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Huang AE, Valdez TA. Artificial Intelligence and Pediatric Otolaryngology. Otolaryngol Clin North Am 2024:S0030-6665(24)00069-0. [PMID: 39033065 DOI: 10.1016/j.otc.2024.04.011] [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: 07/23/2024]
Abstract
Artificial intelligence (AI) studies show how to program computers to simulate human intelligence and perform data interpretation, learning, and adaptive decision-making. Within pediatric otolaryngology, there is a growing body of evidence for the role of AI in diagnosis and triaging of acute otitis media and middle ear effusion, pediatric sleep disorders, and syndromic craniofacial anomalies. The use of automated machine learning with robotic devices intraoperatively is an evolving field of study, particularly in the realms of pediatric otologic surgery and computer-aided planning for maxillofacial reconstruction, and we will likely continue seeing novel applications of machine learning in otolaryngologic surgery.
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Affiliation(s)
- Alice E Huang
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Tulio A Valdez
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA.
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Zhou Z, Pandey R, Valdez TA. Label-Free Optical Technologies for Middle-Ear Diseases. Bioengineering (Basel) 2024; 11:104. [PMID: 38391590 PMCID: PMC10885954 DOI: 10.3390/bioengineering11020104] [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: 11/17/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/24/2024] Open
Abstract
Medical applications of optical technology have increased tremendously in recent decades. Label-free techniques have the unique advantage of investigating biological samples in vivo without introducing exogenous agents. This is especially beneficial for a rapid clinical translation as it reduces the need for toxicity studies and regulatory approval for exogenous labels. Emerging applications have utilized label-free optical technology for screening, diagnosis, and surgical guidance. Advancements in detection technology and rapid improvements in artificial intelligence have expedited the clinical implementation of some optical technologies. Among numerous biomedical application areas, middle-ear disease is a unique space where label-free technology has great potential. The middle ear has a unique anatomical location that can be accessed through a dark channel, the external auditory canal; it can be sampled through a tympanic membrane of approximately 100 microns in thickness. The tympanic membrane is the only membrane in the body that is surrounded by air on both sides, under normal conditions. Despite these favorable characteristics, current examination modalities for middle-ear space utilize century-old technology such as white-light otoscopy. This paper reviews existing label-free imaging technologies and their current progress in visualizing middle-ear diseases. We discuss potential opportunities, barriers, and practical considerations when transitioning label-free technology to clinical applications.
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Affiliation(s)
- Zeyi Zhou
- School of Medicine, Stanford University, Palo Alto, CA 94305, USA
| | - Rishikesh Pandey
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Tulio A Valdez
- Department of Otolaryngology, Stanford University, Palo Alto, CA 94304, USA
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Salimi M, Roshanfar M, Tabatabaei N, Mosadegh B. Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine. J Pers Med 2023; 14:33. [PMID: 38248734 PMCID: PMC10817559 DOI: 10.3390/jpm14010033] [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: 10/24/2023] [Revised: 12/08/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.
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Affiliation(s)
| | - Majid Roshanfar
- Department of Mechanical Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;
| | - Nima Tabatabaei
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada;
| | - Bobak Mosadegh
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA
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Tamir SO, Bialasiewicz S, Brennan-Jones CG, Der C, Kariv L, Macharia I, Marsh RL, Seguya A, Thornton R. ISOM 2023 research Panel 4 - Diagnostics and microbiology of otitis media. Int J Pediatr Otorhinolaryngol 2023; 174:111741. [PMID: 37788516 DOI: 10.1016/j.ijporl.2023.111741] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 10/05/2023]
Abstract
OBJECTIVES To identify and review key research advances from the literature published between 2019 and 2023 on the diagnosis and microbiology of otitis media (OM) including acute otitis media (AOM), recurrent AOM (rAOM), otitis media with effusion (OME), chronic suppurative otitis media (CSOM) and AOM complications (mastoiditis). DATA SOURCES PubMed database of the National Library of Medicine. REVIEW METHODS All relevant original articles published in Medline in English between July 2019 and February 2023 were identified. Studies that were reviews, case studies, relating to OM complications (other than mastoiditis), and studies focusing on guideline adherence, and consensus statements were excluded. Members of the panel drafted the report based on these search results. MAIN FINDINGS For the diagnosis section, 2294 unique records screened, 55 were eligible for inclusion. For the microbiology section 705 unique records were screened and 137 articles were eligible for inclusion. The main themes that arose in OM diagnosis were the need to incorporate multiple modalities including video-otoscopy, tympanometry, telemedicine and artificial intelligence for accurate diagnoses in all diagnostic settings. Further to this, was the use of new, cheap, readily available tools which may improve access in rural and lowmiddle income (LMIC) settings. For OM aetiology, PCR remains the most sensitive method for detecting middle ear pathogens with microbiome analysis still largely restricted to research use. The global pandemic response reduced rates of OM in children, but post-pandemic shifts should be monitored. IMPLICATION FOR PRACTICE AND FUTURE RESEARCH Cheap, easy to use multi-technique assessments combined with artificial intelligence and/or telemedicine should be integrated into future practice to improve diagnosis and treatment pathways in OM diagnosis. Longitudinal studies investigating the in-vivo process of OM development, timings and in-depth interactions between the triad of bacteria, viruses and the host immune response are still required. Standardized methods of collection and analysis for microbiome studies to enable inter-study comparisons are required. There is a need to target underlying biofilms if going to effectively prevent rAOM and OME and possibly enhance ventilation tube retention.
