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Cai Q, Zhang P, Xie F, Zhang Z, Tu B. Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle. BMC Med Imaging 2024; 24:102. [PMID: 38724896 PMCID: PMC11080198 DOI: 10.1186/s12880-024-01277-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
Precision and intelligence in evaluating the complexities of middle ear structures are required to diagnose auriculotemporal and ossicle-related diseases within otolaryngology. Due to the complexity of the anatomical details and the varied etiologies of illnesses such as trauma, chronic otitis media, and congenital anomalies, traditional diagnostic procedures may not yield accurate diagnoses. This research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT). This study employs a deep learning method, Convolutional Neural Network-UNet (CNN-UNet), to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources, leading to groundbreaking discoveries and better patient healthcare. The research effort is the interaction between the CNN-UNet model and high-resolution Computed Tomography (CT) scans, automating activities including ossicle segmentation, fracture detection, and disruption cause classification, accelerating the diagnostic process and increasing clinical decision-making. The suggested HRSCT-DLT model represents the integration of high-resolution spiral CT scans with the CNN-UNet model, which has been fine-tuned to address the nuances of auriculotemporal and ossicular diseases. This novel combination improves diagnostic efficiency and our overall understanding of these intricate diseases. The results of this study highlight the promise of combining high-resolution CT scanning with the CNN-UNet model in otolaryngology, paving the way for more accurate diagnosis and more individualized treatment plans for patients experiencing auriculotemporal and ossicle-related disruptions.
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Affiliation(s)
- Qinfang Cai
- Department of Otolaryngology, The First Clinical Medical College of Jinan University, Guangzhou, 510630, Guangdong, China
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Peishan Zhang
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Fengmei Xie
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Zedong Zhang
- Department of Otolaryngology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, 510900, Guangdong, China
| | - Bo Tu
- Department of Otolaryngology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, Guangdong, China.
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Hong SJ, Hou JU, Chung MJ, Kang SH, Shim BS, Lee SL, Park DH, Choi A, Oh JY, Lee KJ, Shin E, Cho E, Park SW. Convolutional neural network model for automatic recognition and classification of pancreatic cancer cell based on analysis of lipid droplet on unlabeled sample by 3D optical diffraction tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 246:108041. [PMID: 38325025 DOI: 10.1016/j.cmpb.2024.108041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/05/2024] [Accepted: 01/19/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION Pancreatic cancer cells generally accumulate large numbers of lipid droplets (LDs), which regulate lipid storage. To promote rapid diagnosis, an automatic pancreatic cancer cell recognition system based on a deep convolutional neural network was proposed in this study using quantitative images of LDs from stain-free cytologic samples by optical diffraction tomography. METHODS We retrieved 3D refractive index tomograms and reconstructed 37 optical images of one cell. From the four cell lines, the obtained fields were separated into training and test datasets with 10,397 and 3,478 images, respectively. Furthermore, we adopted several machine learning techniques based on a single image-based prediction model to improve the performance of the computer-aided diagnostic system. RESULTS Pancreatic cancer cells had a significantly lower total cell volume and dry mass than did normal pancreatic cells and were accompanied by greater numbers of lipid droplets (LDs). When evaluating multitask learning techniques utilizing the EfficientNet-b3 model through confusion matrices, the overall 2-category accuracy for cancer classification reached 96.7 %. Simultaneously, the overall 4-category accuracy for individual cell line classification achieved a high accuracy of 96.2 %. Furthermore, when we added the core techniques one by one, the overall performance of the proposed technique significantly improved, reaching an area under the curve (AUC) of 0.997 and an accuracy of 97.06 %. Finally, the AUC reached 0.998 through the ablation study with the score fusion technique. DISCUSSION Our novel training strategy has significant potential for automating and promoting rapid recognition of pancreatic cancer cells. In the near future, deep learning-embedded medical devices will substitute laborious manual cytopathologic examinations for sustainable economic potential.
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Affiliation(s)
- Seok Jin Hong
- Department of Otolaryngology-Head and Neck Surgery, Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jong-Uk Hou
- School of Software, Hallym University, Chuncheon, Republic of Korea
| | - Moon Jae Chung
- Division of Gastroenterology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung Hun Kang
- Department of Otolaryngology-Head and Neck Surgery, Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Bo-Seok Shim
- School of Software, Hallym University, Chuncheon, Republic of Korea
| | - Seung-Lee Lee
- School of Software, Hallym University, Chuncheon, Republic of Korea
| | - Da Hae Park
- Division of Gastroenterology, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, 7, Keunjaebong-gil, Hwaseong-si, Gyeonggi-do 18450, Republic of Korea
| | - Anna Choi
- Division of Gastroenterology, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, 7, Keunjaebong-gil, Hwaseong-si, Gyeonggi-do 18450, Republic of Korea
| | - Jae Yeon Oh
- Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Kyong Joo Lee
- Division of Gastroenterology, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, 7, Keunjaebong-gil, Hwaseong-si, Gyeonggi-do 18450, Republic of Korea
| | - Eun Shin
- Department of Pathology, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
| | - Eunae Cho
- Division of Gastroenterology, Department of Internal Medicine, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Se Woo Park
- Division of Gastroenterology, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, 7, Keunjaebong-gil, Hwaseong-si, Gyeonggi-do 18450, Republic of Korea.
