Artificial intelligence to detect tympanic membrane perforations.
Objective: To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings. Methods: A retrospective review was conducted of otoscopic images analysed using transfer lea...
| Publicado en: | Journal of Laryngology & Otology Vol. 134; no. 4; pp. 311 - 316 |
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| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Cambridge University Press
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142907955&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142907955 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00222151 1GA jtl: Journal of Laryngology & Otology issn: 00222151 maglogo: N pubinfo: dt: Apr2020 vid: 134 iid: 4 pid: 15979 pub: Cambridge University Press artinfo: ui: 142907955 142907955 142907955 10.1017/S0022215120000717 142907955 ppf: 311 ppct: 5 formats: tig: atl: Artificial intelligence to detect tympanic membrane perforations. aug: au: Habib, A-R Wong, E Sacks, R Singh, N affil: Department of Otolaryngology – Head and Neck Surgery, Westmead Hospital, Sydney, Australia sug: subj: Tympanic Membrane Perforation Diagnosis Artificial Intelligence Algorithms Human Otoscopy Machine Learning Retrospective Design Confidence Intervals Tympanic Membrane Otolaryngologists ab: Objective: To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings. Methods: A retrospective review was conducted of otoscopic images analysed using transfer learning with Google's Inception-V3 convolutional neural network architecture. The 'gold standard' 'ground truth' was defined by otolaryngologists. Perforation size was categorised as less than one-third (small), one-third to two-thirds (medium), or more than two-thirds (large) of the total tympanic membrane diameter. Results: A total of 233 tympanic membrane images were used (183 for training, 50 for testing). The algorithm correctly identified intact and perforated tympanic membranes (overall accuracy = 76.0 per cent, 95 per cent confidence interval = 62.1–86.0 per cent); the area under the curve was 0.867 (95 per cent confidence interval = 0.771–0.963). Conclusion: A proof-of-concept image-classification artificial intelligence algorithm can be used to detect tympanic membrane perforations and, with further development, may prove to be a valuable tool for ear disease screening. Future endeavours are warranted to develop a point-of-care tool for healthcare workers in areas distant from otolaryngology. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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