly augment the surgical outcomes and management of LSB tumors.
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Yu F, Wu P, Deng H, Wu J, Sun S, Yu H, Yang J, Luo X, He J, Ma X, Wen J, Qiu D, Nie G, Liu R, Hu G, Chen T, Zhang C, Li H. A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study. J Med Internet Res 2022; 24:e34126. [PMID: 35921135 PMCID: PMC9386585 DOI: 10.2196/34126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/14/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022] Open
Abstract
Background Questionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making. A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular disorders. Objective This study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo. Methods In this multicenter prospective study, patients presenting with vertigo entered a consecutive cohort at their first visit to the ENT and vertigo clinics of 7 tertiary referral centers from August 2019 to March 2021, with a follow-up period of 2 months. All participants completed a diagnostic questionnaire after eligibility screening. Patients who received only 1 final diagnosis by their treating specialists for their primary complaint were included in model development and validation. The data of patients enrolled before February 1, 2021 were used for modeling and cross-validation, while patients enrolled afterward entered external validation. Results A total of 1693 patients were enrolled, with a response rate of 96.2% (1693/1760). The median age was 51 (IQR 38-61) years, with 991 (58.5%) females; 1041 (61.5%) patients received the final diagnosis during the study period. Among them, 928 (54.8%) patients were included in model development and validation, and 113 (6.7%) patients who enrolled later were used as a test set for external validation. They were classified into 5 diagnostic categories. We compared 9 candidate machine learning methods, and the recalibrated model of light gradient boosting machine achieved the best performance, with an area under the curve of 0.937 (95% CI 0.917-0.962) in cross-validation and 0.954 (95% CI 0.944-0.967) in external validation. Conclusions The questionnaire-based light gradient boosting machine was able to predict common vestibular disorders and assist decision-making in ENT and vertigo clinics. Further studies with a larger sample size and the participation of neurologists will help assess the generalization and robustness of this machine learning method.
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Affiliation(s)
- Fangzhou Yu
- Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Peixia Wu
- Nursing Department, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Haowen Deng
- Department of Information Management and Information Systems, Fudan University, Shanghai, China
| | - Jingfang Wu
- Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Fudan University, Shanghai, China.,National Health Commission Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Shan Sun
- National Health Commission Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China.,Institutes of Brain Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Huiqian Yu
- Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Fudan University, Shanghai, China.,National Health Commission Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Jianming Yang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xianyang Luo
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital, Medical College, Xiamen University, Xiamen, China
| | - Jing He
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital, Medical College, Xiamen University, Xiamen, China
| | - Xiulan Ma
- Department of Otolaryngology-Head and Neck Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Junxiong Wen
- Department of Otolaryngology-Head and Neck Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Danhong Qiu
- Department of Otolaryngology, Shanghai Pudong Hospital, Shanghai, China
| | - Guohui Nie
- Department of Otolaryngology, Shenzhen Second People's Hospital, Shenzhen, China
| | - Rizhao Liu
- Department of Otolaryngology, Shenzhen Second People's Hospital, Shenzhen, China
| | - Guohua Hu
- Department of Otolaryngology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tao Chen
- Department of Otolaryngology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Cheng Zhang
- Department of Information Management and Information Systems, Fudan University, Shanghai, China
| | - Huawei Li
- Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Fudan University, Shanghai, China.,National Health Commission Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China.,Institutes of Brain Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China.,Institutes of Biomedical Sciences, Fudan University, Shanghai, China
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Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review. SENSORS 2021; 21:s21227565. [PMID: 34833641 PMCID: PMC8621477 DOI: 10.3390/s21227565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/09/2021] [Accepted: 11/11/2021] [Indexed: 01/23/2023]
Abstract
Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.
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Artificial Intelligence Applications in Otology: A State of the Art Review. Otolaryngol Head Neck Surg 2020; 163:1123-1133. [DOI: 10.1177/0194599820931804] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Objective Recent advances in artificial intelligence (AI) are driving innovative new health care solutions. We aim to review the state of the art of AI in otology and provide a discussion of work underway, current limitations, and future directions. Data Sources Two comprehensive databases, MEDLINE and EMBASE, were mined using a directed search strategy to identify all articles that applied AI to otology. Review Methods An initial abstract and title screening was completed. Exclusion criteria included nonavailable abstract and full text, language, and nonrelevance. References of included studies and relevant review articles were cross-checked to identify additional studies. Conclusion The database search identified 1374 articles. Abstract and title screening resulted in full-text retrieval of 96 articles. A total of N = 38 articles were retained. Applications of AI technologies involved the optimization of hearing aid technology (n = 5; 13% of all articles), speech enhancement technologies (n = 4; 11%), diagnosis and management of vestibular disorders (n = 11; 29%), prediction of sensorineural hearing loss outcomes (n = 9; 24%), interpretation of automatic brainstem responses (n = 5; 13%), and imaging modalities and image-processing techniques (n = 4; 10%). Publication counts of the included articles from each decade demonstrated a marked increase in interest in AI in recent years. Implications for Practice This review highlights several applications of AI that otologists and otolaryngologists alike should be aware of given the possibility of implementation in mainstream clinical practice. Although there remain significant ethical and regulatory challenges, AI powered systems offer great potential to shape how healthcare systems of the future operate and clinicians are key stakeholders in this process.
