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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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Dubois C, Eigen D, Simon F, Couloigner V, Gormish M, Chalumeau M, Schmoll L, Cohen JF. Development and validation of a smartphone-based deep-learning-enabled system to detect middle-ear conditions in otoscopic images. NPJ Digit Med 2024; 7:162. [PMID: 38902477 PMCID: PMC11189910 DOI: 10.1038/s41746-024-01159-9] [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: 07/10/2023] [Accepted: 06/10/2024] [Indexed: 06/22/2024] Open
Abstract
Middle-ear conditions are common causes of primary care visits, hearing impairment, and inappropriate antibiotic use. Deep learning (DL) may assist clinicians in interpreting otoscopic images. This study included patients over 5 years old from an ambulatory ENT practice in Strasbourg, France, between 2013 and 2020. Digital otoscopic images were obtained using a smartphone-attached otoscope (Smart Scope, Karl Storz, Germany) and labeled by a senior ENT specialist across 11 diagnostic classes (reference standard). An Inception-v2 DL model was trained using 41,664 otoscopic images, and its diagnostic accuracy was evaluated by calculating class-specific estimates of sensitivity and specificity. The model was then incorporated into a smartphone app called i-Nside. The DL model was evaluated on a validation set of 3,962 images and a held-out test set comprising 326 images. On the validation set, all class-specific estimates of sensitivity and specificity exceeded 98%. On the test set, the DL model achieved a sensitivity of 99.0% (95% confidence interval: 94.5-100) and a specificity of 95.2% (91.5-97.6) for the binary classification of normal vs. abnormal images; wax plugs were detected with a sensitivity of 100% (94.6-100) and specificity of 97.7% (95.0-99.1); other class-specific estimates of sensitivity and specificity ranged from 33.3% to 92.3% and 96.0% to 100%, respectively. We present an end-to-end DL-enabled system able to achieve expert-level diagnostic accuracy for identifying normal tympanic aspects and wax plugs within digital otoscopic images. However, the system's performance varied for other middle-ear conditions. Further prospective validation is necessary before wider clinical deployment.
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Affiliation(s)
| | | | - François Simon
- Department of Pediatric Otolaryngology, Necker-Enfants malades Hospital, APHP, Université Paris Cité, Paris, France
| | - Vincent Couloigner
- Department of Pediatric Otolaryngology, Necker-Enfants malades Hospital, APHP, Université Paris Cité, Paris, France
| | | | - Martin Chalumeau
- Inserm UMR1153 (CRESS), Université Paris Cité, Paris, France
- Department of General Pediatrics and Pediatric Infectious Diseases, Necker-Enfants malades Hospital, APHP, Université Paris Cité, Paris, France
| | | | - Jérémie F Cohen
- Inserm UMR1153 (CRESS), Université Paris Cité, Paris, France.
- Department of General Pediatrics and Pediatric Infectious Diseases, Necker-Enfants malades Hospital, APHP, Université Paris Cité, Paris, France.
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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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Shaikh N, Conway SJ, Kovačević J, Condessa F, Shope TR, Haralam MA, Campese C, Lee MC, Larsson T, Cavdar Z, Hoberman A. Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children. JAMA Pediatr 2024; 178:401-407. [PMID: 38436941 PMCID: PMC10985552 DOI: 10.1001/jamapediatrics.2024.0011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/17/2023] [Indexed: 03/05/2024]
Abstract
Importance Acute otitis media (AOM) is a frequently diagnosed illness in children, yet the accuracy of diagnosis has been consistently low. Multiple neural networks have been developed to recognize the presence of AOM with limited clinical application. Objective To develop and internally validate an artificial intelligence decision-support tool to interpret videos of the tympanic membrane and enhance accuracy in the diagnosis of AOM. Design, Setting, and Participants This diagnostic study analyzed otoscopic videos of the tympanic membrane captured using a smartphone during outpatient clinic visits at 2 sites in Pennsylvania between 2018 and 2023. Eligible participants included children who presented for sick visits or wellness visits. Exposure Otoscopic examination. Main Outcomes and Measures Using the otoscopic videos that were annotated by validated otoscopists, a deep residual-recurrent neural network was trained to predict both features of the tympanic membrane and the diagnosis of AOM vs no AOM. The accuracy of this network was compared with a second network trained using a decision tree approach. A noise quality filter was also trained to prompt users that the video segment acquired may not be adequate for diagnostic purposes. Results Using 1151 videos from 635 children (majority younger than 3 years of age), the deep residual-recurrent neural network had almost identical diagnostic accuracy as the decision tree network. The finalized deep residual-recurrent neural network algorithm classified tympanic membrane videos into AOM vs no AOM categories with a sensitivity of 93.8% (95% CI, 92.6%-95.0%) and specificity of 93.5% (95% CI, 92.8%-94.3%) and the decision tree model had a sensitivity of 93.7% (95% CI, 92.4%-94.9%) and specificity of 93.3% (92.5%-94.1%). Of the tympanic membrane features outputted, bulging of the TM most closely aligned with the predicted diagnosis; bulging was present in 230 of 230 cases (100%) in which the diagnosis was predicted to be AOM in the test set. Conclusions and Relevance These findings suggest that given its high accuracy, the algorithm and medical-grade application that facilitates image acquisition and quality filtering could reasonably be used in primary care or acute care settings to aid with automated diagnosis of AOM and decisions regarding treatment.
