Prevalence of Chronic Suppurative Otitis Media in School Going Children of Patiala District of Punjab, India

Kaur, Imrinder; Goyal, Jagdish Prasad; Singh, Dalbir

doi:10.14260/jemds/2017/1171

Cited by 1 publication

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References 13 publications

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“…Otitis media is a prevalent ear disease that affects a significant portion of the global population, with an estimated 65 to 350 million individuals affected worldwide ( World Health Organization, 2004 ). In developing countries, the prevalence of Chronic Suppurative Otitis Media (CSOM) ranges from 0.4% to 33.3% ( Kaur et al, 2017 ). Our study mainly focuses on non-invasive temporal bone CT images, in order to help clinicians quickly get a relatively accurate preliminary diagnosis and lay the foundation for further judgment of whether patients need surgical treatment.…”

Section: Introductionmentioning

confidence: 99%

An automatic diagnosis model of otitis media with high accuracy rate using transfer learning

Qi,

You,

Guo

et al. 2024

Front. Mol. Biosci.

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Introduction: Chronic Suppurative Otitis Media (CSOM) and Middle Ear Cholesteatoma are two common chronic otitis media diseases that often cause confusion among physicians due to their similar location and shape in clinical CT images of the internal auditory canal. In this study, we utilized the transfer learning method combined with CT scans of the internal auditory canal to achieve accurate lesion segmentation and automatic diagnosis for patients with CSOM and middle ear cholesteatoma.Methods: We collected 1019 CT scan images and utilized the nnUnet skeleton model along with coarse grained focal segmentation labeling to pre-train on the above CT images for focal segmentation. We then fine-tuned the pre-training model for the downstream three-classification diagnosis task.Results: Our proposed algorithm model achieved a classification accuracy of 92.33% for CSOM and middle ear cholesteatoma, which is approximately 5% higher than the benchmark model. Moreover, our upstream segmentation task training resulted in a mean Intersection of Union (mIoU) of 0.569.Discussion: Our results demonstrate that using coarse-grained contour boundary labeling can significantly enhance the accuracy of downstream classification tasks. The combination of deep learning and automatic diagnosis of CSOM and internal auditory canal CT images of middle ear cholesteatoma exhibits high sensitivity and specificity.

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Section: Introductionmentioning

confidence: 99%