An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children.
Objective: To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children.Study Design: Retrospective observational study.Setting: Tertiary referral center.Patients: Rural and remote Abor...
| Publicado en: | Otology & Neurotology Vol. 43; no. 4; pp. 481 - 489 |
|---|---|
| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Apr2022
|
| 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=155774148&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155774148 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15317129 I6D jtl: Otology & Neurotology issn: 15317129 maglogo: N pubinfo: dt: Apr2022 vid: 43 iid: 4 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 155774148 155774148 NLM35239622 155774148 10.1097/MAO.0000000000003484 NLM35239622 155774148 ppf: 481 ppct: 8 formats: tig: atl: An Artificial Intelligence Computer-vision Algorithm to Triage Otoscopic Images From Australian Aboriginal and Torres Strait Islander Children. aug: au: Habib, Al-Rahim Crossland, Graeme Patel, Hemi Wong, Eugene Kong, Kelvin Gunasekera, Hasantha Richards, Brent Caffery, Liam Perry, Chris Sacks, Raymond Kumar, Ashnil Singh, Narinder sug: ab: Objective: To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children.Study Design: Retrospective observational study.Setting: Tertiary referral center.Patients: Rural and remote Aboriginal and Torres Strait Islander children who underwent tele-otology ear health screening in the Northern Territory, Australia between 2010 and 2018.Interventions: Otoscopic images were labeled by otolaryngologists to classify the ground truth. Deep and transfer learning methods were used to develop an image classification algorithm.Main Outcome Measures: Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC) of the resultant algorithm compared with the ground truth.Results: Six thousand five hundred twenty seven images were used (5927 images for training and 600 for testing). The algorithm achieved an accuracy of 99.3% for acute otitis media, 96.3% for chronic otitis media, 77.8% for otitis media with effusion (OME), and 98.2% to classify wax/obstructed canal. To differentiate between multiple diagnoses, the algorithm achieved 74.4 to 92.8% accuracy and an AUC of 0.963 to 0.997. The most common incorrect classification pattern was OME misclassified as normal tympanic membranes.Conclusions: The paucity of access to tertiary otolaryngology care for rural and remote Aboriginal and Torres Strait Islander communities may contribute to an under-identification of ear disease. Computer vision image classification algorithms can accurately classify ear disease from otoscopic images of Indigenous Australian children. In the future, a validated algorithm may integrate with existing telemedicine initiatives to support effective triage and facilitate early treatment and referral. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|