Audiometric Pattern in Moderate and Severe Tympanic Membrane Retraction

Canali, Inesângela; Rosito, Letícia Petersen Schmidt; Longo, Vittoria Dreher; Costa, Sady Selaimen da

doi:10.1097/mao.0000000000003099

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…Previous studies have correlated these features with CHL. 25,27 Tympanic membrane changes and fluid could limit the mobility of the tympanic membrane and cause erosion of middle ear structures. 6,25,27,30,33 However, otologists and the logistic regression model based on tympanic membrane changes show a worse performance in predicting CHL.…”

Section: Discussionmentioning

confidence: 99%

“…25,27 Tympanic membrane changes and fluid could limit the mobility of the tympanic membrane and cause erosion of middle ear structures. 6,25,27,30,33 However, otologists and the logistic regression model based on tympanic membrane changes show a worse performance in predicting CHL. The possible reason is that these tympanic membrane changes were classified for OME diagnostic assessment, rather than CHL assessment.…”

Section: Discussionmentioning

confidence: 99%

“…We chose averaged ABG across the 4 frequencies greater than 10 dB as the criteria of conductive hearing loss . The severity of OME and changes to the tympanic membrane is associated with different degrees of conductive hearing loss at different individual frequencies . Consequently, the DL model was used to predict CHL using different criteria: ABG greater than 10 dB at each frequency, averaged air conduction hearing threshold more than 500, 1000, 2000, 4000 Hz greater than 25 dB, and averaged ABG of the 4 frequencies greater than 20 dB.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, the DL model was used to predict CHL using different criteria: ABG greater than 10 dB at each frequency, averaged air conduction hearing threshold more than 500, 1000, 2000, 4000 Hz greater than 25 dB, and averaged ABG of the 4 frequencies greater than 20 dB. Changes in the tympanic membrane used to develop the logistic regression model included attic retraction pocket, atelectasis, type of fluid (full, air-fluid level, bubble), color of tympanic membrane, color of fluid, myringosclerosis, and opacification . An otologist (J.B.Z.)…”

Section: Methodsmentioning

confidence: 99%

“…Changes in the tympanic membrane used to develop the logistic regression model included attic retraction pocket, atelectasis, type of fluid (full, air-fluid level, bubble), color of tympanic membrane, color of fluid, myringosclerosis, and opacification. 16,25,27 An otologist (J.B.Z.) was assigned to collect otoscopic images, other clinical data, and record changes in the tympanic membrane.…”

Section: Key Pointsmentioning

confidence: 99%

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A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images

Zeng

Kang

Chen

et al. 2022

JAMA Otolaryngol Head Neck Surg

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IMPORTANCEOtitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients. OBJECTIVE To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features. DESIGN, SETTING, AND PARTICIPANTSA retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features. MAIN OUTCOMES AND MEASURESThe performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, which is the harmonic mean of precision and recall; a higher F1 score means better performance). In addition, these evaluation parameters were compared to results obtained from the logistic regression model and predictions made by three otologists. RESULTSThe performance of the DL model in predicting CHL showed the AUC of 0.74, accuracy of 81%, and F1 score of 0.89. This was better than the results from the logistic regression model (ie, AUC of 0.60, accuracy of 76%, and F1 score of 0.82), and much improved on the performance of the 3 otologists; accuracy of 16%, 30%, 39%, and F1 scores of 0.09, 0.18, and 0.25, respectively. Furthermore, the DL model took 2.5 seconds to predict from 205 otoscopic images, whereas the 3 otologists spent 633 seconds, 645 seconds, and 692 seconds, respectively. CONCLUSIONS AND RELEVANCEThe model in this diagnostic/prognostic study provided greater accuracy in prediction of CHL in ears with OME than those obtained from the logistic regression model and otologists. This indicates great potential for the use of artificial intelligence tools to facilitate CHL evaluation when CHL is unable to be measured.

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