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Lookup NU author(s): Dr Sadaf Iqbal, Professor Jaume Bacardit, Dr Bridget Griffiths, Professor John AllenORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2023 Iqbal, Bacardit, Griffiths and Allen. Introduction: A pilot study assessing a novel approach to identify patients with Systemic Sclerosis (SSc) using deep learning analysis of multi-site photoplethysmography (PPG) waveforms (“DL-PPG”). Methods: PPG recordings having baseline, unilateral arm pressure cuff occlusion and reactive hyperaemia flush phases from 6 body sites were studied in 51 Controls and 20 SSc patients. RGB scalogram images were obtained from the PPG, using the continuous wavelet transform (CWT). 2 different pre-trained convolutional neural networks (CNNs, namely, GoogLeNet and EfficientNetB0) were trained to classify the SSc and Control groups, evaluating their performance using 10-fold stratified cross validation (CV). Their classification performance (i.e., accuracy, sensitivity, and specificity, with 95% confidence intervals) was also compared to traditional machine learning (ML), i.e., Linear Discriminant Analysis (LDA) and K-Nearest Neighbour (KNN). Results: On a participant basis DL-PPG accuracy, sensitivity and specificity for GoogLeNet were 83.1 (72.3–90.9), 75.0 (50.9–91.3) and 86.3 (73.7–94.3)% respectively, and for EfficientNetB0 were 87.3 (77.2–94.0), 80.0 (56.3–94.3) and 90.1 (78.6–96.7)%. The corresponding results for ML classification using LDA were 66.2 (53.9–77.0), 65.0 (40.8–84.6) and 66.7 (52.1–79.2)% respectively, and for KNN were 76.1 (64.5–85.4), 40.0 (19.1–63.9), and 90.2 (78.6–96.7)% respectively. Discussion: This study shows the potential of DL-PPG classification using CNNs to detect SSc. EfficientNetB0 gave an overall improved performance compared to GoogLeNet, with both CNNs performing better than the traditional ML methods tested. Our automatic AI approach, using transfer learning, could offer significant benefits for SSc diagnostics in a variety of clinical settings where low-cost portable and easy-to-use diagnostics can be beneficial.
Author(s): Iqbal S, Bacardit J, Griffiths B, Allen J
Publication type: Article
Publication status: Published
Journal: Frontiers in Physiology
Year: 2023
Volume: 14
Online publication date: 13/09/2023
Acceptance date: 18/08/2023
Date deposited: 17/10/2023
ISSN (electronic): 1664-042X
Publisher: Frontiers Media SA
URL: https://doi.org/10.3389/fphys.2023.1242807
DOI: 10.3389/fphys.2023.1242807
Data Access Statement: The data analyzed in this study is subject to the following licenses/restrictions: The datasets presented in this article are not readily available because the participants involved at the time of the original data collection did not consent to their PPG measurements being shared openly such as in a public repository. Requests to access these datasets should be directed to: Not applicable.
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