Title: Project Rhapsody: Investigating the clinical feasibility of using AI-based deep audio and language processing techniques to diagnose neurological and psychiatric diseases
Funder: Department of Health and Social Care

Abstract: Neurological and psychiatric disorders (NPDs) present a threatening economic burden for the NHS.3-7 Similar clinical presentations and prevalent comorbidities of common NPDs31-46, make diagnoses in primary care difficult. State-of-the-art diagnostics in primary care are inaccurate14-20 and cannot disambiguate common NPDs12-13. High misdiagnosis rates lead to mistreated patients, exacerbating the burden47-52. Changes in people s speech have been found in many NPDs, years before they re formally diagnosed21-28,53-55. Our two most recent papers outline speech changes in 14 different neurological and psychiatric conditions29-30. Clinical applications have been limited by lack of harmonisation29-30 across indications, speech tasks, and recording environments. Our proposed innovation addresses these previous barriers using domain-adaptive representation learning, to build the first clinical application of speech biomarkers across multiple NPDs - a universal test. We re building these next-generation speech biomarkers into an API and mobile application that take a few minutes of speech as input and output a disease risk score for clinical decision support. This project firstly evaluates usability and feasibility of administering a universal speech battery - both in clinic and remotely - across common representative NPDs, having similar presentations or being highly comorbid. The following cohorts are enrolled into this observational, case-control study: Alzheimer s Disease, Dementia with Lewy Bodies, Parkinson s Disease, Motor Neuron Disease, Frontotemporal Dementia, Depression, and Bipolar Disorder. Secondly this project evaluates both (1) using our speech analysis libraries29-30 to analyse markers already reported in individual NPDs29-30, consistently across multiple NPDs, and (2) identifying new universal markers using un/semi-supervised learning approaches. The full duration of the project is 12 months. Results will be published. At successful completion, follow-up projects will be initiated to further validate the discovered biomarkers in each of the diseases, and implement them into NHS workflows. NHS adoption strategy is created as part of the project.

Lay abstract: More than one in five people will suffer from a debilitating neurological or psychiatric disorder (NPD) in their lifetime. By the time these people are diagnosed, it is often too late for treatments to be effective and make a difference. This leads to a massive increase in costs, which for the NHS amount to over &pound;40 billion to manage NPDs today. As the UK s population ages, this disease burden will become unsustainable for the NHS and social care services. Treatment of early-stage NPDs can improve prognosis and reduce disease burden. However, the very early (prodromal) signs of most NPDs are almost invisible and there is no simple method to detect such early symptoms of these diseases. GPs &ldquo;state-of-the-art&rdquo;for diagnosing NPDs is based on subjective questionnaires that rely on self-reported mental states. Misdiagnosis rates are high, leading to improper treatment. Many people will only be diagnosed when their disease is so severe that treatments are already ineffective. Therefore, we urgently need an affordable, accessible and accurate way of diagnosing NPDs. Groundbreaking studies have found changes in people s speech and language many years before they re officially diagnosed with NPDs. The problem is that no one knows how to translate these hidden and complicated speech changes into clinical diagnoses. Novoic s AI solution aims to do just this. We have created a new approach for extracting hidden speech and language patterns using new AI-based techniques. Our own proof-of-concept (PoC) studies, involving >300 patients and controls, convincingly demonstrated that complicated speech patterns can be highly predictive and are able to differentiate between diagnoses of multiple NPDs, even where symptoms were mild. In this project, we are studying the feasibility of using Novoic s AI-based tool, NeuroAssist, in clinical settings. We have three aims: To demonstrate that NeuroAssist can be used effectively in different settings: for patients with different NPDs (i.e. Alzheimer s Disease, Lewy Body Dementia, Parkinson s Disease, Motor Neurone Disease, Frontotemporal Dementia, Major Depressive Disorder, Bipolar Disorder); when recording different forms of speech; in different recording conditions. Confirm that NeuroAssist s AI-based speech pattern extraction works consistently across NPDs in clinical settings. Compare NeuroAssist s performance against the current SoA. The development work has four tranches: Pre-clinical design. Clinical study. Prototyping: Refine prototypes using clinical data collected. Post-clinical evaluation and dissemination: Compare NeuroAssist s performance against SoA, and prepare results for publication in leading journals. Involving patients and the public has been crucial in designing this project. Throughout the research, we will invite patients to provide usability feedback on their experience using NeuroAssist; and later to contribute a public perspective on the interpretation of the study s results.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: NEUROLOGICAL;MENTAL HEALTH