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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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Song D, Kim T, Lee Y, Kim J. Image-Based Artificial Intelligence Technology for Diagnosing Middle Ear Diseases: A Systematic Review. J Clin Med 2023; 12:5831. [PMID: 37762772 PMCID: PMC10531728 DOI: 10.3390/jcm12185831] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
Otolaryngological diagnoses, such as otitis media, are traditionally performed using endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians. The integration of objective tools, like artificial intelligence (AI), could potentially improve the diagnostic process by minimizing the influence of subjective biases and variability. We systematically reviewed the AI techniques using medical imaging in otolaryngology. Relevant studies related to AI-assisted otitis media diagnosis were extracted from five databases: Google Scholar, PubMed, Medline, Embase, and IEEE Xplore, without date restrictions. Publications that did not relate to AI and otitis media diagnosis or did not utilize medical imaging were excluded. Of the 32identified studies, 26 used tympanic membrane images for classification, achieving an average diagnosis accuracy of 86% (range: 48.7-99.16%). Another three studies employed both segmentation and classification techniques, reporting an average diagnosis accuracy of 90.8% (range: 88.06-93.9%). These findings suggest that AI technologies hold promise for improving otitis media diagnosis, offering benefits for telemedicine and primary care settings due to their high diagnostic accuracy. However, to ensure patient safety and optimal outcomes, further improvements in diagnostic performance are necessary.
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Affiliation(s)
- Dahye Song
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Taewan Kim
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Yeonjoon Lee
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Jaeyoung Kim
- Department of Dermatology and Skin Sciences, University of British Columbia, Vancouver, BC V6T 1Z1, Canada;
- Core Research & Development Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
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Ding X, Huang Y, Tian X, Zhao Y, Feng G, Gao Z. Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence. Diagnostics (Basel) 2023; 13:2309. [PMID: 37443702 DOI: 10.3390/diagnostics13132309] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/04/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which significantly increases the difficulty of treating the disease and the likelihood of serious complications developing including hearing loss, speech impairment, and even intracranial infection. Several areas of healthcare have shown great promise in the application of artificial intelligence (AI) systems, such as the accurate detection of diseases, the automated interpretation of images, and the prediction of patient outcomes. Several articles have reported some machine learning (ML) algorithms such as ResNet, InceptionV3 and Unet, were applied to the diagnosis of OM successfully. The use of these techniques in the OM is still in its infancy, but their potential is enormous. We present in this review important concepts related to ML and AI, describe how these technologies are currently being applied to diagnosing, treating, and managing OM, and discuss the challenges associated with developing AI-assisted OM technologies in the future.
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Affiliation(s)
- Xin Ding
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Yu Huang
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Xu Tian
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Yang Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Guodong Feng
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Zhiqiang Gao
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
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Abid MH, Ashraf R, Mahmood T, Faisal CMN. Multi-modal medical image classification using deep residual network and genetic algorithm. PLoS One 2023; 18:e0287786. [PMID: 37384779 PMCID: PMC10309999 DOI: 10.1371/journal.pone.0287786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 06/13/2023] [Indexed: 07/01/2023] Open
Abstract
Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments. Precise and consistent image classification has critical in diagnosing and tactical decisions for healthcare. The core issue with image classification has become the semantic gap. Conventional machine learning algorithms for classification rely mainly on low-level but rather high-level characteristics, employ some handmade features to close the gap, but force intense feature extraction as well as classification approaches. Deep learning is a powerful tool with considerable advances in recent years, with deep convolution neural networks (CNNs) succeeding in image classification. The main goal is to bridge the semantic gap and enhance the classification performance of multi-modal medical images based on the deep learning-based model ResNet50. The data set included 28378 multi-modal medical images to train and validate the model. Overall accuracy, precision, recall, and F1-score evaluation parameters have been calculated. The proposed model classifies medical images more accurately than other state-of-the-art methods. The intended research experiment attained an accuracy level of 98.61%. The suggested study directly benefits the health service.