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Afify HM, Mohammed KK, Hassanien AE. Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach. Ann Biomed Eng 2024; 52:865-876. [PMID: 38097895 PMCID: PMC10940396 DOI: 10.1007/s10439-023-03422-8] [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: 02/27/2023] [Accepted: 12/06/2023] [Indexed: 03/16/2024]
Abstract
Examining otoscopic images for ear diseases is necessary when the clinical diagnosis of ear diseases extracted from the knowledge of otolaryngologists is limited. Improved diagnosis approaches based on otoscopic image processing are urgently needed. Recently, convolutional neural networks (CNNs) have been carried out for medical diagnosis to obtain higher accuracy than standard machine learning algorithms and specialists' expertise. Therefore, the proposed approach involves using the Bayesian hyperparameter optimization with the CNN architecture for automatic diagnosis of ear imagery database including four classes: normal, myringosclerosis, earwax plug, and chronic otitis media (COM). The suggested approach was trained using 616 otoscopic images, and the performance of this approach was assessed using 264 testing images. In this paper, the performance of ear disease classification was compared in terms of accuracy, sensitivity, specificity, and positive predictive value (PPV). The results produced a classification accuracy of 98.10%, a sensitivity of 98.11%, a specificity of 99.36%, and a PPV of 98.10%. Finally, the suggested approach demonstrates how to locate optimal CNN hyperparameters for accurate diagnosis of ear diseases while taking time into account. As a result, the usefulness and dependability of the suggested approach will lead to the establishment of an automated tool for better categorization and prediction of different ear diseases.
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Affiliation(s)
- Heba M Afify
- Systems and Biomedical Engineering Department, Higher Institute of Engineering in Shorouk Academy, Al Shorouk City, Cairo, Egypt.
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt.
| | - Kamel K Mohammed
- Center for Virus Research and Studies, Al Azhar University, Cairo, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Aboul Ella Hassanien
- College of Business Administration, Kuwait University, Kuwait, Kuwait
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
- Faculty of Computers and Information, Cairo University, Giza, Egypt
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Sundgaard JV, Hannemose MR, Laugesen S, Bray P, Harte J, Kamide Y, Tanaka C, Paulsen RR, Christensen AN. Multi-modal deep learning for joint prediction of otitis media and diagnostic difficulty. Laryngoscope Investig Otolaryngol 2024; 9:e1199. [PMID: 38362190 PMCID: PMC10866588 DOI: 10.1002/lio2.1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/10/2023] [Accepted: 11/26/2023] [Indexed: 02/17/2024] Open
Abstract
Objectives In this study, we propose a diagnostic model for automatic detection of otitis media based on combined input of otoscopy images and wideband tympanometry measurements. Methods We present a neural network-based model for the joint prediction of otitis media and diagnostic difficulty. We use the subclassifications acute otitis media and otitis media with effusion. The proposed approach is based on deep metric learning, and we compare this with the performance of a standard multi-task network. Results The proposed deep metric approach shows good performance on both tasks, and we show that the multi-modal input increases the performance for both classification and difficulty estimation compared to the models trained on the modalities separately. An accuracy of 86.5% is achieved for the classification task, and a Kendall rank correlation coefficient of 0.45 is achieved for difficulty estimation, corresponding to a correct ranking of 72.6% of the cases. Conclusion This study demonstrates the strengths of a multi-modal diagnostic tool using both otoscopy images and wideband tympanometry measurements for the diagnosis of otitis media. Furthermore, we show that deep metric learning improves the performance of the models.
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Affiliation(s)
| | - Morten Rieger Hannemose
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkDenmark
| | - Søren Laugesen
- Interacoustics Research UnitTechnical University of DenmarkLyngbyDenmark
| | | | - James Harte
- Interacoustics Research UnitTechnical University of DenmarkLyngbyDenmark
| | | | | | - Rasmus R. Paulsen
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkDenmark
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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: 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: 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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Petsiou DP, Martinos A, Spinos D. Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges. Cureus 2023; 15:e44591. [PMID: 37795060 PMCID: PMC10545916 DOI: 10.7759/cureus.44591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2023] [Indexed: 10/06/2023] Open
Abstract
The applications of artificial intelligence (AI) in temporal bone (TB) imaging have gained significant attention in recent years, revolutionizing the field of otolaryngology and radiology. Accurate interpretation of imaging features of TB conditions plays a crucial role in diagnosing and treating a range of ear-related pathologies, including middle and inner ear diseases, otosclerosis, and vestibular schwannomas. According to multiple clinical studies published in the literature, AI-powered algorithms have demonstrated exceptional proficiency in interpreting imaging findings, not only saving time for physicians but also enhancing diagnostic accuracy by reducing human error. Although several challenges remain in routinely relying on AI applications, the collaboration between AI and healthcare professionals holds the key to better patient outcomes and significantly improved patient care. This overview delivers a comprehensive update on the advances of AI in the field of TB imaging, summarizes recent evidence provided by clinical studies, and discusses future insights and challenges in the widespread integration of AI in clinical practice.
