Model-based selection of most informative diagnostic tests and test parameters

Sven Herrmann,Mathias Dietz

doi:10.1051/aacus/2021043

Abstract

Given the complexity of most brain and body processes, it is often not possible to relate experimental data from an individual to the underlying subject-specific physiology or pathology. Computer simulations of these processes have been suggested to assist in establishing such a relation. However, the aforementioned complexity and required simulation accuracy impose considerable challenges. To date, the best-case scenario is varying the model parameters to fit previously recorded experimental data. Confidence intervals can be given in the units of the data, but usually not for the model parameters that are the ultimate interest of the diagnosis. We propose a likelihood-based fitting procedure, operating in the model-parameter space and providing confidence intervals for the parameters under diagnosis. The procedure is capable of running parallel to the measurement, and can adaptively set test parameters to the values that are expected to provide the most diagnostic information. Using the pre-defined acceptable confidence interval, the experiment continues until the goal is reached. As an example, the approach was tested with a simplistic three-parameter auditory model and a psychoacoustic binaural tone in a noise-detection experiment. For a given number of trials, the model-based measurement steering provided 80% more information.

Highlights

Audiological diagnostics has the goal of identifying the physiological cause of a hearing impairment, and of quantifying its extent (e.g., [1])
It is assumed that the behavior of a certain brain or other body function can be described as a function f that has been implemented as a computer model
The parameter set for this artificial test subject correspond to that of a normal-hearing person [11]: BW: 79 Hz, rITD: 0.33, rD: 0.38

Summary

Introduction

Audiological diagnostics has the goal of identifying the physiological cause of a hearing impairment, and of quantifying its extent (e.g., [1]). Due to the complexity of the auditory system, it is a far from trivial exercise to identify a specific cause from non-invasively recorded data. The audiologist gathered lots of quantitative data such as an audiogram, a tympanogram, an auditory brainstem response (ABR), and so forth. It is not going to be “patient X has 60–80% loss of auditory nerve fibers at frequencies above 2000 Hz and a 5–10 mV reduced endocochlear potential”. These physiological parameters would be most useful for providing optimal treatment, at least in theory, if a patient’s auditory system can be fully characterized by a large set of such parameters. To derive physiological parameters from experimental data, an accurate computational model is required

Objectives

Methods

Results