Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model.
In 2021, the National Guideline Alliance for the Royal College of Obstetricians and Gynaecologists reviewed the body of evidence, including two meta-analyses, implicating supine sleeping position as a risk factor for growth restriction and stillbirth. While they concluded that pregnant people should...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-10-01
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| Series: | PLOS Digital Health |
| Online Access: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000353&type=printable |
| _version_ | 1797663891601227776 |
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| author | Allan J Kember Rahavi Selvarajan Emma Park Henry Huang Hafsa Zia Farhan Rahman Sina Akbarian Babak Taati Sebastian R Hobson Elham Dolatabadi |
| author_facet | Allan J Kember Rahavi Selvarajan Emma Park Henry Huang Hafsa Zia Farhan Rahman Sina Akbarian Babak Taati Sebastian R Hobson Elham Dolatabadi |
| author_sort | Allan J Kember |
| collection | DOAJ |
| description | In 2021, the National Guideline Alliance for the Royal College of Obstetricians and Gynaecologists reviewed the body of evidence, including two meta-analyses, implicating supine sleeping position as a risk factor for growth restriction and stillbirth. While they concluded that pregnant people should be advised to avoid going to sleep on their back after 28 weeks' gestation, their main critique of the evidence was that, to date, all studies were retrospective and sleeping position was not objectively measured. As such, the Alliance noted that it would not be possible to prospectively study the associations between sleeping position and adverse pregnancy outcomes. Our aim was to demonstrate the feasibility of building a vision-based model for automated and accurate detection and quantification of sleeping position throughout the third trimester-a model with the eventual goal to be developed further and used by researchers as a tool to enable them to either confirm or disprove the aforementioned associations. We completed a Canada-wide, cross-sectional study in 24 participants in the third trimester. Infrared videos of eleven simulated sleeping positions unique to pregnancy and a sitting position both with and without bed sheets covering the body were prospectively collected. We extracted 152,618 images from 48 videos, semi-randomly down-sampled and annotated 5,970 of them, and fed them into a deep learning algorithm, which trained and validated six models via six-fold cross-validation. The performance of the models was evaluated using an unseen testing set. The models detected the twelve positions, with and without bed sheets covering the body, achieving an average precision of 0.72 and 0.83, respectively, and an average recall ("sensitivity") of 0.67 and 0.76, respectively. For the supine class with and without bed sheets covering the body, the models achieved an average precision of 0.61 and 0.75, respectively, and an average recall of 0.74 and 0.81, respectively. |
| first_indexed | 2024-03-11T19:21:21Z |
| format | Article |
| id | doaj.art-a3a2fdfdf4f7478ba814ca076d7d60a2 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-11T19:21:21Z |
| publishDate | 2023-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-a3a2fdfdf4f7478ba814ca076d7d60a22023-10-07T05:33:15ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-10-01210e000035310.1371/journal.pdig.0000353Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model.Allan J KemberRahavi SelvarajanEmma ParkHenry HuangHafsa ZiaFarhan RahmanSina AkbarianBabak TaatiSebastian R HobsonElham DolatabadiIn 2021, the National Guideline Alliance for the Royal College of Obstetricians and Gynaecologists reviewed the body of evidence, including two meta-analyses, implicating supine sleeping position as a risk factor for growth restriction and stillbirth. While they concluded that pregnant people should be advised to avoid going to sleep on their back after 28 weeks' gestation, their main critique of the evidence was that, to date, all studies were retrospective and sleeping position was not objectively measured. As such, the Alliance noted that it would not be possible to prospectively study the associations between sleeping position and adverse pregnancy outcomes. Our aim was to demonstrate the feasibility of building a vision-based model for automated and accurate detection and quantification of sleeping position throughout the third trimester-a model with the eventual goal to be developed further and used by researchers as a tool to enable them to either confirm or disprove the aforementioned associations. We completed a Canada-wide, cross-sectional study in 24 participants in the third trimester. Infrared videos of eleven simulated sleeping positions unique to pregnancy and a sitting position both with and without bed sheets covering the body were prospectively collected. We extracted 152,618 images from 48 videos, semi-randomly down-sampled and annotated 5,970 of them, and fed them into a deep learning algorithm, which trained and validated six models via six-fold cross-validation. The performance of the models was evaluated using an unseen testing set. The models detected the twelve positions, with and without bed sheets covering the body, achieving an average precision of 0.72 and 0.83, respectively, and an average recall ("sensitivity") of 0.67 and 0.76, respectively. For the supine class with and without bed sheets covering the body, the models achieved an average precision of 0.61 and 0.75, respectively, and an average recall of 0.74 and 0.81, respectively.https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000353&type=printable |
| spellingShingle | Allan J Kember Rahavi Selvarajan Emma Park Henry Huang Hafsa Zia Farhan Rahman Sina Akbarian Babak Taati Sebastian R Hobson Elham Dolatabadi Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model. PLOS Digital Health |
| title | Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model. |
| title_full | Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model. |
| title_fullStr | Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model. |
| title_full_unstemmed | Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model. |
| title_short | Vision-based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting-Building the dataset and model. |
| title_sort | vision based detection and quantification of maternal sleeping position in the third trimester of pregnancy in the home setting building the dataset and model |
| url | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000353&type=printable |
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