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Affiliation(s)
- Dioni-Pinelopi Petsiou
- Otolaryngology-Head and Neck Surgery, National and Kapodistrian University of Athens, School of Medicine, Athens, GRC
| | - Anastasios Martinos
- Otolaryngology-Head and Neck Surgery, National and Kapodistrian University of Athens, School of Medicine, Athens, GRC
| | - Dimitrios Spinos
- Otolaryngology-Head and Neck Surgery, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, GBR
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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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11
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Cao C, Song J, Su R, Wu X, Wang Z, Hou M. Structure-constrained deep feature fusion for chronic otitis media and cholesteatoma identification. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-21. [PMID: 37362730 PMCID: PMC10157598 DOI: 10.1007/s11042-023-15425-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/19/2023] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) were two most common chronic middle ear disease(MED) clinically. Accurate differential diagnosis between these two diseases is of high clinical importance given the difference in etiologies, lesion manifestations and treatments. The high-resolution computed tomography (CT) scanning of the temporal bone presents a better view of auditory structures, which is currently regarded as the first-line diagnostic imaging modality in the case of MED. In this paper, we first used a region-of-interest (ROI) network to find the area of the middle ear in the entire temporal bone CT image and segment it to a size of 100*100 pixels. Then, we used a structure-constrained deep feature fusion algorithm to convert different characteristic features of the middle ear in three groups as suppurative otitis media (CSOM), middle ear cholesteatoma (MEC) and normal patches. To fuse structure information, we introduced a graph isomorphism network that implements a feature vector from neighbourhoods and the coordinate distance between vertices. Finally, we construct a classifier named the "otitis media, cholesteatoma and normal identification classifier" (OMCNIC). The experimental results achieved by the graph isomorphism network revealed a 96.36% accuracy in all CSOM and MEC classifications. The experimental results indicate that our structure-constrained deep feature fusion algorithm can quickly and effectively classify CSOM and MEC. It will help otologist in the selection of the most appropriate treatment, and the complications can also be reduced.
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Affiliation(s)
- Cong Cao
- School of Mathematics and Statistics, Central South University, Changsha, 410083 China
| | - Jian Song
- Department of Otorhinolaryngology of Xiangya Hospital, Central South University, Changsha, 410008 China
- Key Laboratory of Otolaryngology Major Disease Research of Hunan Province, Changsha, 410008 China
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, 410008 China
| | - Ri Su
- School of Mathematics and Statistics, Central South University, Changsha, 410083 China
| | - Xuewen Wu
- Department of Otorhinolaryngology of Xiangya Hospital, Central South University, Changsha, 410008 China
- Key Laboratory of Otolaryngology Major Disease Research of Hunan Province, Changsha, 410008 China
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, 410008 China
| | - Zheng Wang
- School of Computer Science, Hunan First Normal University, Changsha, 410205 China
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha, 410083 China
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12
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A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images. Eur Arch Otorhinolaryngol 2023; 280:1621-1627. [PMID: 36227348 PMCID: PMC9988777 DOI: 10.1007/s00405-022-07632-z] [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: 06/29/2022] [Accepted: 08/25/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. METHOD A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. RESULTS Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. CONCLUSION The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME.
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13
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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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14
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Huang Z, Chen S, Ali HE, Elkamchouchi DH, Hu J, Ali E, Zhang J, Huang Y. Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability. CHEMOSPHERE 2023; 331:138458. [PMID: 36966931 DOI: 10.1016/j.chemosphere.2023.138458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/23/2023] [Accepted: 03/17/2023] [Indexed: 05/05/2023]
Abstract
Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.
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Affiliation(s)
- Zhongguan Huang
- Department of Otolaryngology, Pingyang Hospital Affiliated to Wenzhou Medical University, Pingyang, Zhejiang, 325400, China
| | - Shuainan Chen
- Department of Otolaryngology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - H Elhosiny Ali
- Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia
| | - Dalia H Elkamchouchi
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Jun Hu
- Department of Otolaryngology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Elimam Ali
- Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Jie Zhang
- Department of Otolaryngology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Yideng Huang
- Department of Otolaryngology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
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15
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A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade. INFORMATION 2023. [DOI: 10.3390/info14030192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023] Open
Abstract
Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms.