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Affiliation(s)
- Sharon Ovnat Tamir
- Department of Otolaryngology-Head and Neck Surgery, Sasmon Assuta Ashdod University Hospital, Faculty of Health Sciences, Ben Gurion University of the Negev, Israel.
| | - Seweryn Bialasiewicz
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Christopher G Brennan-Jones
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia; Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
| | - Carolina Der
- Facultad de Medicina, Universidad Del Desarrollo, Dr Luis Calvo Mackenna Hospital, Santiago, Chile
| | - Liron Kariv
- Hearing, Speech and Language Institute, Sasmon Assuta Ashdod University Hospital, Israel
| | - Ian Macharia
- Kenyatta University Teaching, Referral & Research Hospital, Kenya
| | - Robyn L Marsh
- Menzies School of Health Research, Darwin, Australia; School of Health Sciences, University of Tasmania, Launceston, Australia
| | - Amina Seguya
- Department of Otolaryngology - Head and Neck Surgery, Mulago National Referral Hospital, Kampala, Uganda
| | - Ruth Thornton
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia; Centre for Child Health Research, University of Western Australia, Perth, Australia
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Ding X, Huang Y, Tian X, Zhao Y, Feng G, Gao Z. Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence. Diagnostics (Basel) 2023; 13:2309. [PMID: 37443702 DOI: 10.3390/diagnostics13132309] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/04/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which significantly increases the difficulty of treating the disease and the likelihood of serious complications developing including hearing loss, speech impairment, and even intracranial infection. Several areas of healthcare have shown great promise in the application of artificial intelligence (AI) systems, such as the accurate detection of diseases, the automated interpretation of images, and the prediction of patient outcomes. Several articles have reported some machine learning (ML) algorithms such as ResNet, InceptionV3 and Unet, were applied to the diagnosis of OM successfully. The use of these techniques in the OM is still in its infancy, but their potential is enormous. We present in this review important concepts related to ML and AI, describe how these technologies are currently being applied to diagnosing, treating, and managing OM, and discuss the challenges associated with developing AI-assisted OM technologies in the future.
