Using voice as a biomarker for diagnosis

Research performed on this database could allow us to better detect voice disorders, such as laryngeal cancers or vocal fold paralysis; respiratory disorders, such as pneumonia or chronic lung diseases; neurological and neurodegenerative disorders, such as Alzheimer’s, Parkinson’s, stroke, or ALS; mood and psychiatric disorders, such as depression, schizophrenia, or bipolar disorders; and pediatric voice and speech disorders, such as autism or speech and language delays. These diseases have all been studied individually and we know that there is already scientific evidence to show that changes in voice can be related to them. But we need additional research, assisted by AI, to know more.

At first, data collection will be done by clinicians at expert medical centres through an app-based software. However, during the third and fourth years of the project we plan to also collect data in remote and underserved communities and through crowdsourcing. We will use a novel technology called “federated learning technology” that allows machine learning models to be trained on these voice samples, without them ever leaving their original location. This will show that research based on AI can be deployed across multiple research centres, while preserving the privacy and security of sensitive voice data.

The primary objective of this project is to create the database of voice samples in such a way that it will be ready for analysis by AI. Eventually, based on this research, we foresee a future where AI will be able to notice differences in voice that are particular to certain diseases. For example, in Parkinson's disease, voice becomes monotonous with lower amplitude. Trained voice specialists would usually suspect Parkinson's right away when they hear these variations. But not all patients have access to such specialists. The goal is to make AI algorithms as good as trained voice specialists, so that this diagnostic tool is available also in areas of lower resources. This would improve outcomes for patients and promote equity in healthcare.

Although preliminary work with voice data has been promising, previous research had serious limitations and therefore it has been challenging to integrate voice as a biomarker into clinical practice. For example, previous studies only used small datasets and for clinical use we need robust evidence based on very large numbers. Additionally, past research had ethical concerns such as who owns the voice data, how to protect patient privacy, and lack of diversity. For example, if all the samples come from one ethnic or age group, or one gender, the diagnostic tools that are developed may be less effective for others. To solve these issues, the Voice as a Biomarker of Health project is creating a large, high-quality, multi-institutional, and diverse voice database. Our data will be unidentifiable or identity-protected, which means that the voice sample will not be linked to other data about the patient, such as demographics, medical imaging and genomics.

I’m glad you ask, since the Canadian team based in UdeM and Simon Fraser University – led by ethicist Jean-Christophe Belisle-Pipon, an adjunct professor at UdeM and assistant professor at SFU – will focus on the ethical, legal, and social issues this raises. Our unique contribution to this vast effort will be to develop the governance necessary to ensure all this is done responsibly. Some of the issues are novel because voice is not yet a part of clinical care. There are currently no adequate best practices, regulations, and safeguards for its use. Our team will be the one developing them. For example, it is almost paradoxical that because voice is so easy to collect, privacy issues are actually exacerbated by this technology. We need to ensure patient data is protected and that it is not used for anything except medical purposes. We need to build this database with samples collected from diverse populations, to ensure equity in the future clinical use of this biomarker. We need to develop consent mechanisms to make sure patients understand for what purposes they are being recorded and what can be done with their voice in the future. We also need to clarify issues related to voice data ownership: can your voice be shared or sold? Who can benefit from it? Can data owned by health care systems, clinicians, and patients be shared with commercial entities for the development of AI models? These are just some examples of the complex ethical issues we will be addressing.

Very! If the infrastructure is well developed, this could represent the start of an international collaborative mission, such as the Human Genome Project, where voice data would be used by thousands of researchers, and then - based on that research - by clinicians worldwide. It could allow new and important discoveries and enhance what precision medicine has to offer patients. Voice is unique to individuals and can be collected easily in low-resource settings, in cost-effective and non-invasive ways. We hope to create the infrastructure to collect voice data in an accessible and ethical way, so that people feel safe in sharing their voice and associated medical data. Imagine a world in which you can record yourself through a specialized app on your mobile device and send it to your doctor as an additional powerful diagnostic tool! Think about how much easier this is compared with giving a blood sample or going for an imaging test such as a CT! This is what we hope would be the eventual outcome of this big research effort.