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16
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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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17
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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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18
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Chen YC, Chu YC, Huang CY, Lee YT, Lee WY, Hsu CY, Yang AC, Liao WH, Cheng YF. Smartphone-based artificial intelligence using a transfer learning algorithm for the detection and diagnosis of middle ear diseases: A retrospective deep learning study. EClinicalMedicine 2022; 51:101543. [PMID: 35856040 PMCID: PMC9287624 DOI: 10.1016/j.eclinm.2022.101543] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/09/2022] [Accepted: 06/13/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Middle ear diseases such as otitis media and middle ear effusion, for which diagnoses are often delayed or misdiagnosed, are among the most common issues faced by clinicians providing primary care for children and adolescents. Artificial intelligence (AI) has the potential to assist clinicians in the detection and diagnosis of middle ear diseases through imaging. METHODS Otoendoscopic images obtained by otolaryngologists from Taipei Veterans General Hospital in Taiwan between Jany 1, 2011 to Dec 31, 2019 were collected retrospectively and de-identified. The images were entered into convolutional neural network (CNN) training models after data pre-processing, augmentation and splitting. To differentiate sophisticated middle ear diseases, nine CNN-based models were constructed to recognize middle ear diseases. The best-performing models were chosen and ensembled in a small CNN for mobile device use. The pretrained model was converted into the smartphone-based program, and the utility was evaluated in terms of detecting and classifying ten middle ear diseases based on otoendoscopic images. A class activation map (CAM) was also used to identify key features for CNN classification. The performance of each classifier was determined by its accuracy, precision, recall, and F1-score. FINDINGS A total of 2820 clinical eardrum images were collected for model training. The programme achieved a high detection accuracy for binary outcomes (pass/refer) of otoendoscopic images and ten different disease categories, with an accuracy reaching 98.0% after model optimisation. Furthermore, the application presented a smooth recognition process and a user-friendly interface and demonstrated excellent performance, with an accuracy of 97.6%. A fifty-question questionnaire related to middle ear diseases was designed for practitioners with different levels of clinical experience. The AI-empowered mobile algorithm's detection accuracy was generally superior to that of general physicians, resident doctors, and otolaryngology specialists (36.0%, 80.0% and 90.0%, respectively). Our results show that the proposed method provides sufficient treatment recommendations that are comparable to those of specialists. INTERPRETATION We developed a deep learning model that can detect and classify middle ear diseases. The use of smartphone-based point-of-care diagnostic devices with AI-empowered automated classification can provide real-world smart medical solutions for the diagnosis of middle ear diseases and telemedicine. FUNDING This study was supported by grants from the Ministry of Science and Technology (MOST110-2622-8-075-001, MOST110-2320-B-075-004-MY3, MOST-110-2634-F-A49 -005, MOST110-2745-B-075A-001A and MOST110-2221-E-075-005), Veterans General Hospitals and University System of Taiwan Joint Research Program (VGHUST111-G6-11-2, VGHUST111c-140), and Taipei Veterans General Hospital (V111E-002-3).
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Affiliation(s)
- Yen-Chi Chen
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, NO. 201, Sec. 2, Shipai Rd., Beitou District, Taipei 112, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, 3F Shouren Building, No.155, Sec.2, Linong Street, Beitou District, Taipei 112, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Kaohsiung Municipal Gangshan Hospital (Outsourced by Show-Chwan Memorial Hospital), Kaohsiung 820, Taiwan
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Big Data Canter, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Department of Information Management, National Taipei University of Nursing and Health Sciences, 365 Ming-De Road, Taipei 112, Taiwan
| | - Chii-Yuan Huang
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, NO. 201, Sec. 2, Shipai Rd., Beitou District, Taipei 112, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yen-Ting Lee
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, NO. 201, Sec. 2, Shipai Rd., Beitou District, Taipei 112, Taiwan
| | - Wen-Ya Lee
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, NO. 201, Sec. 2, Shipai Rd., Beitou District, Taipei 112, Taiwan
| | - Chien-Yeh Hsu
- Department of Information Management, National Taipei University of Nursing and Health Sciences, 365 Ming-De Road, Taipei 112, Taiwan
- Master Program in Global Health and Development, College of Public Health, Taipei Medical University, 250 Wu-Hsing Street, Taipei 110, Taiwan
| | - Albert C. Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, 3F Shouren Building, No.155, Sec.2, Linong Street, Beitou District, Taipei 112, Taiwan
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Corresponding authors.
| | - Wen-Huei Liao
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, NO. 201, Sec. 2, Shipai Rd., Beitou District, Taipei 112, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Corresponding authors.
| | - Yen-Fu Cheng
- Department of Otolaryngology-Head and Neck Surgery, Taipei Veterans General Hospital, NO. 201, Sec. 2, Shipai Rd., Beitou District, Taipei 112, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, 3F Shouren Building, No.155, Sec.2, Linong Street, Beitou District, Taipei 112, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Corresponding authors.
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19
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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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20
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Montaha S, Azam S, Rafid AKMRH, Hasan MZ, Karim A, Hasib KM, Patel SK, Jonkman M, Mannan ZI. MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique. Front Med (Lausanne) 2022; 9:924979. [PMID: 36052321 PMCID: PMC9424498 DOI: 10.3389/fmed.2022.924979] [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: 04/25/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.
