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Paderno A, Bedi N, Rau A, Holsinger CF. Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence. Otolaryngol Clin North Am 2024; 57:703-718. [PMID: 38981809 DOI: 10.1016/j.otc.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of artificial intelligence (AI) in nonradiologic imaging within otolaryngology, noting the benefits and challenges, such as improving diagnostic accuracy and optimizing therapeutic outcomes, while also pointing out the necessity for enhanced data curation and standardized research methodologies to advance clinical applications. Technical aspects are also covered, providing a detailed view of the progression from manual feature extraction to more complex AI models, including convolutional neural networks and vision transformers and their potential application in clinical settings.
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Affiliation(s)
- Alberto Paderno
- IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy.
| | - Nikita Bedi
- Division of Head and Neck Surgery, Department of Otolaryngology, Stanford University, Palo Alto, CA, USA
| | - Anita Rau
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
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Futami S, Miwa T. Comprehensive Equilibrium Function Tests for an Accurate Diagnosis in Vertigo: A Retrospective Analysis. J Clin Med 2024; 13:2450. [PMID: 38730980 PMCID: PMC11084401 DOI: 10.3390/jcm13092450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024] Open
Abstract
Background/Objectives: An accurate diagnosis of vertigo is crucial in patient care. Traditional balance function tests often fail to offer independent, conclusive diagnoses. This study aimed to bridge the gap between traditional diagnostic approaches and the evolving landscape of automated diagnostic tools, laying the groundwork for advancements in vertigo care. Methods: A cohort of 1400 individuals with dizziness underwent a battery of equilibrium function tests, and diagnoses were established based on the criteria by the Japanese Society for Vertigo and Equilibrium. A multivariate analysis identified the key diagnostic factors for various vestibudata nlar disorders, including Meniere's disease, vestibular neuritis, and benign paroxysmal positional vertigo. Results: This study underscored the complexity of diagnosing certain disorders such as benign paroxysmal positional vertigo, where clinical symptoms play a crucial role. Additionally, it highlighted the utility of specific physical balance function tests for differentiating central diseases. These findings bolster the reliability of established diagnostic tools, such as audiometry for Meniere's disease and spontaneous nystagmus for vestibular neuritis. Conclusions: This study concluded that a multifaceted approach integrating multiple diagnostic indicators is crucial for accurate clinical decisions in vestibular disorders. Future studies should incorporate novel tests, quantitative assessments, and advanced technologies to enhance the diagnostic capabilities of vestibular medicine.
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Affiliation(s)
- Shumpei Futami
- Department of Otolaryngology, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan;
| | - Toru Miwa
- Department of Otolaryngology, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan;
- Department of Otolaryngology-Head and Neck Surgery, Graduate of School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
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Li Z, Zhou L, Bin X, Tan S, Tan Z, Tang A. Utility of deep learning for the diagnosis of cochlear malformation on temporal bone CT. Jpn J Radiol 2024; 42:261-267. [PMID: 37812304 DOI: 10.1007/s11604-023-01494-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023]
Abstract
OBJECTIVE Diagnosis of cochlear malformation on temporal bone CT images is often difficult. Our aim was to assess the utility of deep learning analysis in diagnosing cochlear malformation on temporal bone CT images. METHODS A total of 654 images from 165 temporal bone CTs were divided into the training set (n = 534) and the testing set (n = 120). A target region that includes the area of the cochlear was extracted to create a diagnostic model. 4 models were used: ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The testing data set was subsequently analyzed using these models and by 4 doctors. RESULTS The areas under the curve was 0.91, 0.94, 0.93, and 0.73 in ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The accuracy of ResNet10, ResNet50, and SE-ResNet50 is better than chief physician. CONCLUSIONS Deep learning technique implied a promising prospect for clinical application of artificial intelligence in the diagnosis of cochlear malformation based on CT images.
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Affiliation(s)
- Zhenhua Li
- Department of Otorhinolaryngology-Head and Neck Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Langtao Zhou
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, People's Republic of China
| | - Xiang Bin
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Songhua Tan
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Zhiqiang Tan
- Department of Otorhinolaryngology-Head and Neck Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Anzhou Tang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
- Clinical Teaching Building, Guangxi Medical University, Nanning, 530000, People's Republic of China.