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Affiliation(s)
- Nader Shaikh
- Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pennsylvania
| | - Shannon J. Conway
- Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pennsylvania
| | - Jelena Kovačević
- Tandon School of Engineering, New York University, New York, New York
| | - Filipe Condessa
- Bosch Center for Artificial Intelligence, Pittsburgh, Pennsylvania
| | - Timothy R. Shope
- Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pennsylvania
| | - Mary Ann Haralam
- Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pennsylvania
| | - Catherine Campese
- Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pennsylvania
| | - Matthew C. Lee
- Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pennsylvania
| | | | | | - Alejandro Hoberman
- Department of Pediatrics, Division of General Academic Pediatrics, University of Pittsburgh School of Medicine, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pennsylvania
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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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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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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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Song D, Kim T, Lee Y, Kim J. Image-Based Artificial Intelligence Technology for Diagnosing Middle Ear Diseases: A Systematic Review. J Clin Med 2023; 12:5831. [PMID: 37762772 PMCID: PMC10531728 DOI: 10.3390/jcm12185831] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
Otolaryngological diagnoses, such as otitis media, are traditionally performed using endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians. The integration of objective tools, like artificial intelligence (AI), could potentially improve the diagnostic process by minimizing the influence of subjective biases and variability. We systematically reviewed the AI techniques using medical imaging in otolaryngology. Relevant studies related to AI-assisted otitis media diagnosis were extracted from five databases: Google Scholar, PubMed, Medline, Embase, and IEEE Xplore, without date restrictions. Publications that did not relate to AI and otitis media diagnosis or did not utilize medical imaging were excluded. Of the 32identified studies, 26 used tympanic membrane images for classification, achieving an average diagnosis accuracy of 86% (range: 48.7-99.16%). Another three studies employed both segmentation and classification techniques, reporting an average diagnosis accuracy of 90.8% (range: 88.06-93.9%). These findings suggest that AI technologies hold promise for improving otitis media diagnosis, offering benefits for telemedicine and primary care settings due to their high diagnostic accuracy. However, to ensure patient safety and optimal outcomes, further improvements in diagnostic performance are necessary.
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Affiliation(s)
- Dahye Song
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Taewan Kim
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Yeonjoon Lee
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Jaeyoung Kim
- Department of Dermatology and Skin Sciences, University of British Columbia, Vancouver, BC V6T 1Z1, Canada;
- Core Research & Development Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
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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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Cao Z, Chen F, Grais EM, Yue F, Cai Y, Swanepoel DW, Zhao F. Machine Learning in Diagnosing Middle Ear Disorders Using Tympanic Membrane Images: A Meta-Analysis. Laryngoscope 2023; 133:732-741. [PMID: 35848851 DOI: 10.1002/lary.30291] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 06/18/2022] [Accepted: 06/21/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To systematically evaluate the development of Machine Learning (ML) models and compare their diagnostic accuracy for the classification of Middle Ear Disorders (MED) using Tympanic Membrane (TM) images. METHODS PubMed, EMBASE, CINAHL, and CENTRAL were searched up until November 30, 2021. Studies on the development of ML approaches for diagnosing MED using TM images were selected according to the inclusion criteria. PRISMA guidelines were followed with study design, analysis method, and outcomes extracted. Sensitivity, specificity, and area under the curve (AUC) were used to summarize the performance metrics of the meta-analysis. Risk of Bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool in combination with the Prediction Model Risk of Bias Assessment Tool. RESULTS Sixteen studies were included, encompassing 20254 TM images (7025 normal TM and 13229 MED). The sample size ranged from 45 to 6066 per study. The accuracy of the 25 included ML approaches ranged from 76.00% to 98.26%. Eleven studies (68.8%) were rated as having a low risk of bias, with the reference standard as the major domain of high risk of bias (37.5%). Sensitivity and specificity were 93% (95% CI, 90%-95%) and 85% (95% CI, 82%-88%), respectively. The AUC of total TM images was 94% (95% CI, 91%-96%). The greater AUC was found using otoendoscopic images than otoscopic images. CONCLUSIONS ML approaches perform robustly in distinguishing between normal ears and MED, however, it is proposed that a standardized TM image acquisition and annotation protocol should be developed. LEVEL OF EVIDENCE NA Laryngoscope, 133:732-741, 2023.