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Affiliation(s)
- Sidratul Montaha
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Sami Azam
- College of Engineering, IT & Environment, Charles Darwin University, Darwin, NT, Australia
| | | | - Md. Zahid Hasan
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Asif Karim
- College of Engineering, IT & Environment, Charles Darwin University, Darwin, NT, Australia
| | - Khan Md. Hasib
- Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
| | - Shobhit K. Patel
- Department of Computer Engineering, Marwadi University, Rajkot, India
| | - Mirjam Jonkman
- College of Engineering, IT & Environment, Charles Darwin University, Darwin, NT, Australia
| | - Zubaer Ibna Mannan
- Department of Smart Computing, Kyungdong University – Global Campus, Sokcho-si, South Korea
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Zeng J, Kang W, Chen S, Lin Y, Deng W, Wang Y, Chen G, Ma K, Zhao F, Zheng Y, Liang M, Zeng L, Ye W, Li P, Chen Y, Chen G, Gao J, Wu M, Su Y, Zheng Y, Cai Y. A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images. JAMA Otolaryngol Head Neck Surg 2022; 148:612-620. [PMID: 35588049 DOI: 10.1001/jamaoto.2022.0900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients. Objective To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features. Design, Setting, and Participants A retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features. Main Outcomes and Measures The performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, which is the harmonic mean of precision and recall; a higher F1 score means better performance). In addition, these evaluation parameters were compared to results obtained from the logistic regression model and predictions made by three otologists. Results The performance of the DL model in predicting CHL showed the AUC of 0.74, accuracy of 81%, and F1 score of 0.89. This was better than the results from the logistic regression model (ie, AUC of 0.60, accuracy of 76%, and F1 score of 0.82), and much improved on the performance of the 3 otologists; accuracy of 16%, 30%, 39%, and F1 scores of 0.09, 0.18, and 0.25, respectively. Furthermore, the DL model took 2.5 seconds to predict from 205 otoscopic images, whereas the 3 otologists spent 633 seconds, 645 seconds, and 692 seconds, respectively. Conclusions and Relevance The model in this diagnostic/prognostic study provided greater accuracy in prediction of CHL in ears with OME than those obtained from the logistic regression model and otologists. This indicates great potential for the use of artificial intelligence tools to facilitate CHL evaluation when CHL is unable to be measured.
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Affiliation(s)
- Junbo Zeng
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weibiao Kang
- The second Hospital, Medical College, Shantou University, Shantou, Guangdong Province, China
| | - Suijun Chen
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi Lin
- Jarvis Lab, Tencent, Shen Zhen city, Guangdong Province, China.,Hong Kong University of Science and Technology, Hong Kong, China
| | - Wenting Deng
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yajing Wang
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guisheng Chen
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kai Ma
- Jarvis Lab, Tencent, Shen Zhen city, Guangdong Province, China
| | - Fei Zhao
- Centre for Speech and Language Therapy and Hearing Science, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Wales, United Kingdom
| | - Yefeng Zheng
- Jarvis Lab, Tencent, Shen Zhen city, Guangdong Province, China
| | - Maojin Liang
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Linqi Zeng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Weijie Ye
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Peng Li
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yubin Chen
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guoping Chen
- Department of Otolaryngology, Zhongshan City People's Hospital, Zhongshan Affiliated Hospital of Sun Yat-sen University, Zhongshan, Guangdong Province, China
| | - Jinliang Gao
- Department of Otolaryngology, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, Guangdong Province, China
| | - Minjian Wu
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuejia Su
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yiqing Zheng
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Shenzhen-Shanwei Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei City, Guangdong Province, China
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Shenzhen-Shanwei Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei City, Guangdong Province, China
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22
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Habib AR, Crossland G, Patel H, Wong E, Kong K, Gunasekera H, Richards B, Caffery L, Perry C, Sacks R, Kumar A, Singh N. An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children. Otol Neurotol 2022; 43:481-488. [PMID: 35239622 DOI: 10.1097/mao.0000000000003484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children. STUDY DESIGN Retrospective observational study. SETTING Tertiary referral center. PATIENTS Rural and remote Aboriginal and Torres Strait Islander children who underwent tele-otology ear health screening in the Northern Territory, Australia between 2010 and 2018. INTERVENTIONS Otoscopic images were labeled by otolaryngologists to classify the ground truth. Deep and transfer learning methods were used to develop an image classification algorithm. MAIN OUTCOME MEASURES Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC) of the resultant algorithm compared with the ground truth. RESULTS Six thousand five hundred twenty seven images were used (5927 images for training and 600 for testing). The algorithm achieved an accuracy of 99.3% for acute otitis media, 96.3% for chronic otitis media, 77.8% for otitis media with effusion (OME), and 98.2% to classify wax/obstructed canal. To differentiate between multiple diagnoses, the algorithm achieved 74.4 to 92.8% accuracy and an AUC of 0.963 to 0.997. The most common incorrect classification pattern was OME misclassified as normal tympanic membranes. CONCLUSIONS The paucity of access to tertiary otolaryngology care for rural and remote Aboriginal and Torres Strait Islander communities may contribute to an under-identification of ear disease. Computer vision image classification algorithms can accurately classify ear disease from otoscopic images of Indigenous Australian children. In the future, a validated algorithm may integrate with existing telemedicine initiatives to support effective triage and facilitate early treatment and referral.