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Affiliation(s)
- Muhammad Haris Abid
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - Rehan Ashraf
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - Toqeer Mahmood
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - C. M. Nadeem Faisal
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
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5
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Habib AR, Xu Y, Bock K, Mohanty S, Sederholm T, Weeks WB, Dodhia R, Ferres JL, Perry C, Sacks R, Singh N. Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy. Sci Rep 2023; 13:5368. [PMID: 37005441 PMCID: PMC10067817 DOI: 10.1038/s41598-023-31921-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 03/20/2023] [Indexed: 04/04/2023] Open
Abstract
To evaluate the generalizability of artificial intelligence (AI) algorithms that use deep learning methods to identify middle ear disease from otoscopic images, between internal to external performance. 1842 otoscopic images were collected from three independent sources: (a) Van, Turkey, (b) Santiago, Chile, and (c) Ohio, USA. Diagnostic categories consisted of (i) normal or (ii) abnormal. Deep learning methods were used to develop models to evaluate internal and external performance, using area under the curve (AUC) estimates. A pooled assessment was performed by combining all cohorts together with fivefold cross validation. AI-otoscopy algorithms achieved high internal performance (mean AUC: 0.95, 95%CI: 0.80-1.00). However, performance was reduced when tested on external otoscopic images not used for training (mean AUC: 0.76, 95%CI: 0.61-0.91). Overall, external performance was significantly lower than internal performance (mean difference in AUC: -0.19, p ≤ 0.04). Combining cohorts achieved a substantial pooled performance (AUC: 0.96, standard error: 0.01). Internally applied algorithms for otoscopy performed well to identify middle ear disease from otoscopy images. However, external performance was reduced when applied to new test cohorts. Further efforts are required to explore data augmentation and pre-processing techniques that might improve external performance and develop a robust, generalizable algorithm for real-world clinical applications.
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Affiliation(s)
- Al-Rahim Habib
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
- Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Sydney, NSW, Australia.
| | - Yixi Xu
- AI for Good Lab, Microsoft, Redmond, WA, USA
| | - Kris Bock
- Azure FastTrack Engineering, Brisbane, QLD, Australia
| | | | | | | | | | | | - Chris Perry
- University of Queensland Medical School, Brisbane, QLD, Australia
| | - Raymond Sacks
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Narinder Singh
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Sydney, NSW, Australia
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Mao S, Wu X, Hou M, Mei L, Feng Y, Song J. Research and application progress in deep learning in otology. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:463-471. [PMID: 37164930 PMCID: PMC10930069 DOI: 10.11817/j.issn.1672-7347.2023.210588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Indexed: 05/12/2023]
Abstract
With the optimization of deep learning algorithms and the accumulation of medical big data, deep learning technology has been widely applied in research in various fields of otology in recent years. At present, research on deep learning in otology is combined with a variety of data such as endoscopy, temporal bone images, audiograms, and intraoperative images, which involves diagnosis of otologic diseases (including auricular malformations, external auditory canal diseases, middle ear diseases, and inner ear diseases), treatment (guiding medication and surgical planning), and prognosis prediction (involving hearing regression and speech learning). According to the type of data and the purpose of the study (disease diagnosis, treatment and prognosis), the different neural network models can be used to take advantage of their algorithms, and the deep learning can be a good aid in treating otologic diseases. The deep learning has a good applicable prospect in the clinical diagnosis and treatment of otologic diseases, which can play a certain role in promoting the development of deep learning combined with intelligent medicine.
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Affiliation(s)
- Shuang Mao
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008.
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008.
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008.
| | - Xuewen Wu
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha 410083
| | - Lingyun Mei
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008
| | - Yong Feng
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008
- Department of Otorhinolaryngology Head and Neck Surgery, Changsha Central Hospital Affiliated to South China University, Changsha 410018, China
| | - Jian Song
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008.
- Hunan Provincial Key Laboratory of Major Otorhinolaryngology Diseases, Changsha 410008.
- National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), Changsha 410008.