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Affiliation(s)
- Zuwei Cao
- Center for Rehabilitative Auditory Research, Guizhou Provincial People's Hospital, Guiyang City, China
| | - Feifan Chen
- Centre for Speech and Language Therapy and Hearing Science, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK
| | - Emad M Grais
- Centre for Speech and Language Therapy and Hearing Science, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK
| | - Fengjuan Yue
- Medical Examination Center, Guizhou Provincial People's Hospital, Guiyang City, China
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou City, China
| | - De Wet Swanepoel
- Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa
| | - Fei Zhao
- Centre for Speech and Language Therapy and Hearing Science, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK
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Habib AR, Xu Y, Bock K, Mohanty S, Sederholm T, Weeks WB, Dodhia R, Ferres JL, Perry C, Sacks R, Singh N. Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy. Sci Rep 2023; 13:5368. [PMID: 37005441 PMCID: PMC10067817 DOI: 10.1038/s41598-023-31921-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/20/2023] [Indexed: 04/04/2023] Open
Abstract
To evaluate the generalizability of artificial intelligence (AI) algorithms that use deep learning methods to identify middle ear disease from otoscopic images, between internal to external performance. 1842 otoscopic images were collected from three independent sources: (a) Van, Turkey, (b) Santiago, Chile, and (c) Ohio, USA. Diagnostic categories consisted of (i) normal or (ii) abnormal. Deep learning methods were used to develop models to evaluate internal and external performance, using area under the curve (AUC) estimates. A pooled assessment was performed by combining all cohorts together with fivefold cross validation. AI-otoscopy algorithms achieved high internal performance (mean AUC: 0.95, 95%CI: 0.80-1.00). However, performance was reduced when tested on external otoscopic images not used for training (mean AUC: 0.76, 95%CI: 0.61-0.91). Overall, external performance was significantly lower than internal performance (mean difference in AUC: -0.19, p ≤ 0.04). Combining cohorts achieved a substantial pooled performance (AUC: 0.96, standard error: 0.01). Internally applied algorithms for otoscopy performed well to identify middle ear disease from otoscopy images. However, external performance was reduced when applied to new test cohorts. Further efforts are required to explore data augmentation and pre-processing techniques that might improve external performance and develop a robust, generalizable algorithm for real-world clinical applications.
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Affiliation(s)
- Al-Rahim Habib
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
- Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Sydney, NSW, Australia.
| | - Yixi Xu
- AI for Good Lab, Microsoft, Redmond, WA, USA
| | - Kris Bock
- Azure FastTrack Engineering, Brisbane, QLD, Australia
| | | | | | | | | | | | - Chris Perry
- University of Queensland Medical School, Brisbane, QLD, Australia
| | - Raymond Sacks
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Narinder Singh
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Sydney, NSW, Australia
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Mao S, Wu X, Hou M, Mei L, Feng Y, Song J. Research and application progress in deep learning in otology. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:463-471. [PMID: 37164930 PMCID: PMC10930069 DOI: 10.11817/j.issn.1672-7347.2023.210588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Indexed: 05/12/2023]
Abstract
With the optimization of deep learning algorithms and the accumulation of medical big data, deep learning technology has been widely applied in research in various fields of otology in recent years. At present, research on deep learning in otology is combined with a variety of data such as endoscopy, temporal bone images, audiograms, and intraoperative images, which involves diagnosis of otologic diseases (including auricular malformations, external auditory canal diseases, middle ear diseases, and inner ear diseases), treatment (guiding medication and surgical planning), and prognosis prediction (involving hearing regression and speech learning). According to the type of data and the purpose of the study (disease diagnosis, treatment and prognosis), the different neural network models can be used to take advantage of their algorithms, and the deep learning can be a good aid in treating otologic diseases. The deep learning has a good applicable prospect in the clinical diagnosis and treatment of otologic diseases, which can play a certain role in promoting the development of deep learning combined with intelligent medicine.