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Affiliation(s)
- Al-Rahim Habib
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
- Department of Otolaryngology-Head and Neck Surgery, Princess Alexandra Hospital, Brisbane, Queensland, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Sydney, New South Wales, Australia
| | - Graeme Crossland
- Department of Otolaryngology - Head and Neck Surgery, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Hemi Patel
- Department of Otolaryngology - Head and Neck Surgery, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Eugene Wong
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Sydney, New South Wales, Australia
| | - Kelvin Kong
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia
- Department of Linguistics, Faculty of Medicine, Macquarie University, Sydney, New South Wales, Australia
- School of Population Health, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Hasantha Gunasekera
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
- The Children's Hospital at Westmead, Sydney, New South Wales, Australia
| | - Brent Richards
- Division of Medical Services, Gold Coast University Hospital, Gold Coast, Queensland, Australia
- Griffith Health, Griffith University Queensland, Australia
| | - Liam Caffery
- Centre for Online Health, University of Queensland, Australia
| | - Chris Perry
- Centre for Online Health, University of Queensland, Australia
| | - Raymond Sacks
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
| | - Ashnil Kumar
- School of Biomedical Engineering, Faculty of Engineering, University of Sydney, Camperdown, New South Wales, Australia
| | - Narinder Singh
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Sydney, New South Wales, Australia
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23
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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: 17] [Impact Index Per Article: 8.5] [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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24
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Wang W, Tamhane A, Santos C, Rzasa JR, Clark JH, Canares TL, Unberath M. Pediatric Otoscopy Video Screening With Shift Contrastive Anomaly Detection. Front Digit Health 2022; 3:810427. [PMID: 35224535 PMCID: PMC8866874 DOI: 10.3389/fdgth.2021.810427] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 12/28/2021] [Indexed: 11/13/2022] Open
Abstract
Ear related concerns and symptoms represent the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered diseases of the middle and external presents a significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitates the presence or absence of ear pathology to be determined clinically. Research has, however, demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. With recent advances in computer vision and machine learning, there is an increasing interest in helping clinicians to accurately diagnose middle and external ear pathology with computer-aided systems. It has been shown that AI has the capacity to analyze a single clinical image captured during the examination of the ear canal and eardrum from which it can determine the likelihood of a pathognomonic pattern for a specific diagnosis being present. The capture of such an image can, however, be challenging especially to inexperienced clinicians. To help mitigate this technical challenge, we have developed and tested a method using video sequences. The videos were collected using a commercially available otoscope smartphone attachment in an urban, tertiary-care pediatric emergency department. We present a two stage method that first, identifies valid frames by detecting and extracting ear drum patches from the video sequence, and second, performs the proposed shift contrastive anomaly detection (SCAD) to flag the otoscopy video sequences as normal or abnormal. Our method achieves an AUROC of 88.0% on the patient level and also outperforms the average of a group of 25 clinicians in a comparative study, which is the largest of such published to date. We conclude that the presented method achieves a promising first step toward the automated analysis of otoscopy video.
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Affiliation(s)
- Weiyao Wang
- Department of Computer Science, Johns Hopkins University School of Engineering, Baltimore, MA, United States
- *Correspondence: Weiyao Wang
| | - Aniruddha Tamhane
- Department of Computer Science, Johns Hopkins University School of Engineering, Baltimore, MA, United States
| | - Christine Santos
- Department of Pediatric, Johns Hopkins University School of Medicine, Baltimore, MA, United States
| | - John R. Rzasa
- Robert E. Fischell Institute for Biomedical Devices, University of Maryland, College Park, MA, United States
| | - James H. Clark
- Department of Otolaryngology, Johns Hopkins University School of Medicine, Baltimore, MA, United States
| | - Therese L. Canares
- Department of Pediatric, Johns Hopkins University School of Medicine, Baltimore, MA, United States
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University School of Engineering, Baltimore, MA, United States
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25
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Chawdhary G, Shoman N. Emerging artificial intelligence applications in otological imaging. Curr Opin Otolaryngol Head Neck Surg 2021; 29:357-364. [PMID: 34459798 DOI: 10.1097/moo.0000000000000754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW To highlight the recent literature on artificial intelligence (AI) pertaining to otological imaging and to discuss future directions, obstacles and opportunities. RECENT FINDINGS The main themes in the recent literature centre around automated otoscopic image diagnosis and automated image segmentation for application in virtual reality surgical simulation and planning. Other applications that have been studied include identification of tinnitus MRI biomarkers, facial palsy analysis, intraoperative augmented reality systems, vertigo diagnosis and endolymphatic hydrops ratio calculation in Meniere's disease. Studies are presently at a preclinical, proof-of-concept stage. SUMMARY The recent literature on AI in otological imaging is promising and demonstrates the future potential of this technology in automating certain imaging tasks in a healthcare environment of ever-increasing demand and workload. Some studies have shown equivalence or superiority of the algorithm over physicians, albeit in narrowly defined realms. Future challenges in developing this technology include the compilation of large high quality annotated datasets, fostering strong collaborations between the health and technology sectors, testing the technology within real-world clinical pathways and bolstering trust among patients and physicians in this new method of delivering healthcare.
