Deep learning networks for quantitative evaluation of organic voice disorders and their treatment

• Applicants: Professor Dr.-Ing. Michael Döllinger; Professorin Dr. Anne Schützenberger
• Subject Area: Otolaryngology, Phoniatrics and Audiology
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 468206600

Current diagnostic and treatment routines for organic voice disorders are still dominated by subjective/objective evaluation of the acoustical signal and subjective evaluation of the vibrational characteristics of the vocal folds. This subjective evaluation is mostly due to the complex physical processes involved and the limited accessibility of the vocal folds and larynx, which hinder quantitative measurements and evaluations. However, recent scientific advances in the areas of Machine Learning and numerical modeling have demonstrated the potential to contribute to quantitative diagnosis and treatment support for many diseases, including for voice disorders. In this proposal we will exploit stat-of-the-art neuronal network classification methods in combination with numerical modelling and multi-modal based data to support clinical treatment of organic voice disorders by providing quantitative information on severity status, pre- during and after treatment. The first innovative aspect and working package is the development of an improved lumped mass model (6MM+) with a realistic Lattice-Boltzmann airflow solver to simulate vocal fold vibrations, utilizing the advantages of modern Graphics Processing Units (GPUs). (2) We will utilize a deep neural network to optimize the 6MM+ dynamics towards endoscopic high-speed recordings of the vocal fold vibrations. This will yield biomechanical laryngeal parameters (i.e. local masses, stiffness, collision forces of vocal folds and subglottal pressure) provided by the new 6MM+ model. (3) The estimated biomechanical parameters, parameters representing vocal fold and glottis dynamics extracted from endoscopic high-speed recordings, parameters computed from the acoustic signal and patient specific data will be combined to a multi-modal data set. Then, we will again utilize a deep neural network on this multi-modal data set to quantify the severity of the organic voice disorder pre-, during and past surgical treatment. (4) We will utilize feature importance analysis (Adaptive Boosting) within this multi-modal data set to identify the important parameters representing organic voice disorders. (5) The entire work-flow will be integrated in the VoIce Treatment AnaLysIs Tool (VITALITy) software and made available for other researchers.To achieve our goals, we will conduct a study on 90 patients suffering from vocal fold paresis, muscle atrophy or vocal fold polyps and a control group (60 subjects) to train and validate the VITALITy system. Clinicians will assist in the creation of VITALITy to allow the aspired clinical application in future. Thereby, the VITALITy system will help to provide the desired support on quantitative evaluation for diagnostics, therapy progress and outcome of organic voice disorders.This proposal will set new standards in the quantitative evaluation of the severity of organic voice disorders by combining state-of-the-art machine learning, numerical modeling and multi-modal data.

• DFG Programme: Research Grants