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A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
M Musalia1, S Laha2,3, J Cazalilla-Chica1
1Critical Care Unit, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK.
Critical Care (London, England)
|July 10, 2023
Summary
Speech/phrase recognition software shows promise for critically ill patients with speech impairments. Deep neural networks achieved 75% rank 1 accuracy, offering a new communication aid.
Area of Science:
- Medical technology
- Artificial intelligence in healthcare
- Critical care medicine
Background:
- Critically ill patients often experience speech impairments, necessitating alternative communication methods like visual charts or gestures.
- Existing communication aids may not fully restore natural interaction for patients with severe speech difficulties.
Purpose of the Study:
- To evaluate the effectiveness of a novel speech/phrase recognition app in critically ill patients with speech impairments.
- To assess the performance of dynamic time warping (DTW) and deep neural networks (DNN) for recognizing mouthed phrases.
Main Methods:
- A prospective study was conducted in a tertiary hospital critical care unit.
- 14 patients with tracheostomies used a speech/phrase recognition app for voice impaired (SRAVI) to mouth phrases.
- Recordings were analyzed using DNN and DTW methods, with results ranked by likelihood.
Main Results:
- The DNN method achieved an overall recognition accuracy of 86% and a rank 1 accuracy of 75%.
- The DTW method yielded a total recognition accuracy of 74% and a rank 1 accuracy of 48%.
- 516 out of 616 (83.8%) recorded phrases were identifiable.
Conclusions:
- Speech/phrase recognition software, particularly using DNN, demonstrates feasibility and effectiveness in aiding communication for critically ill patients.
- This technology has the potential to significantly improve communication for patients with speech impairments.
- Further research and development could enhance its role as a therapeutic option in critical care settings.
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