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A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study

Musalia, M ; Laha, S ; Cazalilla-Chica, J ; Allan, J ; Roach, L ; Twamley, J ; Nanda, S ; Verlander, M ; Williams, A ; Kempe, I ; Patel, I I ; Campbell-West, F ; Blackwood, B ; McAuley, D F

Critical care (London, England), 2023-07, Vol.27 (1), p.277-277, Article 277 [Periódico revisado por pares]

England: BioMed Central Ltd

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Título:
A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study
Autor: Musalia, M ; Laha, S ; Cazalilla-Chica, J ; Allan, J ; Roach, L ; Twamley, J ; Nanda, S ; Verlander, M ; Williams, A ; Kempe, I ; Patel, I I ; Campbell-West, F ; Blackwood, B ; McAuley, D F
Assuntos: Accuracy ; Analysis ; Care and treatment ; Cognitive ability ; Collaboration ; Communication ; Critical care ; Critical Illness - therapy ; Critically ill ; Feasibility Studies ; Female ; Health aspects ; Humans ; Lip reading ; Male ; Methods ; Mobile Applications ; Neural networks ; Ostomy ; Patient outcomes ; Patients ; Prospective Studies ; Speech ; Speech recognition software ; SRAVI ; Teaching hospitals ; Tracheostomy ; Tracheotomy
É parte de: Critical care (London, England), 2023-07, Vol.27 (1), p.277-277, Article 277
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: Evaluating effectiveness of speech/phrase recognition software in critically ill patients with speech impairments. Prospective study. Tertiary hospital critical care unit in the northwest of England. 14 patients with tracheostomies, 3 female and 11 male. Evaluation of dynamic time warping (DTW) and deep neural networks (DNN) methods in a speech/phrase recognition application. Using speech/phrase recognition app for voice impaired (SRAVI), patients attempted mouthing various supported phrases with recordings evaluated by both DNN and DTW processing methods. Then, a trio of potential recognition phrases was displayed on the screen, ranked from first to third in order of likelihood. A total of 616 patient recordings were taken with 516 phrase identifiable recordings. The overall results revealed a total recognition accuracy across all three ranks of 86% using the DNN method. The rank 1 recognition accuracy of the DNN method was 75%. The DTW method had a total recognition accuracy of 74%, with a rank 1 accuracy of 48%. This feasibility evaluation of a novel speech/phrase recognition app using SRAVI demonstrated a good correlation between spoken phrases and app recognition. This suggests that speech/phrase recognition technology could be a therapeutic option to bridge the gap in communication in critically ill patients. Communication can be attempted using visual charts, eye gaze boards, alphabet boards, speech/phrase reading, gestures and speaking valves in critically ill patients with speech impairments. Deep neural networks and dynamic time warping methods can be used to analyse lip movements and identify intended phrases. Our study shows that speech/phrase recognition software has a role to play in bridging the communication gap in speech impairment.
Editor: England: BioMed Central Ltd
Idioma: Inglês

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A user evaluation of speech/phrase recognition software in critically ill patients: a DECIDE-AI feasibility study

Musalia, M ; Laha, S ; Cazalilla-Chica, J ; Allan, J ; Roach, L ; Twamley, J ; Nanda, S ; Verlander, M ; Williams, A ; Kempe, I ; Patel, I I ; Campbell-West, F ; Blackwood, B ; McAuley, D F

England: BioMed Central Ltd

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