title: Fast, Continuous Audiogram Estimation Using Machine Learning.
abstract: OBJECTIVE
  Pure-tone audiometry has been a staple of hearing assessments for decades. Many different procedures have been proposed for measuring thresholds with pure tones by systematically manipulating intensity one frequency at a time until a discrete threshold function is determined. The authors have developed a novel nonparametric approach for estimating a continuous threshold audiogram using Bayesian estimation and machine learning classification. The objective of this study was to assess the accuracy and reliability of this new method relative to a commonly used threshold measurement technique.
  
  
  METHODS
  The authors performed air conduction pure-tone audiometry on 21 participants between the ages of 18 and 90 years with varying degrees of hearing ability. Two repetitions of automated machine learning audiogram estimation and one repetition of conventional modified Hughson-Westlake ascending-descending audiogram estimation were acquired by an audiologist. The estimated hearing thresholds of these two techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz).
  
  
  RESULTS
  The two threshold estimate methods delivered very similar estimates at standard audiogram frequencies. Specifically, the mean absolute difference between estimates was 4.16 ± 3.76 dB HL. The mean absolute difference between repeated measurements of the new machine learning procedure was 4.51 ± 4.45 dB HL. These values compare favorably with those of other threshold audiogram estimation procedures. Furthermore, the machine learning method generated threshold estimates from significantly fewer samples than the modified Hughson-Westlake procedure while returning a continuous threshold estimate as a function of frequency.
  
  
  CONCLUSIONS
  The new machine learning audiogram estimation technique produces continuous threshold audiogram estimates accurately, reliably, and efficiently, making it a strong candidate for widespread application in clinical and research audiometry.
authors.affiliation: 1Laboratory of Sensory Neuroscience and Neuroengineering, Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA; 2Program in Audiology and Communication Sciences, Washington University in St. Louis, St. Louis, Missouri, USA; and 3Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, Missouri, USA.
authors.forename: Xinyu D
authors.initials: XD
authors.lastname: Song
authors.forename: Brittany M
authors.initials: BM
authors.lastname: Wallace
authors.forename: Jacob R
authors.initials: JR
authors.lastname: Gardner
authors.forename: Noah M
authors.initials: NM
authors.lastname: Ledbetter
authors.forename: Kilian Q
authors.initials: KQ
authors.lastname: Weinberger
authors.forename: Dennis L
authors.initials: DL
authors.lastname: Barbour
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001301:Audiometry, Pure-Tone / Q000379:methods*; D001499:Bayes Theorem; D005260:Female; D034381:Hearing Loss / Q000175:diagnosis*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D055815:Young Adult