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Predicting and Weighting the Factors Affecting Workers' Hearing Loss Based on Audiometric Data Using C5 Algorithm. Ann Glob Health 2019; 85. [PMID: 31225964 PMCID: PMC6634330 DOI: 10.5334/aogh.2522] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Introduction: With the extensively spread of industrialization in the world, noise exposure is becoming more prevalent in the industrial settings. The most important and definite harmful effects of sound include hearing loss, both permanent and temporary. Objective: This study was designed aimed to use the C5 algorithm to determine the weight of factors affecting the workers’ hearing loss based on the audiometric data. Methods: This cross-sectional, descriptive, analytical study was conducted in 2018 in a mining industry in southeastern Iran. In this study, workers were divided into three exposed groups with different sound pressure levels (one control group and two case groups). Audiometry was conducted for each group of 50 persons; hence, the total number of subjects was 150. The stages of this study include: 1) selecting factors (predictive) to check and weigh them; 2) conducting the audiometry for both ears; 3) calculating the permanent hearing loss in each ear and permanent hearing loss of both ears; 4) classifying the types of hearing loss; and 5) investigating and determining the weight of factors affecting the hearing loss and their classification based on the C5 algorithm and determining the error and accuracy rate of each model. To assess and determine the factors affecting the hearing loss of workers, the C5 algorithm and IBM SPSS Modeler 18.0 were used. SPSS V.18 was used to analyze the linear regression and paired t-test tests, too. Results: The results showed that in the first model (SPL <70 dBA), the 8KHz frequency with the weight of 31% had the highest effect, the factors of work experience and the frequency of 250Hz each with the weight of 3%, had the least effect, and the accuracy of the model was 100%. In the second model (SPL 70–80 dBA) the frequency of 8KHz with the weight of 21% had the highest effect, the frequency of 250Hz and the working experience each had the lowest effect with the weight of 7% and the accuracy of the model was calculated as 100%. In the third model (SPL >85 dBA), the 4KHz frequency with the weight of 31% had the highest effect, and the work experience with a weight of 1% had the lowest effect, and the accuracy of the model was 94%. In the fourth model, the 4KHz frequency with the weight of 22% had the highest effect and 250Hz and age each with the weight of 8% had the lowest effects; the accuracy of this model was calculated to be 99.05%. Conclusions: During investigating and determining the weight of the factors affecting hearing loss by the C5 algorithm, the high weight and effect of the 4KHz frequency were predicted in hearing loss changes. Considering the high accuracy obtained in this modeling, this algorithm is a suitable and powerful tool for predicting and modeling the hearing loss.
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Huo D, Kou B, Zhou Z, Lv M. A machine learning model to classify aortic dissection patients in the early diagnosis phase. Sci Rep 2019; 9:2701. [PMID: 30804372 PMCID: PMC6389887 DOI: 10.1038/s41598-019-39066-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 10/06/2018] [Indexed: 12/03/2022] Open
Abstract
Aortic dissection is one of the most clinical-challenging and life-threatening cardiovascular diseases associated with high morbidity and mortality. Aortic dissection requires fast diagnosis and timely therapy. Any delay or misdiagnosis can cause severe consequence to aortic dissection patients with even higher mortality. To better help physicians identify the potential dissection within the scope of all misdiagnosed patients, this paper describes a method which is developed with data mining methods for aortic dissection patient classification and prediction in the phase of early diagnosis. Various machine learning algorithms were used to build the models which were all trained and tested on the patient dataset with cross validation. Among them, Bayesian Network model achieved the best performance by predicting at a precision rate of 84.55% with Area Under the Curve (AUC) value of 0.857. On this basis, the Bayesian Network model can help physicians better with early diagnosis of aortic dissection in clinical practice. Beyond this study, more data from diverse regions and the internal pathology can be crucial to further build a universal model with broader predictive power.
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Affiliation(s)
- Da Huo
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, China
- Department of System Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Bo Kou
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, China.
- Department of Otorhinolaryngology-Head & Neck Surgery, The First Affiliated Hospital, Medical School of Xi'an Jiaotong University, Xi'an, 710061, China.
| | - Zhili Zhou
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, China
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ming Lv
- School of Management, Xi'an Jiaotong University, Xi'an, 710049, China
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Exarchos T, Rigas G, Bibas A, Kikidis D, Nikitas C, Wuyts F, Ihtijarevic B, Maes L, Cenciarini M, Maurer C, Macdonald N, Bamiou DE, Luxon L, Prasinos M, Spanoudakis G, Koutsouris D, Fotiadis D. Mining balance disorders' data for the development of diagnostic decision support systems. Comput Biol Med 2016; 77:240-8. [DOI: 10.1016/j.compbiomed.2016.08.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 08/22/2016] [Accepted: 08/23/2016] [Indexed: 10/21/2022]
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Dong C, Wang Y, Zhang Q, Wang N. The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 113:162-174. [PMID: 24176413 DOI: 10.1016/j.cmpb.2013.10.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Revised: 08/12/2013] [Accepted: 10/02/2013] [Indexed: 06/02/2023]
Abstract
Vertigo is a common complaint with many potential causes involving otology, neurology and general medicine, and it is fairly difficult to distinguish the vertiginous disorders from each other accurately even for experienced physicians. Based on comprehensive investigations to relevant characteristics of vertigo, we propose a diagnostic modeling and reasoning methodology using Dynamic Uncertain Causality Graph. The symptoms, signs, findings of examinations, medical histories, etiology and pathogenesis, and so on, are incorporated in the diagnostic model. A modularized modeling scheme is presented to reduce the difficulty in model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. We resort to the "chaining" inference algorithm and weighted logic operation mechanism, which guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal insights into underlying interactions among diseases and symptoms intuitively demonstrate the reasoning process in a graphical manner. These solutions make the conclusions and advices more explicable and convincing, further increasing the objectivity of clinical decision-making. Verification experiments and empirical evaluations are performed with clinical vertigo cases. The results reveal that, even with incomplete observations, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising assistance tool for physicians in diagnosis of vertigo.
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Affiliation(s)
- Chunling Dong
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China; Shandong Normal University, Jinan 250014, China.
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