Use of artificial intelligence for the diagnosis of cholesteatoma. Issue 1 (17th January 2023)

• Record Type: Journal Article
• Authors: Tseng, Christopher C.; Lim, Valerie; Jyung, Robert W.
• Is Part Of: Laryngoscope investigative otolaryngology. Volume 8:Issue 1(2023)
• Page Start: 201
• Page End: 211
• Publication Date: 2023-01-17
• Subjects: artificial intelligence -- cholesteatoma -- diagnosis -- neural network -- otoscopy; Otolaryngology -- Periodicals; Laryngoscopy -- Periodicals; Otolaryngology; Otolaryngology; Periodicals; Periodicals; 617.51
• DOI: 10.1002/lio2.1008
• Languages: English
• ISSNs: 2378-8038

Abstract

Abstract: 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 non‐cholesteatoma, 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. Level of Evidence: 3. Abstract : Convolutional neural networks (CNNs) have performed well in medical image classification, so we evaluated their use for detecting cholesteatomas in otoscopic images. 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). 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.