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Affiliation(s)
- Shuang Mao
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008.
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008.
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008.
| | - Xuewen Wu
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha 410083
| | - Lingyun Mei
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008
| | - Yong Feng
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008
- Department of Otorhinolaryngology Head and Neck Surgery, Changsha Central Hospital Affiliated to South China University, Changsha 410018, China
| | - Jian Song
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008.
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008.
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008.
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Suresh K, Cohen MS, Hartnick CJ, Bartholomew RA, Lee DJ, Crowson MG. Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data. PLOS DIGITAL HEALTH 2023; 2:e0000202. [PMID: 36827244 PMCID: PMC9956018 DOI: 10.1371/journal.pdig.0000202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/24/2023] [Indexed: 02/25/2023]
Abstract
Synthetic clinical images could augment real medical image datasets, a novel approach in otolaryngology-head and neck surgery (OHNS). Our objective was to develop a generative adversarial network (GAN) for tympanic membrane images and to validate the quality of synthetic images with human reviewers. Our model was developed using a state-of-the-art GAN architecture, StyleGAN2-ADA. The network was trained on intraoperative high-definition (HD) endoscopic images of tympanic membranes collected from pediatric patients undergoing myringotomy with possible tympanostomy tube placement. A human validation survey was administered to a cohort of OHNS and pediatrics trainees at our institution. The primary measure of model quality was the Frechet Inception Distance (FID), a metric comparing the distribution of generated images with the distribution of real images. The measures used for human reviewer validation were the sensitivity, specificity, and area under the curve (AUC) for humans' ability to discern synthetic from real images. Our dataset comprised 202 images. The best GAN was trained at 512x512 image resolution with a FID of 47.0. The progression of images through training showed stepwise "learning" of the anatomic features of a tympanic membrane. The validation survey was taken by 65 persons who reviewed 925 images. Human reviewers demonstrated a sensitivity of 66%, specificity of 73%, and AUC of 0.69 for the detection of synthetic images. In summary, we successfully developed a GAN to produce synthetic tympanic membrane images and validated this with human reviewers. These images could be used to bolster real datasets with various pathologies and develop more robust deep learning models such as those used for diagnostic predictions from otoscopic images. However, caution should be exercised with the use of synthetic data given issues regarding data diversity and performance validation. Any model trained using synthetic data will require robust external validation to ensure validity and generalizability.
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Affiliation(s)
- Krish Suresh
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
| | - Michael S. Cohen
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Christopher J. Hartnick
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ryan A. Bartholomew
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Daniel J. Lee
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Matthew G. Crowson
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, United States of America
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
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Tseng CC, Lim V, Jyung RW. Use of artificial intelligence for the diagnosis of cholesteatoma. Laryngoscope Investig Otolaryngol 2023; 8:201-211. [PMID: 36846416 PMCID: PMC9948563 DOI: 10.1002/lio2.1008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 12/07/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Objectives Accurate diagnosis of cholesteatomas is crucial. However, cholesteatomas can easily be missed in routine otoscopic exams. Convolutional neural networks (CNNs) have performed well in medical image classification, so we evaluated their use for detecting cholesteatomas in otoscopic images. Study Design Design and evaluation of artificial intelligence driven workflow for cholesteatoma diagnosis. Methods Otoscopic images collected from the faculty practice of the senior author were deidentified and labeled by the senior author as cholesteatoma, abnormal non-cholesteatoma, or normal. An image classification workflow was developed to automatically differentiate cholesteatomas from other possible tympanic membrane appearances. Eight pretrained CNNs were trained on our otoscopic images, then tested on a withheld subset of images to evaluate their final performance. CNN intermediate activations were also extracted to visualize important image features. Results A total of 834 otoscopic images were collected, further categorized into 197 cholesteatoma, 457 abnormal non-cholesteatoma, and 180 normal. Final trained CNNs demonstrated strong performance, achieving accuracies of 83.8%-98.5% for differentiating cholesteatoma from normal, 75.6%-90.1% for differentiating cholesteatoma from abnormal non-cholesteatoma, and 87.0%-90.4% for differentiating cholesteatoma from non-cholesteatoma (abnormal non-cholesteatoma + normal). DenseNet201 (100% sensitivity, 97.1% specificity), NASNetLarge (100% sensitivity, 88.2% specificity), and MobileNetV2 (94.1% sensitivity, 100% specificity) were among the best performing CNNs in distinguishing cholesteatoma versus normal. Visualization of intermediate activations showed robust detection of relevant image features by the CNNs. Conclusion While further refinement and more training images are needed to improve performance, artificial intelligence-driven analysis of otoscopic images shows great promise as a diagnostic tool for detecting cholesteatomas. Level of Evidence 3.