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Ding X, Huang Y, Zhao Y, Tian X, Feng G, Gao Z. Accurate Segmentation and Tracking of Chorda Tympani in Endoscopic Middle Ear Surgery with Artificial Intelligence. EAR, NOSE & THROAT JOURNAL 2023:1455613231212051. [PMID: 38083840 DOI: 10.1177/01455613231212051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2023] Open
Abstract
Objective: We introduce a novel endoscopic middle ear surgery dataset specifically designed for evaluating deep learning (DL)-based semantic segmentation of chorda tympani. Methods: We curated a dataset comprising 8240 images from 25 patients, divided into a training set (20%, 1648 images), validation set (5%, 412 images), and test set (75%, 6180 images). We employed data enhancement techniques to expand the picture size of the training and validation sets by 5 times (training set: 8240 images, verification set: 2060 images). Subsequently, we employed a multistage transfer learning training method to establish, train, and validate various convolutional neural networks. Results: On the validation set of 2060 labeled images, our proposed network achieved good results, with the U-net exhibiting the highest effectiveness (mIOU = 0.8737, mPA = 0.9263). Furthermore, when applied to the test dataset of 6180 raw images and contrasted with the prediction of otologists, the overall performance of the U-net was excellent (accuracy = 0.911, precision = 0.9823, sensitivity = 0.8777, specificity = 0.9714). Conclusions: Our findings demonstrate that DL can be successfully employed for automatic segmentation of chorda tympani in endoscopic middle ear surgery, yielding high-performance results. This study validates the potential feasibility of future intelligent navigation technologies to assist in endoscopic middle ear surgery.
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Affiliation(s)
- Xin Ding
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Yu Huang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Yang Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Xu Tian
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Guodong Feng
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Zhiqiang Gao
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
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Wu Q, Wang X, Liang G, Luo X, Zhou M, Deng H, Zhang Y, Huang X, Yang Q. Advances in Image-Based Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery: A Systematic Review. Otolaryngol Head Neck Surg 2023; 169:1132-1142. [PMID: 37288505 DOI: 10.1002/ohn.391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/27/2023] [Accepted: 05/13/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To 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 SOURCES Web of Science, Embase, PubMed, and Cochrane Library. REVIEW METHODS Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies. RESULTS Overall, 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. CONCLUSION This 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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Affiliation(s)
- Qingwu Wu
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xinyue Wang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guixian Liang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Luo
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Min Zhou
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Huiyi Deng
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yana Zhang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xuekun Huang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qintai Yang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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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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Ma T, Wu Q, Jiang L, Zeng X, Wang Y, Yuan Y, Wang B, Zhang T. Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace. EAR, NOSE & THROAT JOURNAL 2023:1455613231185074. [PMID: 37515527 DOI: 10.1177/01455613231185074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2023] Open
Abstract
BACKGROUND Otorhinolaryngology diseases are well suited for artificial intelligence (AI)-based interpretation. The use of AI, particularly AI based on deep learning (DL), in the treatment of human diseases is becoming more and more popular. However, there are few bibliometric analyses that have systematically studied this field. OBJECTIVE The objective of this study was to visualize the research hot spots and trends of AI and DL in ENT diseases through bibliometric analysis to help researchers understand the future development of basic and clinical research. METHODS In all, 232 articles and reviews were retrieved from The Web of Science Core Collection. Using CiteSpace and VOSviewer software, countries, institutions, authors, references, and keywords in the field were visualized and examined. RESULTS The majority of these papers came from 44 nations and 498 institutions, with China and the United States leading the way. Common diseases used by AI in ENT include otosclerosis, otitis media, nasal polyps, sinusitis, and so on. In the early years, research focused on the analysis of hearing and articulation disorders, and in recent years mainly on the diagnosis, localization, and grading of diseases. CONCLUSIONS The analysis shows the periodical hot spots and development direction of AI and DL application in ENT diseases from the time dimension. The diagnosis and prognosis of otolaryngology diseases and the analysis of otolaryngology endoscopic images have been the focus of current research and the development trend of future.
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Affiliation(s)
- Tianyu Ma
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qilong Wu
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Li Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoyun Zeng
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuyao Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yi Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Bingxuan Wang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Tianhong Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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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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Tseng CC, Lim V, Jyung RW. Use of artificial intelligence for the diagnosis of cholesteatoma. Laryngoscope Investig Otolaryngol 2023; 8:201-211. [PMID: 36846416 PMCID: PMC9948563 DOI: 10.1002/lio2.1008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 12/07/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
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.
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Affiliation(s)
- Christopher C. Tseng
- Department of Otolaryngology – Head and Neck SurgeryRutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Valerie Lim
- Department of Otolaryngology – Head and Neck SurgeryRutgers New Jersey Medical SchoolNewarkNew JerseyUSA
| | - Robert W. Jyung
- Department of Otolaryngology – Head and Neck SurgeryRutgers New Jersey Medical SchoolNewarkNew JerseyUSA
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Chawdhary G, Shoman N. Emerging artificial intelligence applications in otological imaging. Curr Opin Otolaryngol Head Neck Surg 2021; 29:357-364. [PMID: 34459798 DOI: 10.1097/moo.0000000000000754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
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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Affiliation(s)
- Gaurav Chawdhary
- ENT Department, Royal Hallamshire Hospital, Broomhall, Sheffield, UK
| | - Nael Shoman
- ENT Department, Queen Elizabeth II Health Sciences Centre, Halifax, Nova Scotia, Canada
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