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Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review

BACKGROUND: Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient’s self-report, what the clinician observes directly, and what the clinician gathers from col...

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Autores principales: Jiang, Zifan, Luskus, Mark, Seyedi, Salman, Griner, Emily L., Rad, Ali Bahrami, Clifford, Gari D., Boazak, Mina, Cotes, Robert O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992987/
https://www.ncbi.nlm.nih.gov/pubmed/35395049
http://dx.doi.org/10.1371/journal.pone.0266828
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author Jiang, Zifan
Luskus, Mark
Seyedi, Salman
Griner, Emily L.
Rad, Ali Bahrami
Clifford, Gari D.
Boazak, Mina
Cotes, Robert O.
author_facet Jiang, Zifan
Luskus, Mark
Seyedi, Salman
Griner, Emily L.
Rad, Ali Bahrami
Clifford, Gari D.
Boazak, Mina
Cotes, Robert O.
author_sort Jiang, Zifan
collection PubMed
description BACKGROUND: Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient’s self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia. METHOD: We conducted a systematic review using PubMed and Google Scholar. Relevant publications published before (including) December 2021 were identified and evaluated for inclusion. The objective was to conduct a systematic review of computer vision for facial behavior analysis in schizophrenia studies, the clinical findings, and the corresponding data processing and machine learning methods. RESULTS: Seventeen studies published between 2007 to 2021 were included, with an increasing trend in the number of publications over time. Only 14 articles used interviews to collect data, of which different combinations of passive to evoked, unstructured to structured interviews were used. Various types of hardware were adopted and different types of visual data were collected. Commercial, open-access, and in-house developed models were used to recognize facial behaviors, where frame-level and subject-level features were extracted. Statistical tests and evaluation metrics varied across studies. The number of subjects ranged from 2-120, with an average of 38. Overall, facial behaviors appear to have a role in estimating diagnosis of schizophrenia and psychotic symptoms. When studies were evaluated with a quality assessment checklist, most had a low reporting quality. CONCLUSION: Despite the rapid development of computer vision techniques, there are relatively few studies that have applied this technology to schizophrenia research. There was considerable variation in the clinical paradigm and analytic techniques used. Further research is needed to identify and develop standardized practices, which will help to promote further advances in the field.
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spelling pubmed-89929872022-04-09 Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review Jiang, Zifan Luskus, Mark Seyedi, Salman Griner, Emily L. Rad, Ali Bahrami Clifford, Gari D. Boazak, Mina Cotes, Robert O. PLoS One Research Article BACKGROUND: Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient’s self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia. METHOD: We conducted a systematic review using PubMed and Google Scholar. Relevant publications published before (including) December 2021 were identified and evaluated for inclusion. The objective was to conduct a systematic review of computer vision for facial behavior analysis in schizophrenia studies, the clinical findings, and the corresponding data processing and machine learning methods. RESULTS: Seventeen studies published between 2007 to 2021 were included, with an increasing trend in the number of publications over time. Only 14 articles used interviews to collect data, of which different combinations of passive to evoked, unstructured to structured interviews were used. Various types of hardware were adopted and different types of visual data were collected. Commercial, open-access, and in-house developed models were used to recognize facial behaviors, where frame-level and subject-level features were extracted. Statistical tests and evaluation metrics varied across studies. The number of subjects ranged from 2-120, with an average of 38. Overall, facial behaviors appear to have a role in estimating diagnosis of schizophrenia and psychotic symptoms. When studies were evaluated with a quality assessment checklist, most had a low reporting quality. CONCLUSION: Despite the rapid development of computer vision techniques, there are relatively few studies that have applied this technology to schizophrenia research. There was considerable variation in the clinical paradigm and analytic techniques used. Further research is needed to identify and develop standardized practices, which will help to promote further advances in the field. Public Library of Science 2022-04-08 /pmc/articles/PMC8992987/ /pubmed/35395049 http://dx.doi.org/10.1371/journal.pone.0266828 Text en © 2022 Jiang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jiang, Zifan
Luskus, Mark
Seyedi, Salman
Griner, Emily L.
Rad, Ali Bahrami
Clifford, Gari D.
Boazak, Mina
Cotes, Robert O.
Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review
title Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review
title_full Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review
title_fullStr Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review
title_full_unstemmed Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review
title_short Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review
title_sort utilizing computer vision for facial behavior analysis in schizophrenia studies: a systematic review
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992987/
https://www.ncbi.nlm.nih.gov/pubmed/35395049
http://dx.doi.org/10.1371/journal.pone.0266828
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