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Affiliation(s)
- Christopher C. Tseng
- Department of Otolaryngology – Head and Neck SurgeryRutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Valerie Lim
- Department of Otolaryngology – Head and Neck SurgeryRutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Robert W. Jyung
- Department of Otolaryngology – Head and Neck SurgeryRutgers New Jersey Medical SchoolNewarkNew JerseyUSA
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Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet). Sci Rep 2022; 12:21884. [PMID: 36536152 PMCID: PMC9763432 DOI: 10.1038/s41598-022-26486-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.
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Xie H, Chen Z, Deng J, Zhang J, Duan H, Li Q. Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network. J Transl Med 2022; 20:524. [PMID: 36371220 PMCID: PMC9652981 DOI: 10.1186/s12967-022-03732-w] [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: 08/05/2022] [Accepted: 10/28/2022] [Indexed: 11/15/2022] Open
Abstract
Objective This paper intends to propose a method of using TransResSEUnet2.5D network for accurate automatic segmentation of the Gross Target Volume (GTV) in Radiotherapy for lung cancer. Methods A total of 11,370 computed tomograms (CT), deriving from 137 cases, of lung cancer patients under radiotherapy developed by radiotherapists were used as the training set; 1642 CT images in 20 cases were used as the validation set, and 1685 CT images in 20 cases were used as the test set. The proposed network was tuned and trained to obtain the best segmentation model and its performance was measured by the Dice Similarity Coefficient (DSC) and with 95% Hausdorff distance (HD95). Lastly, as to demonstrate the accuracy of the automatic segmentation of the network proposed in this study, all possible mirrors of the input images were put into Unet2D, Unet2.5D, Unet3D, ResSEUnet3D, ResSEUnet2.5D, and TransResUnet2.5D, and their respective segmentation performances were compared and assessed. Results The segmentation results of the test set showed that TransResSEUnet2.5D performed the best in the DSC (84.08 ± 0.04) %, HD95 (8.11 ± 3.43) mm and time (6.50 ± 1.31) s metrics compared to the other three networks. Conclusions The TransResSEUnet 2.5D proposed in this study can automatically segment the GTV of radiotherapy for lung cancer patients with more accuracy.
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Byun H, Lee SH, Kim TH, Oh J, Chung JH. Feasibility of the Machine Learning Network to Diagnose Tympanic Membrane Lesions without Coding Experience. J Pers Med 2022; 12:jpm12111855. [PMID: 36579584 PMCID: PMC9697619 DOI: 10.3390/jpm12111855] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/29/2022] [Accepted: 10/31/2022] [Indexed: 11/10/2022] Open
Abstract
A machine learning platform operated without coding knowledge (Teachable machine®) has been introduced. The aims of the present study were to assess the performance of the Teachable machine® for diagnosing tympanic membrane lesions. A total of 3024 tympanic membrane images were used to train and validate the diagnostic performance of the network. Tympanic membrane images were labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), and cholesteatoma. According to the complexity of the categorization, Level I refers to normal versus abnormal tympanic membrane; Level II was defined as normal, OME, or COM + cholesteatoma; and Level III distinguishes between all four pathologies. In addition, eighty representative test images were used to assess the performance. Teachable machine® automatically creates a classification network and presents diagnostic performance when images are uploaded. The mean accuracy of the Teachable machine® for classifying tympanic membranes as normal or abnormal (Level I) was 90.1%. For Level II, the mean accuracy was 89.0% and for Level III it was 86.2%. The overall accuracy of the classification of the 80 representative tympanic membrane images was 78.75%, and the hit rates for normal, OME, COM, and cholesteatoma were 95.0%, 70.0%, 90.0%, and 60.0%, respectively. Teachable machine® could successfully generate the diagnostic network for classifying tympanic membrane.
