An Observational Study to Compare Prevalence and Demography of Sensorineural Hearing Loss Among Military Personnel and Civilian Population

Raghavan, Anil; Patnaik, Uma; Bhaudaria, Ankush Singh

doi:10.1007/s12070-020-02180-6

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Despite this, the classification accuracy of the majority of the currently proposed solutions ranges between 90 and 95%, which, while very high, still leaves substantial room for error. According to clinical standards, the margin of error should be kept under 5% 24 and ideally should be close to 3% 25 . Only one of the discussed classifiers satisfies these requirements.…”

Section: Methodsmentioning

confidence: 99%

Automated hearing loss type classification based on pure tone audiometry data

Kassjański,

Kulawiak,

Przewoźny

et al. 2024

Sci Rep

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Hearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient’s hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of an audiogram, need to be interpreted by a professional audiologist in order to determine the exact type of hearing loss and administer proper treatment. However, the small number of professionals in the field can severely delay proper diagnosis. The presented work proposes a neural network solution for classification of tonal audiometry data. The solution, based on the Bidirectional Long Short-Term Memory architecture, has been devised and evaluated for classifying audiometry results into four classes, representing normal hearing, conductive hearing loss, mixed hearing loss, and sensorineural hearing loss. The network was trained using 15,046 test results analysed and categorised by professional audiologists. The proposed model achieves 99.33% classification accuracy on datasets outside of training. In clinical application, the model allows general practitioners to independently classify tonal audiometry results for patient referral. In addition, the proposed solution provides audiologists and otolaryngologists with access to an AI decision support system that has the potential to reduce their burden, improve diagnostic accuracy, and minimise human error.

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Section: Methodsmentioning

confidence: 99%

Automated hearing loss type classification based on pure tone audiometry data

Kassjański,

Kulawiak,

Przewoźny

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This being said, however, the currently proposed solutions deliver classification accuracy in the 90-95% range, which, although very high, still leaves considerable room for error. Clinical standards suggest that the margin of error should be kept under 5% [16] and optimally should be close to 3% [17]. These requirements are met only by two of the discussed classifiers.…”

Section: Introductionmentioning

confidence: 99%

Development of an AI-based audiogram classification method for patient referral

Kassjański¹,

Kulawiak²,

Przewoźny³

2022

Annals of Computer Science and Information Systems

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Hearing loss is one of the most significant sensory disabilities. It can have various negative effects on a person's quality of life, ranging from impeded school and academic performance to total social isolation in severe cases. It is therefore vital that early symptoms of hearing loss are diagnosed quickly and accurately. Audiology tests are commonly performed with the use of tonal audiometry, which measures a patient's hearing threshold both in air and bone conduction at different frequencies. The graphic result of this test is represented on an audiogram, which is a diagram depicting the values of the patient's measured hearing thresholds. In the course of the presented work several different artificial neural network models, including MLP, CNN and RNN, have been developed and tested for classification of audiograms into two classes -normal and pathological represented hearing loss. The networks have been trained on a set of 2400 audiograms analysed and classified by professional audiologists. The best classification performance was achieved by the RNN architecture (represented by simple RNN, GRU and LSTM), with the highest out-of-training accuracy being 98% for LSTM. In clinical application, the developed classifier can significantly reduce the workload of audiology specialists by enabling the transfer of tasks related to analysis of hearing test results towards general practitioners. The proposed solution should also noticeably reduce the patient's average wait time between taking the hearing test and receiving a diagnosis. Further work will concentrate on automating the process of audiogram interpretation for the purpose of diagnosing different types of hearing loss.

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Resource Availability and Socio-economic Profile of Scheduled Caste (SC) Community in Agrarian Society: Approach Towards Sustainability

Alam

Satpati

2021

Agriculture, Food and Nutrition Security

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An Observational Study to Compare Prevalence and Demography of Sensorineural Hearing Loss Among Military Personnel and Civilian Population

Cited by 4 publications

References 12 publications

Automated hearing loss type classification based on pure tone audiometry data

Automated hearing loss type classification based on pure tone audiometry data

Development of an AI-based audiogram classification method for patient referral

Resource Availability and Socio-economic Profile of Scheduled Caste (SC) Community in Agrarian Society: Approach Towards Sustainability

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