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Chang KM, Surapaneni SS, Shaikh N, Marston AP, Vecchiotti MA, Rangarajan N, Hill CA, Scott AR. Pediatric tympanostomy tube assessment via deep learning. Am J Otolaryngol 2024; 45:104334. [PMID: 38723380 DOI: 10.1016/j.amjoto.2024.104334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 04/21/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE Tympanostomy tube (TT) placement is the most frequently performed ambulatory surgery in children under 15. After the procedure it is recommended that patients follow up regularly for "tube checks" until TT extrusion. Such visits incur direct and indirect costs to families in the form of days off from work, copays, and travel expenses. This pilot study aims to compare the efficacy of tympanic membrane (TM) evaluation by an artificial intelligence algorithm with that of clinical staff for determining presence or absence of a tympanostomy tube within the TM. METHODS Using a digital otoscope, we performed a prospective study in children (ages 10 months-10 years) with a history of TTs who were being seen for follow up in a pediatric otolaryngology clinic. A smartphone otoscope was used by study personnel who were not physicians to take ear exam images, then through conventional otoscopic exam, ears were assessed by a clinician for tubes being in place or tubes having extruded from the TM. We trained and tested a deep learning (artificial intelligence) algorithm to assess the images and compared that with the clinician's assessment. RESULTS A total of 123 images were obtained from 28 subjects. The algorithm classified images as TM with or without tube in place. Overall classification accuracy was 97.7 %. Recall and precision were 100 % and 96 %, respectively, for TM without a tube present, and 95 % and 100 %, respectively, for TM with a tube in place. DISCUSSION This is a promising deep learning algorithm for classifying ear tube presence in the TM utilizing images obtained in awake children using an over-the-counter otoscope available to the lay population. We are continuing enrollment, with the goal of building an algorithm to assess tube patency and extrusion.
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Affiliation(s)
- K M Chang
- Tufts University School of Medicine, Boston, MA, United States of America
| | | | - N Shaikh
- Tufts Medical Center, Boston, MA, United States of America
| | - A P Marston
- Tufts Medical Center, Boston, MA, United States of America
| | - M A Vecchiotti
- Tufts Medical Center, Boston, MA, United States of America
| | - N Rangarajan
- COHI Group, St. Paul, MN, United States of America
| | - C A Hill
- COHI Group, St. Paul, MN, United States of America
| | - A R Scott
- Tufts University School of Medicine, Boston, MA, United States of America; Tufts Medical Center, Boston, MA, United States of America.
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Rapoport N, Pavelchek C, Michelson AP, Shew MA. Artificial Intelligence in Otology and Neurotology. Otolaryngol Clin North Am 2024:S0030-6665(24)00067-7. [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] [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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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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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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El Feghaly RE, Nedved A, Katz SE, Frost HM. New insights into the treatment of acute otitis media. Expert Rev Anti Infect Ther 2023; 21:523-534. [PMID: 37097281 PMCID: PMC10231305 DOI: 10.1080/14787210.2023.2206565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/20/2023] [Indexed: 04/26/2023]
Abstract
INTRODUCTION Acute otitis media (AOM) affects most (80%) children by 5 years of age and is the most common reason children are prescribed antibiotics. The epidemiology of AOM has changed considerably since the widespread use of pneumococcal conjugate vaccines, which has broad-reaching implications for management. AREAS COVERED In this narrative review, we cover the epidemiology of AOM, best practices for diagnosis and management, new diagnostic technology, effective stewardship interventions, and future directions of the field. Literature review was performed using PubMed and ClinicalTrials.gov. EXPERT OPINION Inaccurate diagnoses, unnecessary antibiotic use, and increasing antimicrobial resistance remain major challenges in AOM management. Fortunately, effective tools and interventions to improve diagnostic accuracy, de-implement unnecessary antibiotic use, and individualize care are on the horizon. Successful scaling of these tools and interventions will be critical to improving overall care for children.
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Affiliation(s)
- Rana E. El Feghaly
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- Department of Pediatrics, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Amanda Nedved
- Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, MO, USA
- Department of Pediatrics, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Sophie E. Katz
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Holly M. Frost
- Department of Pediatrics, Denver Health and Hospital Authority, Denver, CO, USA
- Center for Health Systems Research, Denver Health and Hospital Authority, Denver, CO, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
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