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Affiliation(s)
- Hayoung Byun
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea
| | - Seung Hwan Lee
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Tae Hyun Kim
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
| | - Jaehoon Oh
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Jae Ho Chung
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea
- Department of HY-KIST Bio-Convergence, College of Medicine, Hanyang University, Seoul 04763, Korea
- Correspondence:
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Choi Y, Chae J, Park K, Hur J, Kweon J, Ahn JH. Automated multi-class classification for prediction of tympanic membrane changes with deep learning models. PLoS One 2022; 17:e0275846. [PMID: 36215265 PMCID: PMC9550050 DOI: 10.1371/journal.pone.0275846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/25/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUNDS AND OBJECTIVE Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks. STUDY DESIGN This retrospective study investigated the impact of concurrent diseases in the tympanic membrane on diagnostic performance using multi-class classification. A customized architecture of EfficientNet-B4 was introduced to predict the primary class (otitis media with effusion (OME), chronic otitis media (COM), and 'None' without OME and COM) and secondary classes (attic cholesteatoma, myringitis, otomycosis, and ventilating tube). RESULTS Deep-learning classifications accurately predicted the primary class with dice similarity coefficient (DSC) of 95.19%, while misidentification between COM and OME rarely occurred. Among the secondary classes, the diagnosis of attic cholesteatoma and myringitis achieved a DSC of 88.37% and 88.28%, respectively. Although concurrent diseases hampered the prediction performance, there was only a 0.44% probability of inaccurately predicting two or more secondary classes (29/6,630). The inference time per image was 2.594 ms on average. CONCLUSION Deep-learning classification can be used to support clinical decision-making by accurately and reproducibly predicting tympanic membrane changes in real time, even in the presence of multiple concurrent diseases.
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Affiliation(s)
- Yeonjoo Choi
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Chae
- Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Keunwoo Park
- Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jaehee Hur
- Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihoon Kweon
- Departments of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- * E-mail: (JHA); (JK)
| | - Joong Ho Ahn
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
- * E-mail: (JHA); (JK)
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Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm. Ear Hear 2022; 43:1563-1573. [DOI: 10.1097/aud.0000000000001217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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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: 5] [Impact Index Per Article: 2.5] [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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22
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Habib AR, Kajbafzadeh M, Hasan Z, Wong E, Gunasekera H, Perry C, Sacks R, Kumar A, Singh N. Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta-analysis. Clin Otolaryngol 2022; 47:401-413. [PMID: 35253378 PMCID: PMC9310803 DOI: 10.1111/coa.13925] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/08/2022] [Accepted: 02/27/2022] [Indexed: 11/29/2022]
Abstract
Objectives To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy. Design Systematic review and meta‐analysis. Methods Using the PRISMA guidelines, nine online databases were searched for articles that used AI computer vision algorithms developed from various methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees and k‐nearest neighbours) to classify otoscopic images. Diagnostic classes of interest: normal tympanic membrane, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM) with or without perforation, cholesteatoma and canal obstruction. Main outcome measures Accuracy to correctly classify otoscopic images compared to otolaryngologists (ground truth). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool was used to assess the quality of methodology and risk of bias. Results Thirty‐nine articles were included. Algorithms achieved 90.7% (95%CI: 90.1–91.3%) accuracy to difference between normal or abnormal otoscopy images in 14 studies. The most common multiclassification algorithm (3 or more diagnostic classes) achieved 97.6% (95%CI: 97.3–97.9%) accuracy to differentiate between normal, AOM and OME in three studies. AI algorithms outperformed human assessors to classify otoscopy images achieving 93.4% (95%CI: 90.5–96.4%) versus 73.2% (95%CI: 67.9–78.5%) accuracy in three studies. Convolutional neural networks achieved the highest accuracy compared to other classification methods. Conclusion AI can classify ear disease from otoscopy. A concerted effort is required to establish a comprehensive and reliable otoscopy database for algorithm training. An AI‐supported otoscopy system may assist health care workers, trainees and primary care practitioners with less otology experience identify ear disease.