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Affiliation(s)
- Xin Ding
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Yu Huang
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Xu Tian
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Yang Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Guodong Feng
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Zhiqiang Gao
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
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Chen CK, Lai YH, Hsieh LC, Tsui PH. Quantitative transmastoid ultrasound for detecting middle ear effusion in pediatric patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107557. [PMID: 37100023 DOI: 10.1016/j.cmpb.2023.107557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/30/2023] [Accepted: 04/17/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Ultrasound has emerged as a promising modality for detecting middle ear effusion (MEE) in pediatric patients. Among different ultrasound techniques, ultrasound mastoid measurement was proposed to allow noninvasive detection of MEE by estimating the Nakagami parameters of backscattered signals to describe the echo amplitude distribution. This study further developed the multiregional-weighted Nakagami parameter (MNP) of the mastoid as a new ultrasound signature for assessing effusion severity and fluid properties in pediatric patients with MEE. METHODS A total of 197 pediatric patients (n = 133 for the training group; n = 64 for the testing group) underwent multiregional backscattering measurements of the mastoid for estimating MNP values. MEE, the severity of effusion (mild to moderate vs. severe), and the fluid properties (serous and mucous) were confirmed through otoscopy, tympanometry, and grommet surgery and were compared with the ultrasound findings. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS The training dataset revealed significant differences in MNPs between the control and MEE groups, between mild to moderate and severe MEE, and between serous and mucous effusion were observed (p < 0.05). As with the conventional Nakagami parameter, the MNP could be used to detect MEE (AUROC: 0.87; sensitivity: 90.16%; specificity: 75.35%). The MNP could further identify effusion severity (AUROC: 0.88; sensitivity: 73.33%; specificity: 86.87%) and revealed the possibility of characterizing fluid properties (AUROC: 0.68; sensitivity: 62.50%; specificity: 70.00%). The testing results demonstrated that the MNP method enabled MEE detection (AUROC = 0.88, accuracy = 88.28%, sensitivity = 92.59%, specificity = 84.21%), was effective in assessing MEE severity (AUROC = 0.83, accuracy = 77.78%, sensitivity = 66.67%, specificity = 83.33%), and showed potential for characterizing fluid properties of effusion (AUROC = 0.70, accuracy = 72.22%, sensitivity = 62.50%, specificity = 80.00%). CONCLUSIONS Transmastoid ultrasound combined with the MNP not only leverages the strengths of the conventional Nakagami parameter for MEE diagnosis but also provides a means to assess MEE severity and effusion properties in pediatric patients, thereby offering a comprehensive approach to noninvasive MEE evaluation.
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Affiliation(s)
- Chin-Kuo Chen
- Department of Otolaryngology-Head and Neck Surgery and Communication Enhancement Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan; School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Yan-Heng Lai
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Li-Chun Hsieh
- Department of Otolaryngology-Head and Neck Surgery, Mackay Memorial Hospital, Taipei, Taiwan; Department of Audiology and Speech Language Pathology, Mackay Medical College, New Taipei, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan.
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Babaei M, Bonakdar S, Nasernejad B. Selective biofunctionalization of 3D cell-imprinted PDMS with collagen immobilization for targeted cell attachment. Sci Rep 2022; 12:12837. [PMID: 35896682 PMCID: PMC9329428 DOI: 10.1038/s41598-022-17252-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 07/22/2022] [Indexed: 12/02/2022] Open
Abstract
Cell-imprinted polydimethylsiloxane substrates, in terms of their ability to mimic the physiological niche, low microfabrication cost, and excellent biocompatibility were widely used in tissue engineering. Cells inside the mature cells' cell-imprinted PDMS pattern have been shown in previous research to be capable of being differentiated into a specific mature cell line. On the other hand, the hydrophobicity of PDMS substrate leads to weak cell adhesion. Moreover, there was no guarantee that the cells would be exactly located in the cavities of the cells' pattern. In many studies, PDMS surface was modified by plasma treatment, chemical modification, and ECM coating. Hence, to increase the efficiency of cell-imprinting method, the concavity region created by the cell-imprinted pattern is conjugated with collagen. A simple and economical method of epoxy silane resin was applied for the selective protein immobilization on the desired regions of the PDMS substrate. This method could be paved to enhance the cell trapping into the cell-imprinted pattern, and it could be helpful for stem cell differentiation studies. The applied method for selective protein attachment, and as a consequence, selective cell integration was assessed on the aligned cell-imprinted PDMS. A microfluidic chip created the aligned cell pattern. After Ar+ plasma and APTES treatment of the PDMS substrate, collagen immobilization was performed. The immobilized collagen was removed by epoxy silane resin stamp from the ridge area where the substrate lacked cell pattern and leaving the collagen only within the patterned areas. Coomassie brilliant blue staining was evaluated for selective collagen immobilization, and the collagen-binding stability was assessed by BCA analysis. MTT assay for the evaluation of cell viability on the modified surface was further analyzed. Subsequently, the crystal violet staining has confirmed the selective cell integration to the collagen-immobilized site on the PDMS substrate. The results proved the successfully selective collagen immobilization on the cell-imprinted PDMS and showed that this method increased the affinity of cells to attach inside the cell pattern cavity.