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7
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Alarcão SM, Mendonça V, Maruta C, Fonseca MJ. ExpertosLF: dynamic late fusion of CBIR systems using online learning with relevance feedback. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:11619-11661. [PMID: 36035324 PMCID: PMC9391217 DOI: 10.1007/s11042-022-13119-0] [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: 06/21/2021] [Revised: 01/11/2022] [Accepted: 04/10/2022] [Indexed: 06/15/2023]
Abstract
One of the main challenges in CBIR systems is to choose discriminative and compact features, among dozens, to represent the images under comparison. Over the years, a great effort has been made to combine multiple features, mainly using early, late, and hierarchical fusion techniques. Unveiling the perfect combination of features is highly domain-specific and dependent on the type of image. Thus, the process of designing a CBIR system for new datasets or domains involves a huge experimentation overhead, leading to multiple fine-tuned CBIR systems. It would be desirable to dynamically find the best combination of CBIR systems without needing to go through such extensive experimentation and without requiring previous domain knowledge. In this paper, we propose ExpertosLF, a model-agnostic interpretable late fusion technique based on online learning with expert advice, which dynamically combines CBIR systems without knowing a priori which ones are the best for a given domain. At each query, ExpertosLF takes advantage of user's feedback to determine each CBIR contribution in the ensemble for the following queries. ExpertosLF produces an interpretable ensemble that is independent of the dataset and domain. Moreover, ExpertosLF is designed to be modular, and scalable. Experiments on 13 benchmark datasets from the Biomedical, Real, and Sketch domains revealed that: (i) ExpertosLF surpasses the performance of state of the art late-fusion techniques; (ii) it successfully and quickly converges to the performance of the best CBIR sets across domains without any previous domain knowledge (in most cases, fewer than 25 queries need to receive human feedback).
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Affiliation(s)
- Soraia M. Alarcão
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Vânia Mendonça
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Carolina Maruta
- Laboratório de Estudos de Linguagem, Centro de Estudos Egas Moniz, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - Manuel J. Fonseca
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
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Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery. REMOTE SENSING 2022. [DOI: 10.3390/rs14071746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Monitoring changes within the land surface and open water bodies is critical for natural resource management, conservation, and environmental policy. While the use of satellite imagery for these purposes is common, fine-scale change detection can be a technical challenge. Difficulties arise from variable atmospheric conditions and the problem of assigning pixels to individual objects. We examined the degree to which two machine learning approaches can better characterize change detection in the context of a current conservation challenge, artisanal small-scale gold mining (ASGM). We obtained Sentinel-2 imagery and consulted with domain experts to construct an open-source labeled land-cover change dataset. The focus of this dataset is the Madre de Dios (MDD) region in Peru, a hotspot of ASGM activity. We also generated datasets of active ASGM areas in other countries (Venezuela, Indonesia, and Myanmar) for out-of-sample testing. With these labeled data, we utilized a supervised (E-ReCNN) and semi-supervised (SVM-STV) approach to study binary and multi-class change within mining ponds in the MDD region. Additionally, we tested how the inclusion of multiple channels, histogram matching, and La*b* color metrics improved the performance of the models and reduced the influence of atmospheric effects. Empirical results show that the supervised E-ReCNN method on 6-Channel histogram-matched images generated the most accurate detection of change not only in the focal region (Kappa: 0.92 (± 0.04), Jaccard: 0.88 (± 0.07), F1: 0.88 (± 0.05)) but also in the out-of-sample prediction regions (Kappa: 0.90 (± 0.03), Jaccard: 0.84 (± 0.04), and F1: 0.77 (± 0.04)). While semi-supervised methods did not perform as accurately on 6- or 10-channel imagery, histogram matching and the inclusion of La*b* metrics generated accurate results with low memory and resource costs. These results show that E-ReCNN is capable of accurately detecting specific and object-oriented environmental changes related to ASGM. E-ReCNN is scalable to areas outside the focal area and is a method of change detection that can be extended to other forms of land-use modification.
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Binol H, Niazi MKK, Elmaraghy C, Moberly AC, Gurcan MN. OtoXNet—automated identification of eardrum diseases from otoscope videos: a deep learning study for video-representing images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07107-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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10
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Camalan S, Mahmood H, Binol H, Araújo ALD, Santos-Silva AR, Vargas PA, Lopes MA, Khurram SA, Gurcan MN. Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results. Cancers (Basel) 2021; 13:cancers13061291. [PMID: 33799466 PMCID: PMC8001078 DOI: 10.3390/cancers13061291] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 03/08/2021] [Accepted: 03/10/2021] [Indexed: 11/16/2022] Open
Abstract
Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as "suspicious" and "normal" by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method's feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.
