| 1. |
- Myburgh, Hermanus C., et al.
(author)
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Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis
- 2016
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In: EBioMedicine. - : Elsevier BV. - 2352-3964. ; 5, s. 156-160
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Journal article (peer-reviewed)abstract
- Background: Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. Methods: A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. Findings: An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. Interpretation: The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (similar to 64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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| 2. |
- Myburgh, Hermanus C., et al.
(author)
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Towards low cost automated smartphone- and cloud-based otitis media diagnosis
- 2018
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In: Biomedical Signal Processing and Control. - : Elsevier. - 1746-8094 .- 1746-8108. ; 39, s. 34-52
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Journal article (peer-reviewed)abstract
- Odds media is one of the most common childhood illnesses. Access to ear specialists and specialist equipment is rudimentary in many third world countries, and general practitioners do not always have enough experience in diagnosing the different otitis medias. In this paper a system recently proposed by three of the authors for automated diagnosis of middle ear pathology, or otitis media, is extended to enable the use of the system on a smartphone with an Internet connection. In addition, a neural network is also proposed in the current system as a classifier, and compared to a decision tree similar to what was proposed before. The system is able to diagnose with high accuracy (1) a normal tympanic membrane, (2) obstructing wax or foreign bodies in the external ear canal (W/O), (3) acute otitis media (AOM), (4) otitis media with effusion (OME) and (5) chronic suppurative otitis media (CSOM). The average classification accuracy of the proposed system is 81.58% (decision tree) and 86.84% (neural network) for images captured with commercial video-otoscopes, using 80% of the 389 images for training, and 20% for testing and validation.
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| 3. |
- Sandström, Josefin, et al.
(author)
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A Machine Learning Approach to Screen for Otitis Media Using Digital Otoscope Images Labelled by an Expert Panel
- 2022
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In: Diagnostics. - : MDPI. - 2075-4418. ; 12:6
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Journal article (peer-reviewed)abstract
- Background: Otitis media includes several common inflammatory conditions of the middle ear that can have severe complications if left untreated. Correctly identifying otitis media can be difficult and a screening system supported by machine learning would be valuable for this prevalent disease. This study investigated the performance of a convolutional neural network in screening for otitis media using digital otoscopic images labelled by an expert panel.Methods: Five experienced otologists diagnosed 347 tympanic membrane images captured with a digital otoscope. Images with a majority expert diagnosis (n = 273) were categorized into three screening groups Normal, Pathological and Wax, and the same images were used for training and testing of the convolutional neural network. Expert panel diagnoses were compared to the convolutional neural network classification. Different approaches to the convolutional neural network were tested to identify the best performing model.Results: Overall accuracy of the convolutional neural network was above 0.9 in all except one approach. Sensitivity to finding ears with wax or pathology was above 93% in all cases and specificity was 100%. Adding more images to train the convolutional neural network had no positive impact on the results. Modifications such as normalization of datasets and image augmentation enhanced the performance in some instances.Conclusions: A machine learning approach could be used on digital otoscopic images to accurately screen for otitis media.
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| 4. |
- Sandström, Josefin, et al.
(author)
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Smartphone threshold audiometry in underserved primary health-care contexts
- 2016
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In: International Journal of Audiology. - : Informa UK Limited. - 1499-2027 .- 1708-8186. ; 55:4, s. 232-238
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Journal article (peer-reviewed)abstract
- OBJECTIVE: To validate a calibrated smartphone-based hearing test in a sound booth environment and in primary health-care clinics.DESIGN: A repeated-measure within-subject study design was employed whereby air-conduction hearing thresholds determined by smartphone-based audiometry was compared to conventional audiometry in a sound booth and a primary health-care clinic environment.STUDY SAMPLE: A total of 94 subjects (mean age 41 years ± 17.6 SD and range 18-88; 64% female) were assessed of whom 64 were tested in the sound booth and 30 within primary health-care clinics without a booth.RESULTS: In the sound booth 63.4% of conventional and smartphone thresholds indicated normal hearing (≤15 dBHL). Conventional thresholds exceeding 15 dB HL corresponded to smartphone thresholds within ≤10 dB in 80.6% of cases with an average threshold difference of -1.6 dB ± 9.9 SD. In primary health-care clinics 13.7% of conventional and smartphone thresholds indicated normal hearing (≤15 dBHL). Conventional thresholds exceeding 15 dBHL corresponded to smartphone thresholds within ≤10 dB in 92.9% of cases with an average threshold difference of -1.0 dB ± 7.1 SD.CONCLUSIONS: Accurate air-conduction audiometry can be conducted in a sound booth and without a sound booth in an underserved community health-care clinic using a smartphone.
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