Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression
<p dir="ltr">Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting de...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| _version_ | 1864513535721078784 |
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| author | Alaa Abd-Alrazaq (17430900) |
| author2 | Rawan AlSaad (14159019) Farag Shuweihdi (12573046) Arfan Ahmed (17541309) Sarah Aziz (17541312) Javaid Sheikh (5534825) |
| author2_role | author author author author author |
| author_facet | Alaa Abd-Alrazaq (17430900) Rawan AlSaad (14159019) Farag Shuweihdi (12573046) Arfan Ahmed (17541309) Sarah Aziz (17541312) Javaid Sheikh (5534825) |
| author_role | author |
| dc.creator.none.fl_str_mv | Alaa Abd-Alrazaq (17430900) Rawan AlSaad (14159019) Farag Shuweihdi (12573046) Arfan Ahmed (17541309) Sarah Aziz (17541312) Javaid Sheikh (5534825) |
| dc.date.none.fl_str_mv | 2023-05-05T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41746-023-00828-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Systematic_review_and_meta-analysis_of_performance_of_wearable_artificial_intelligence_in_detecting_and_predicting_depression/24717150 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Artificial intelligence Mathematical sciences Statistics Systematic review meta-analysis wearable artificial intelligence detecting predicting depression |
| dc.title.none.fl_str_mv | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.</p><h2>Other Information</h2><p dir="ltr">Published in: npj Digital Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41746-023-00828-5" target="_blank">https://dx.doi.org/10.1038/s41746-023-00828-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6c9526be7a71705ce9e6afa6148d60df |
| identifier_str_mv | 10.1038/s41746-023-00828-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717150 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depressionAlaa Abd-Alrazaq (17430900)Rawan AlSaad (14159019)Farag Shuweihdi (12573046)Arfan Ahmed (17541309)Sarah Aziz (17541312)Javaid Sheikh (5534825)Health sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMathematical sciencesStatisticsSystematic reviewmeta-analysiswearable artificial intelligencedetectingpredicting depression<p dir="ltr">Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.</p><h2>Other Information</h2><p dir="ltr">Published in: npj Digital Medicine<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41746-023-00828-5" target="_blank">https://dx.doi.org/10.1038/s41746-023-00828-5</a></p>2023-05-05T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41746-023-00828-5https://figshare.com/articles/journal_contribution/Systematic_review_and_meta-analysis_of_performance_of_wearable_artificial_intelligence_in_detecting_and_predicting_depression/24717150CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247171502023-05-05T03:00:00Z |
| spellingShingle | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression Alaa Abd-Alrazaq (17430900) Health sciences Health services and systems Information and computing sciences Artificial intelligence Mathematical sciences Statistics Systematic review meta-analysis wearable artificial intelligence detecting predicting depression |
| status_str | publishedVersion |
| title | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression |
| title_full | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression |
| title_fullStr | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression |
| title_full_unstemmed | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression |
| title_short | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression |
| title_sort | Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression |
| topic | Health sciences Health services and systems Information and computing sciences Artificial intelligence Mathematical sciences Statistics Systematic review meta-analysis wearable artificial intelligence detecting predicting depression |