Project Rhapsody: Investigating the clinical feasibility of using AI-based deep audio and language processing techniques to diagnose neurological and psychiatric diseases
Award Number
AI_AWARD01984Programme
Artificial IntelligenceStatus / Stage
CompletedDates
2 April 2021 -1 October 2022
Duration (calculated)
01 years 05 monthsFunder(s)
NIHRFunding Amount
£149,787.00Funder/Grant study page
NIHRContracted Centre
Novoic LtdPrincipal Investigator
Mr Emil FristedPI ORCID
0000-0002-9590-7275WHO Catergories
Development of clinical assessment of cognition and functionDevelopment of novel therapies
Disease Type
Alzheimer's Disease (AD)Frontotemporal Dementia (FTD)
Lewy body dementia (LBD)
CPEC Review Info
| Reference ID | 165 |
|---|---|
| Researcher | Reside Team |
| Published | 12/06/2023 |
Data
| Award Number | AI_AWARD01984 |
|---|---|
| Status / Stage | Completed |
| Start Date | 20210402 |
| End Date | 20221001 |
| Duration (calculated) | 01 years 05 months |
| Funder/Grant study page | NIHR |
| Contracted Centre | Novoic Ltd |
| Funding Amount | £149,787.00 |
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.
Aims
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.