Artificial intelligence to diagnose ear disease using otoscopic image analysis: a review

Canares, Therese; Wang, Weiyao; Unberath, Mathias; Clark, James H.

doi:10.1136/jim-2021-001870

Cited by 4 publications

(4 citation statements)

References 47 publications

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“…12 Thus, the growing role of AI in performing repetitive analytic tasks 13 and assisting with complex forecasts 14,15 has been confirmed. With regard to otological disturbances, AI has shown its effectiveness in making a differential diagnosis of otitis based on otoscopy imaging, 16 and in recognizing cholesteatoma from chronic otitis media on a CT scan. 17 Therefore, we can state with reasonable confidence that submitting visual data, or a clinical history, to AI enables an accurate assessment of the patient in near-real time.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

Pugliese,

Maccari,

Felisati

et al. 2023

Clinical Case Reports

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Key Clinical MessageLarge language models have made artificial intelligence readily available to the general public and potentially have a role in healthcare; however, their use in difficult differential diagnosis is still limited, as demonstrated by a case of necrotizing otitis externa.AbstractThis case report presents a peculiar case of necrotizing otitis externa (NOE) with skull base involvement which proved diagnostically challenging. The initial patient presentation and the imaging performed on the 78‐year‐old patient suggested a neoplastic rhinopharyngeal lesion and only after several unsuccessful biopsies the patient was transferred to our unit. Upon re‐evaluation of the clinical picture, a clinical hypothesis of NOE with skull base erosion was made and confirmed by identifying Pseudomonas aeruginosa in biopsy specimens of skull base bone and external auditory canal skin. Upon diagnosis confirmation, the patient was treated with culture‐oriented long‐term antibiotics with complete resolution of the disease. Given the complex clinical presentation, we chose to submit a posteriori this NOE case to two large language models (LLM) to test their ability to handle difficult differential diagnoses. LLMs are easily approachable artificial intelligence tools that enable human‐like interaction with the user relying upon large information databases for analyzing queries. The LLMs of choice were ChatGPT‐3 and ChatGPT‐4 and they were requested to analyze the case being provided with only objective clinical and imaging data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

Pugliese,

Maccari,

Felisati

et al. 2023

Clinical Case Reports

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“…Beyond risking unnecessary medical complications and the downstream unintended consequence of potential antibiotic resistance, over-diagnosis of ear disease adds an estimated $59 million in unnecessary healthcare spending in the US per annum ( 4 ). Computer-aided diagnosis on otoscopy images ( 5 ) has been suggested as a potential tool to improve the care of ear disease. Previous studies have mainly focused on applying machine learning to classify images of the eardrum.…”

Section: Introductionmentioning

confidence: 99%

Pediatric Otoscopy Video Screening With Shift Contrastive Anomaly Detection

Wang

Tamhane

Santos

et al. 2022

Front. Digit. Health

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Ear related concerns and symptoms represent the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered diseases of the middle and external presents a significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitates the presence or absence of ear pathology to be determined clinically. Research has, however, demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. With recent advances in computer vision and machine learning, there is an increasing interest in helping clinicians to accurately diagnose middle and external ear pathology with computer-aided systems. It has been shown that AI has the capacity to analyze a single clinical image captured during the examination of the ear canal and eardrum from which it can determine the likelihood of a pathognomonic pattern for a specific diagnosis being present. The capture of such an image can, however, be challenging especially to inexperienced clinicians. To help mitigate this technical challenge, we have developed and tested a method using video sequences. The videos were collected using a commercially available otoscope smartphone attachment in an urban, tertiary-care pediatric emergency department. We present a two stage method that first, identifies valid frames by detecting and extracting ear drum patches from the video sequence, and second, performs the proposed shift contrastive anomaly detection (SCAD) to flag the otoscopy video sequences as normal or abnormal. Our method achieves an AUROC of 88.0% on the patient level and also outperforms the average of a group of 25 clinicians in a comparative study, which is the largest of such published to date. We conclude that the presented method achieves a promising first step toward the automated analysis of otoscopy video.

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“…For instance, AI has demonstrated potential for broad applications in otolaryngology, ranging from the diagnosis of laryngeal cancer 3 to ear disease using otoscopic images. 4,5 While several reviews have examined AI and its applications in otolaryngology, 2,[6][7][8] rhinology, 9,10 otological images, 4,5,11 laryngeal cancer, 3,12 and head and neck cancer diagnosis, 13 some of them were based on the data from a few years ago and focused on only some diseases or specialties. With the emergence of new algorithms, it is important to update the literature and provide otolaryngologists with an overview of AI applications in otolaryngology.…”

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confidence: 99%

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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Artificial intelligence to diagnose ear disease using otoscopic image analysis: a review

Cited by 4 publications

References 47 publications

Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

Pediatric Otoscopy Video Screening With Shift Contrastive Anomaly Detection

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

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