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Affiliation(s)
- Al-Rahim Habib
- Faculty of Medicine and Health, University of Sydney, New South Wales, Australia.,Department of Otolaryngology - Head and Neck Surgery, Princess Alexandra Hospital, Queensland, Australia.,Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, New South Wales, Australia
| | - Majid Kajbafzadeh
- Faculty of Medicine and Health, University of Sydney, New South Wales, Australia
| | - Zubair Hasan
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, New South Wales, Australia
| | - Eugene Wong
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, New South Wales, Australia
| | - Hasantha Gunasekera
- Faculty of Medicine and Health, University of Sydney, New South Wales, Australia.,The Children's Hospital at Westmead, New South Wales, Australia
| | - Chris Perry
- Department of Otolaryngology - Head and Neck Surgery, Princess Alexandra Hospital, Queensland, Australia.,University of Queensland Medical School, Queensland, Australia
| | - Raymond Sacks
- Faculty of Medicine and Health, University of Sydney, New South Wales, Australia
| | - Ashnil Kumar
- School of Biomedical Engineering, Faculty of Engineering, University of Sydney, New South Wales, Australia
| | - Narinder Singh
- Faculty of Medicine and Health, University of Sydney, New South Wales, Australia.,Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, New South Wales, Australia
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23
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Cha D, Pae C, Lee SA, Na G, Hur YK, Lee HY, Cho AR, Cho YJ, Han SG, Kim SH, Choi JY, Park HJ. Differential Biases and Variabilities of Deep Learning-Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study. JMIR Med Inform 2021; 9:e33049. [PMID: 34889764 PMCID: PMC8701703 DOI: 10.2196/33049] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/29/2021] [Accepted: 10/12/2021] [Indexed: 11/13/2022] Open
Abstract
Background Deep learning (DL)–based artificial intelligence may have different diagnostic characteristics than human experts in medical diagnosis. As a data-driven knowledge system, heterogeneous population incidence in the clinical world is considered to cause more bias to DL than clinicians. Conversely, by experiencing limited numbers of cases, human experts may exhibit large interindividual variability. Thus, understanding how the 2 groups classify given data differently is an essential step for the cooperative usage of DL in clinical application. Objective This study aimed to evaluate and compare the differential effects of clinical experience in otoendoscopic image diagnosis in both computers and physicians exemplified by the class imbalance problem and guide clinicians when utilizing decision support systems. Methods We used digital otoendoscopic images of patients who visited the outpatient clinic in the Department of Otorhinolaryngology at Severance Hospital, Seoul, South Korea, from January 2013 to June 2019, for a total of 22,707 otoendoscopic images. We excluded similar images, and 7500 otoendoscopic images were selected for labeling. We built a DL-based image classification model to classify the given image into 6 disease categories. Two test sets of 300 images were populated: balanced and imbalanced test sets. We included 14 clinicians (otolaryngologists and nonotolaryngology specialists including general practitioners) and 13 DL-based models. We used accuracy (overall and per-class) and kappa statistics to compare the results of individual physicians and the ML models. Results Our ML models had consistently high accuracies (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%), equivalent to those of otolaryngologists (balanced: mean 71.17%, SD 3.37%; imbalanced: mean 72.84%, SD 6.41%) and far better than those of nonotolaryngologists (balanced: mean 45.63%, SD 7.89%; imbalanced: mean 44.08%, SD 15.83%). However, ML models suffered from class imbalance problems (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%). This was mitigated by data augmentation, particularly for low incidence classes, but rare disease classes still had low per-class accuracies. Human physicians, despite being less affected by prevalence, showed high interphysician variability (ML models: kappa=0.83, SD 0.02; otolaryngologists: kappa=0.60, SD 0.07). Conclusions Even though ML models deliver excellent performance in classifying ear disease, physicians and ML models have their own strengths. ML models have consistent and high accuracy while considering only the given image and show bias toward prevalence, whereas human physicians have varying performance but do not show bias toward prevalence and may also consider extra information that is not images. To deliver the best patient care in the shortage of otolaryngologists, our ML model can serve a cooperative role for clinicians with diverse expertise, as long as it is kept in mind that models consider only images and could be biased toward prevalent diseases even after data augmentation.
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Affiliation(s)
- Dongchul Cha
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University College of Medicine, Seoul, Republic of Korea.,Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Se A Lee
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Gina Na
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young Kyun Hur
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ho Young Lee
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - A Ra Cho
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young Joon Cho
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sang Gil Han
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung Huhn Kim
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae Young Choi
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hae-Jeong Park
- Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University College of Medicine, Seoul, Republic of Korea.,Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
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