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Affiliation(s)
- Gaurav Chawdhary
- ENT Department, Royal Hallamshire Hospital, Broomhall, Sheffield, UK
| | - Nael Shoman
- ENT Department, Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
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26
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Canares TL, Wang W, Unberath M, Clark JH. Artificial intelligence to diagnose ear disease using otoscopic image analysis: a review. J Investig Med 2021; 70:354-362. [PMID: 34521730 DOI: 10.1136/jim-2021-001870] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/27/2021] [Indexed: 12/22/2022]
Abstract
AI relates broadly to the science of developing computer systems to imitate human intelligence, thus allowing for the automation of tasks that would otherwise necessitate human cognition. Such technology has increasingly demonstrated capacity to outperform humans for functions relating to image recognition. Given the current lack of cost-effective confirmatory testing, accurate diagnosis and subsequent management depend on visual detection of characteristic findings during otoscope examination. The aim of this manuscript is to perform a comprehensive literature review and evaluate the potential application of artificial intelligence for the diagnosis of ear disease from otoscopic image analysis.
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Affiliation(s)
- Therese L Canares
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Weiyao Wang
- Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland, USA
| | - Mathias Unberath
- Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland, USA
| | - James H Clark
- Otolaryngology-HNS, Johns Hopkins Medicine School of Medicine, Baltimore, Maryland, USA
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27
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Byun H, Yu S, Oh J, Bae J, Yoon MS, Lee SH, Chung JH, Kim TH. An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases. J Clin Med 2021; 10:jcm10153198. [PMID: 34361982 PMCID: PMC8347824 DOI: 10.3390/jcm10153198] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/16/2021] [Accepted: 07/19/2021] [Indexed: 02/07/2023] Open
Abstract
The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the “ResNet18 + Shuffle” network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.
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Affiliation(s)
- Hayoung Byun
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea; (H.B.); (S.H.L.)
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
| | - Sangjoon Yu
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
| | - Jaehoon Oh
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Junwon Bae
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Myeong Seong Yoon
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea
| | - Seung Hwan Lee
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea; (H.B.); (S.H.L.)
| | - Jae Ho Chung
- Department of Otolaryngology & Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea; (H.B.); (S.H.L.)
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of HY-KIST Bio-Convergence, College of Medicine, Hanyang University, Seoul 04763, Korea
- Correspondence: (J.H.C.); (T.H.K.)
| | - Tae Hyun Kim
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04763, Korea; (S.Y.); (J.O.); (J.B.); (M.S.Y.)
- Department of Computer Science, Hanyang University, Seoul 04763, Korea
- Correspondence: (J.H.C.); (T.H.K.)
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28
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Kashani RG, Młyńczak MC, Zarabanda D, Solis-Pazmino P, Huland DM, Ahmad IN, Singh SP, Valdez TA. Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach. Sci Rep 2021; 11:12509. [PMID: 34131163 PMCID: PMC8206083 DOI: 10.1038/s41598-021-91736-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/04/2021] [Indexed: 02/05/2023] Open
Abstract
Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains challenging due to the inherent limitations of visible light otoscopy and user interpretation. Here we describe a powerful diagnostic approach to otitis media utilizing advancements in otoscopy and machine learning. We developed an otoscope that visualizes middle ear structures and fluid in the shortwave infrared region, holding several advantages over traditional approaches. Images were captured in vivo and then processed by a novel machine learning based algorithm. The model predicts the presence of effusions with greater accuracy than current techniques, offering specificity and sensitivity over 90%. This platform has the potential to reduce costs and resources associated with otitis media, especially as improvements are made in shortwave imaging and machine learning.
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Affiliation(s)
- Rustin G. Kashani
- grid.168010.e0000000419368956Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94304 USA
| | - Marcel C. Młyńczak
- grid.1035.70000000099214842Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland
| | - David Zarabanda
- grid.168010.e0000000419368956Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94304 USA
| | - Paola Solis-Pazmino
- grid.168010.e0000000419368956Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94304 USA
| | - David M. Huland
- grid.168010.e0000000419368956Department of Radiology, Stanford University School of Medicine, Palo Alto, CA USA
| | - Iram N. Ahmad
- grid.168010.e0000000419368956Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94304 USA ,grid.414123.10000 0004 0450 875XLucile Packard Children’s Hospital, Palo Alto, CA USA
| | - Surya P. Singh
- grid.495560.b0000 0004 6003 8393Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad, Karnataka India
| | - Tulio A. Valdez
- grid.168010.e0000000419368956Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA 94304 USA ,grid.414123.10000 0004 0450 875XLucile Packard Children’s Hospital, Palo Alto, CA USA
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29
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Kim T, Kim J, Choi HS, Kim ES, Keum B, Jeen YT, Lee HS, Chun HJ, Han SY, Kim DU, Kwon S, Choo J, Lee JM. Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation. Sci Rep 2021; 11:8381. [PMID: 33863970 PMCID: PMC8052314 DOI: 10.1038/s41598-021-87737-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/17/2021] [Indexed: 12/21/2022] Open
Abstract
The advancement of artificial intelligence (AI) has facilitated its application in medical fields. However, there has been little research for AI-assisted endoscopy, despite the clinical significance of the efficiency and safety of cannulation in the endoscopic retrograde cholangiopancreatography (ERCP). In this study, we aim to assist endoscopists performing ERCP through automatic detection of the ampulla and the identification of cannulation difficulty. We developed a novel AI-assisted system based on convolutional neural networks that predict the location of the ampulla and the difficulty of cannulation to the ampulla. ERCP data of 531 and 451 patients were utilized in the evaluation of our model for each task. Our model detected the ampulla with mean intersection-over-union 64.1%, precision 76.2%, recall 78.4%, and centroid distance 0.021. In classifying the cannulation difficulty, it achieved the recall of 71.9% for the class of easy cases and that of 61.1% for that of difficult cases. Remarkably, our model accurately detected AOV with varying morphological shape, size, and texture on par with the level of a human expert and showed promising results for recognizing cannulation difficulty. It demonstrated its potential to improve the quality of ERCP by assisting endoscopists.
