  Hearing loss is a major health problem and psychological burden in humans.
Mouse models offer a possibility to elucidate genes involved in the underlying
developmental and pathophysiological mechanisms of hearing impairment. To this
end, large-scale mouse phenotyping programs include auditory phenotyping of
single-gene knockout mouse lines. Using the auditory brainstem response (ABR)
procedure, the German Mouse Clinic and similar facilities worldwide have
produced large, uniform data sets of averaged ABR raw data of mutant and
wildtype mice. In the course of standard ABR analysis, hearing thresholds are
assessed visually by trained staff from series of signal curves of increasing
sound pressure level. This is time-consuming and prone to be biased by the
reader as well as the graphical display quality and scale. In an attempt to
reduce workload and improve quality and reproducibility, we developed and
compared two methods for automated hearing threshold identification from
averaged ABR raw data: a supervised approach involving two combined neural
networks trained on human-generated labels and a self-supervised approach,
which exploits the signal power spectrum and combines random forest sound level
estimation with a piece-wise curve fitting algorithm for threshold finding. We
show that both models work well, outperform human threshold detection, and are
suitable for fast, reliable, and unbiased hearing threshold detection and
quality control. In a high-throughput mouse phenotyping environment, both
methods perform well as part of an automated end-to-end screening pipeline to
detect candidate genes for hearing involvement. Code for both models as well as
data used for this work are freely available.