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Affiliation(s)
- Mahrokh Babaei
- Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Shahin Bonakdar
- National Cell Bank Department, Pasteur Institute of Iran, Tehran, Iran
| | - Bahram Nasernejad
- Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
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Transmastoid Ultrasound Detection of Middle Ear Effusion and Its Association with Clinical Audiometric Tests. Life (Basel) 2022; 12:life12040599. [PMID: 35455090 PMCID: PMC9028690 DOI: 10.3390/life12040599] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 04/14/2022] [Accepted: 04/16/2022] [Indexed: 11/23/2022] Open
Abstract
Medical history taking, otoscopy, tympanometry, and audiometry are clinical methods to diagnose middle ear effusion (MEE); however, these procedures are experience-dependent and result in misdiagnosis under unfavorable conditions of the external auditory canal in non-cooperative young children. This study aimed to explore the use of transmastoid ultrasound combined with the Nakagami parameter analysis to detect MEE in children aged 3−5 years and to compare the proposed method with clinical evaluation methods. A total of forty subjects were enrolled; for each subject, a single-element ultrasound transducer of 2.25 MHz was used to measure backscattered signals returned from the mastoid for estimating the Nakagami parameter, which is a measure of the echo amplitude distribution. Tympanogram and hearing loss were also measured for comparisons. The results showed that the Nakagami parameter in the patients with MEE was significantly larger than that of the normal group (p < 0.05). The area under the receiver operating characteristic curve (AUROC) for using the Nakagami parameter to detect MEE was 0.90, and the sensitivity, specificity, and accuracy were 82.5%, 97.5%, and 79.6%, respectively. The Nakagami parameter for tympanogram types B/C was higher than that for tympanogram type A (p < 0.05); it was also higher in the subjects with hearing loss (p < 0.05). Quantitative transmastoid ultrasound based on the Nakagami parameter analysis has the potential to detect MEE and evaluate hearing loss.
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Viscaino M, Talamilla M, Maass JC, Henríquez P, Délano PH, Auat Cheein C, Auat Cheein F. Color Dependence Analysis in a CNN-Based Computer-Aided Diagnosis System for Middle and External Ear Diseases. Diagnostics (Basel) 2022; 12:diagnostics12040917. [PMID: 35453965 PMCID: PMC9031192 DOI: 10.3390/diagnostics12040917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/15/2022] [Accepted: 03/23/2022] [Indexed: 02/01/2023] Open
Abstract
Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.
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Affiliation(s)
- Michelle Viscaino
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390382, Chile;
- Advanced Center of Electrical and Electronic Engineering, Valparaíso 2390136, Chile;
| | - Matias Talamilla
- Interdisciplinary Program of Physiology and Biophysics, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, University of Chile, Santiago 8320328, Chile; (M.T.); (J.C.M.)
| | - Juan Cristóbal Maass
- Interdisciplinary Program of Physiology and Biophysics, Institute of Biomedical Sciences (ICBM), Faculty of Medicine, University of Chile, Santiago 8320328, Chile; (M.T.); (J.C.M.)
- Department of Otolaryngology, Hospital Clínico Universidad de Chile, Faculty of Medicine, University of Chile, Santiago 8320328, Chile;
- Unit of Otolaryngology, Department of Surgery, Clínica Alemana de Santiago, Facultad de Medicina Clínica Alemana-Universidad del Desarrollo, Santiago 0323142, Chile
| | - Pablo Henríquez
- Department of Otolaryngology, Hospital Clínico Universidad de Chile, Faculty of Medicine, University of Chile, Santiago 8320328, Chile;
- Medical Sciences Doctorate Program, Postgraduate School, Faculty of Medicine, University of Chile, Santiago 8320328, Chile
| | - Paul H. Délano
- Advanced Center of Electrical and Electronic Engineering, Valparaíso 2390136, Chile;
- Department of Otolaryngology, Hospital Clínico Universidad de Chile, Faculty of Medicine, University of Chile, Santiago 8320328, Chile;
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago 8320328, Chile
| | - Cecilia Auat Cheein
- Facultad de Ciencias Médicas, Universidad Nacional de Santiago del Estero, Santiago del Estero 4200, Argentina;
| | - Fernando Auat Cheein
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390382, Chile;
- Advanced Center of Electrical and Electronic Engineering, Valparaíso 2390136, Chile;
- Correspondence:
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