Application of artificial intelligence using a convolutional neural network for detecting cholesteatoma in endoscopic enhanced images

Miwa, Toru; Minoda, Ryosei; Yamaguchi, Tomoya; Kita, Shin-ichiro; Osaka, Keiichi; Takeda, Hiroyuki; Kanemaru, Shin‐ichi; Omori, Koichi

doi:10.1016/j.anl.2021.03.018

Cited by 15 publications

(15 citation statements)

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“…Additionally, we used data augmentation to increase the number of images for the training step. While vertical flips of an isolated mass, for example, cholesteatoma, can still result in a realistic mass, 24 we used images that included the entire tympanic membrane and other anatomical features which would not realistically be vertically flipped, though such data augmentation steps should contribute toward improving CNN performance. Furthermore, we are limited by the number of images and representation of categories available to our study.…”

Section: Discussionmentioning

confidence: 99%

“…Notably, Miwa et al used transfer learning to train a CNN assisted by digital image enhancement modalities to distinguish cholesteatoma matrix, cholesteatoma debris, and a normal middle ear mucosa. 24 Accuracies using the different digital enhancement modalities were compared. In the task of identifying cholesteatoma matrix lesions, they achieved sensitivity of 34.6% and 42.3%, and with a specificity of 81.3% and 87.5%, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…They then applied transfer learning of the best performing weights to mobile CNNs, achieving an accuracy of 97.6%. Notably, Miwa et al used transfer learning to train a CNN assisted by digital image enhancement modalities to distinguish cholesteatoma matrix, cholesteatoma debris, and a normal middle ear mucosa 24 . Accuracies using the different digital enhancement modalities were compared.…”

Section: Introductionmentioning

confidence: 99%

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Use of artificial intelligence for the diagnosis of cholesteatoma

Tseng

Lim

Jyung

2023

Laryngoscope Investig Oto

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Objectives: Accurate diagnosis of cholesteatomas is crucial. However, cholesteatomas can easily be missed in routine otoscopic exams. Convolutional neural networks (CNNs) have performed well in medical image classification, so we evaluated their use for detecting cholesteatomas in otoscopic images.Study Design: Design and evaluation of artificial intelligence driven workflow for cholesteatoma diagnosis.Methods: Otoscopic images collected from the faculty practice of the senior author were deidentified and labeled by the senior author as cholesteatoma, abnormal noncholesteatoma, or normal. An image classification workflow was developed to automatically differentiate cholesteatomas from other possible tympanic membrane appearances. Eight pretrained CNNs were trained on our otoscopic images, then tested on a withheld subset of images to evaluate their final performance. CNN intermediate activations were also extracted to visualize important image features.Results: A total of 834 otoscopic images were collected, further categorized into 197 cholesteatoma, 457 abnormal non-cholesteatoma, and 180 normal. Final trained CNNs demonstrated strong performance, achieving accuracies of 83.8%-98.5% for differentiating cholesteatoma from normal, 75.6%-90.1% for differentiating cholesteatoma from abnormal non-cholesteatoma, and 87.0%-90.4% for differentiating cholesteatoma from non-cholesteatoma (abnormal non-cholesteatoma + normal). DenseNet201 (100% sensitivity, 97.1% specificity), NASNetLarge (100% sensitivity, 88.2% specificity), and MobileNetV2 (94.1% sensitivity, 100% specificity) were among the best performing CNNs in distinguishing cholesteatoma versus normal. Visualization of intermediate activations showed robust detection of relevant image features by the CNNs. Conclusion:While further refinement and more training images are needed to improve performance, artificial intelligence-driven analysis of otoscopic images shows great promise as a diagnostic tool for detecting cholesteatomas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of artificial intelligence for the diagnosis of cholesteatoma

Tseng

Lim

Jyung

2023

Laryngoscope Investig Oto

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“…The recent literature consists of AI applied to otological imaging in a range of modalities and clinical contexts (Table 1). Groups have combined AI with computed tomography (CT) [4–14], magnetic resonance imaging (MRI) [15–17,18 ▪ ,19,20] and light otoscopy/otoendoscopy [21 ▪ ,22,23,24 ▪▪ ,25,26,27 ▪ ,28–31,32 ▪▪ ]. Most of the recent literature focuses on two themes: automated, image-based otoscopic diagnosis and automated segmentation of temporal bone CT and MRI scans for use in virtual reality, surgical simulation and surgical planning.…”

Section: Artificial Intelligence and Otological Imagingmentioning

confidence: 99%

“…Multiple papers directly compare an AI algorithm against human physicians [4,8,10,16,18 ▪ ,19,21 ▪ ,26,29,31]. From a computer science perspective, several innovative approaches have been taken.…”

Section: Artificial Intelligence and Otological Imagingmentioning

confidence: 99%

Emerging artificial intelligence applications in otological imaging

Chawdhary

2021

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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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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Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Wang

Liang

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveTo 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 SourcesWeb of Science, Embase, PubMed, and Cochrane Library.Review MethodsStudies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.ResultsOverall, 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.ConclusionThis 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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Application of artificial intelligence using a convolutional neural network for detecting cholesteatoma in endoscopic enhanced images

Cited by 15 publications

References 14 publications

Use of artificial intelligence for the diagnosis of cholesteatoma

Use of artificial intelligence for the diagnosis of cholesteatoma

Emerging artificial intelligence applications in otological imaging

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

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