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Affiliation(s)
- Seda Camalan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.B.); (M.N.G.)
- Correspondence: ; Tel.: +1-(336)-713-7675
| | - Hanya Mahmood
- School of Clinical Dentistry, The University of Sheffield, Sheffield S10 2TA, UK; (H.M.); (S.A.K.)
| | - Hamidullah Binol
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.B.); (M.N.G.)
| | - Anna Luiza Damaceno Araújo
- Oral Diagnosis Department, Semiology and Oral Pathology Areas, Piracicaba Dental School, University of Campinas (UNICAMP), Bairro Areão, Piracicaba 13414-903, São Paulo, Brazil; (A.L.D.A.); (A.R.S.-S.); (P.A.V.); (M.A.L.)
| | - Alan Roger Santos-Silva
- Oral Diagnosis Department, Semiology and Oral Pathology Areas, Piracicaba Dental School, University of Campinas (UNICAMP), Bairro Areão, Piracicaba 13414-903, São Paulo, Brazil; (A.L.D.A.); (A.R.S.-S.); (P.A.V.); (M.A.L.)
| | - Pablo Agustin Vargas
- Oral Diagnosis Department, Semiology and Oral Pathology Areas, Piracicaba Dental School, University of Campinas (UNICAMP), Bairro Areão, Piracicaba 13414-903, São Paulo, Brazil; (A.L.D.A.); (A.R.S.-S.); (P.A.V.); (M.A.L.)
| | - Marcio Ajudarte Lopes
- Oral Diagnosis Department, Semiology and Oral Pathology Areas, Piracicaba Dental School, University of Campinas (UNICAMP), Bairro Areão, Piracicaba 13414-903, São Paulo, Brazil; (A.L.D.A.); (A.R.S.-S.); (P.A.V.); (M.A.L.)
| | - Syed Ali Khurram
- School of Clinical Dentistry, The University of Sheffield, Sheffield S10 2TA, UK; (H.M.); (S.A.K.)
| | - Metin N. Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA; (H.B.); (M.N.G.)
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11
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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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Binol H, Niazi MKK, Essig G, Shah J, Mattingly JK, Harris MS, Elmaraghy C, Teknos T, Taj-Schaal N, Yu L, Gurcan MN, Moberly AC. Digital Otoscopy Videos Versus Composite Images: A Reader Study to Compare the Accuracy of ENT Physicians. Laryngoscope 2020; 131:E1668-E1676. [PMID: 33170529 DOI: 10.1002/lary.29253] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/24/2020] [Accepted: 10/27/2020] [Indexed: 11/09/2022]
Abstract
OBJECTIVES/HYPOTHESIS With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video. STUDY DESIGN Diagnostic survey analysis. METHODS Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image. RESULTS There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate. CONCLUSIONS Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training. LEVEL OF EVIDENCE 3 Laryngoscope, 131:E1668-E1676, 2021.
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Affiliation(s)
- Hamidullah Binol
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, U.S.A
| | | | - Garth Essig
- Department of Otolaryngology, Ohio State University, Columbus, Ohio, U.S.A
| | - Jay Shah
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, U.S.A
| | | | - Michael S Harris
- Otolaryngology and Communication Sciences, Froedtert Hospital, Wauwatosa, Wisconsin, U.S.A
| | - Charles Elmaraghy
- Department of Otolaryngology, Ohio State University, Columbus, Ohio, U.S.A
| | - Theodoros Teknos
- Head and Neck Cancer Center, University Hospitals Seidman Cancer Center, Cleveland, Ohio, U.S.A
| | - Nazhat Taj-Schaal
- Department of Internal Medicine, Ohio State University College of Medicine, Columbus, Ohio, U.S.A
| | - Lianbo Yu
- Department of Biomedical Informatics, Ohio State University, Columbus, Ohio, U.S.A
| | - Metin N Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, U.S.A
| | - Aaron C Moberly
- Department of Otolaryngology, Ohio State University, Columbus, Ohio, U.S.A
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