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Affiliation(s)
- Taesung Kim
- Graduate School of Artificial Intelligence, KAIST, Daehak-ro 291, Yuseong-gu, Daejeon, 34141, Korea
| | - Jinhee Kim
- Graduate School of Artificial Intelligence, KAIST, Daehak-ro 291, Yuseong-gu, Daejeon, 34141, Korea
| | - Hyuk Soon Choi
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Korea University Medical Center, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Korea
| | - Eun Sun Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Korea University Medical Center, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Korea
| | - Bora Keum
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Korea University Medical Center, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Korea
| | - Yoon Tae Jeen
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Korea University Medical Center, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Korea
| | - Hong Sik Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Korea University Medical Center, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Korea
| | - Hoon Jai Chun
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Korea University Medical Center, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Korea
| | - Sung Yong Han
- Department of Internal Medicine, Pusan National University College of Medicine, Pusan, Korea
| | - Dong Uk Kim
- Department of Internal Medicine, Pusan National University College of Medicine, Pusan, Korea
| | - Soonwook Kwon
- Department of Anatomy, Catholic University of Daegu, Daegu, Korea
| | - Jaegul Choo
- Graduate School of Artificial Intelligence, KAIST, Daehak-ro 291, Yuseong-gu, Daejeon, 34141, Korea.
| | - Jae Min Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Korea University Medical Center, Goryeodae-ro 73, Seongbuk-gu, Seoul, 02841, Korea.
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EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images. Artif Intell Med 2021; 115:102065. [PMID: 34001323 DOI: 10.1016/j.artmed.2021.102065] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 04/02/2021] [Accepted: 04/05/2021] [Indexed: 11/20/2022]
Abstract
This paper presents a method for automatic segmentation of tympanic membranes (TMs) from video-otoscopic images based on deep fully convolutional neural network. Built upon the UNet architecture, the proposed EAR scheme is based on three main paradigms: EfficientNet for the encoder, Attention gate for the skip connection path, and Residual blocks for the decoder. The paper also introduces a new loss function term for the neural networks to perform segmentation tasks. Particularly, we propose to integrate EfficientNet-B4 into the encoder part of the UNet. In addition, the decoder part of the proposed network is constructed based on residual blocks from ResNet architecture. By this way, the proposed approach could take advantages of the EfficientNet and ResNet architectures such as preserving efficient reception field size for the model and avoiding overfitting problem. In addition, in the skip connection path, we employ the attention gate that can handle the varieties in shapes and sizes of interested objects, which are common issues in TM regions. Moreover, for network training, we proposed a new loss function term based on the shape distance between predicted and ground truth masks, and exploited the stochastic weight averaging to avoid being trapped in local minima. We evaluate the proposed approach on a TM dataset which includes 1012 otoscopic images from patients diagnosed with and without otitis media. Experimental results show that the proposed approach achieves high segmentation performance with the average Dice similarity coefficient of 0.929, without any pre- or post-processing steps, that outperforms other state-of-the-art methods.
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OtoPair: Combining Right and Left Eardrum Otoscopy Images to Improve the Accuracy of Automated Image Analysis. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041831] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The accurate diagnosis of otitis media (OM) and other middle ear and eardrum abnormalities is difficult, even for experienced otologists. In our earlier studies, we developed computer-aided diagnosis systems to improve the diagnostic accuracy. In this study, we investigate a novel approach, called OtoPair, which uses paired eardrum images together rather than using a single eardrum image to classify them as ‘normal’ or ‘abnormal’. This also mimics the way that otologists evaluate ears, because they diagnose eardrum abnormalities by examining both ears. Our approach creates a new feature vector, which is formed with extracted features from a pair of high-resolution otoscope images or images that are captured by digital video-otoscopes. The feature vector has two parts. The first part consists of lookup table-based values created by using deep learning techniques reported in our previous OtoMatch content-based image retrieval system. The second part consists of handcrafted features that are created by recording registration errors between paired eardrums, color-based features, such as histogram of a* and b* component of the L*a*b* color space, and statistical measurements of these color channels. The extracted features are concatenated to form a single feature vector, which is then classified by a tree bagger classifier. A total of 150-pair (300-single) of eardrum images, which are either the same category (normal-normal and abnormal-abnormal) or different category (normal-abnormal and abnormal-normal) pairs, are used to perform several experiments. The proposed approach increases the accuracy from 78.7% (±0.1%) to 85.8% (±0.2%) on a three-fold cross-validation method. These are promising results with a limited number of eardrum pairs to demonstrate the feasibility of using a pair of eardrum images instead of single eardrum images to improve the diagnostic accuracy.
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Uçar M, Akyol K, Atila Ü, Uçar E. Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.01.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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