A User Evaluation of Speech/phrase Recognition Software in Critically Ill Patients: a DECIDE-AI Feasibility Study

Overview

Journal Crit Care

Specialty Critical Care

Date 2023 Jul 10

PMID 37430313

Authors

M Musalia

S Laha

J Cazalilla-Chica

J Allan

L Roach

J Twamley

S Nanda

M Verlander

A Williams

I Kempe

I I Patel

F Campbell-West

B Blackwood

D F McAuley

Affiliations

Soon will be listed here.

Abstract

Objectives: Evaluating effectiveness of speech/phrase recognition software in critically ill patients with speech impairments.

Design: Prospective study.

Setting: Tertiary hospital critical care unit in the northwest of England.

Participants: 14 patients with tracheostomies, 3 female and 11 male.

Main Outcome Measures: 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.

Results: 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%.

Conclusion: 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.

What Is Already Known About This Topic: 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.

What This Study Adds: Deep neural networks and dynamic time warping methods can be used to analyse lip movements and identify intended phrases.

How This Study Might Affect Research, Practice And Policy: Our study shows that speech/phrase recognition software has a role to play in bridging the communication gap in speech impairment.

References

Vasey B, Nagendran M, Campbell B, Clifton D, Collins G, Denaxas S . Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022; 28(5):924-933. DOI: 10.1038/s41591-022-01772-9. View

Ten Hoorn S, Elbers P, Girbes A, Tuinman P . Communicating with conscious and mechanically ventilated critically ill patients: a systematic review. Crit Care. 2016; 20(1):333. PMC: 5070186. DOI: 10.1186/s13054-016-1483-2. View

Al-Yahyai Rn Bsn A, Arulappan Rn Rm Bsc N Msc N PhD N DNSc J, Matua G, Al-Ghafri Rn Bsn S, Al-Sarakhi Rn Bsn S, Al-Rahbi Rn Bsn K . Communicating to Non-Speaking Critically Ill Patients: Augmentative and Alternative Communication Technique as an Essential Strategy. SAGE Open Nurs. 2021; 7:23779608211015234. PMC: 8186114. DOI: 10.1177/23779608211015234. View

Abril M, Berkowitz D, Chen Y, Waller L, Martin G, Kempker J . The Epidemiology of Adult Tracheostomy in the United States 2002-2017: A Serial Cross-Sectional Study. Crit Care Explor. 2021; 3(9):e0523. PMC: 8437212. DOI: 10.1097/CCE.0000000000000523. View

Wilkinson K, Freeth H, Kelly K . 'On the Right Trach?' A review of the care received by patients who undergo tracheostomy. Br J Hosp Med (Lond). 2015; 76(3):163-5. DOI: 10.12968/hmed.2015.76.3.163. View