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Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders
Dissertation (MSc (Genetics))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613482542694400 |
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| access_status_str | Open Access |
| author2 | Coetzee, Vinet |
| author_browse | Coetzee, Vinet |
| author_facet | Coetzee, Vinet |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Genetics))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/87932 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:51.203Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/87932 Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders Coetzee, Vinet lize.schoeman@me.com Honey, Engela Malherbe, Helen Wilke, Daniel Nicolas Schoeman, Lize Facial analysis Congenital disorders Facial images Patient perspectives Facial diagnostics UCTD Dissertation (MSc (Genetics))--University of Pretoria, 2022. Congenital disorders are a major health care burden. Most congenital disorders that are due to genetic causes do not have a cure, but an early and accurate diagnosis may alleviate associated symptoms and contribute to the correct management of the disorder. However, there is a lack of medical geneticists and doctors who can make these diagnoses in developing countries. Thus, facial analysis technologies can provide a quick and objective way to initially diagnose individuals with a congenital disorder where resources are limited because almost half of all inherited disorders have a typical facial gestalt. Chapter 1 is a literature review, focusing on facial analysis technologies and how it is used to make an initial diagnosis based on the typical facial features of an individual, with a special focus on Face2Gene. I briefly reviewed the four disorders under investigation in this study, their prevalence, cause, and particularly the typical facial features associated with each disorder. We first aimed to better understand parents’ views on the collection, storage, use, and publication of their children’s facial images for research and diagnosis. Large datasets of facial photographs are required to train facial analysis algorithms, and we wanted to better understand the public’s views on this topic. This was achieved by conducting an online survey, found in Chapter 2, aimed at parents of children with and without a congenital disorder. The second aim of this study was to determine and compare the diagnostic accuracies of two- dimensional facial analyses of congenital disorders. Face2Gene is a popular phenotyping web tool and is free to use for healthcare professionals. The technology does not, however, classify an individual as “non-syndromic” and will suggest likely syndromes to all submitted facial images. Differentiation between syndromic and non-syndromic individuals is important for clinicians to determine if the child requires further testing or investigation into a potential diagnosis. Chapter 3 aimed to establish how well Face2Gene can differentiate between syndromic and non-syndromic facial images, and we compared that to our in-house analyses of the facial features of individuals. Previous research showed that Face2Gene did not perform well in African ethnic groups before training. This is likely due to the algorithm’s training data mostly consisting of European individuals. It is also important to establish a diagnosis as early as possible, to ensure the correct management strategies are put in place. In Chapter 4, we thus aimed to establish how well the Face2Gene algorithm can differentiate between syndromic and non-syndromic facial images in different syndrome, ethnic, and age groups. We again compared that to the results from our in-house analyses. University of Pretoria Biochemistry, Genetics and Microbiology (BGM) MSc (Genetics) Unrestricted 2022-10-25T08:50:13Z 2022-10-25T08:50:13Z 2023-04 2022 Dissertation * A2023 https://repository.up.ac.za/handle/2263/87932 https://doi.org/10.25403/UPresearchdata.21378360.v1 https://doi.org/10.25403/UPresearchdata.21378360 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Facial analysis Congenital disorders Facial images Patient perspectives Facial diagnostics UCTD Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders |
| title | Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders |
| title_full | Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders |
| title_fullStr | Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders |
| title_full_unstemmed | Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders |
| title_short | Parents' perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders |
| title_sort | parents perspectives and performance evaluation of facial analysis technologies for the diagnosis of congenital disorders |
| topic | Facial analysis Congenital disorders Facial images Patient perspectives Facial diagnostics UCTD |
| url | https://repository.up.ac.za/handle/2263/87932 https://doi.org/10.25403/UPresearchdata.21378360.v1 https://doi.org/10.25403/UPresearchdata.21378360 |