Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol
Introduction Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected or is detected too late, worsening pregnancy compli...
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BMJ Publishing Group
2025-05-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/5/e097342.full |
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| author | Haripriya Sakthivel Sang Mok Park Semin Kwon Eunice Kaguiri Elizabeth Nyaranga Jung Woo Leem Shaun G Hong Peter J Lane Edwin O Were Martin C Were Young L Kim |
| author_facet | Haripriya Sakthivel Sang Mok Park Semin Kwon Eunice Kaguiri Elizabeth Nyaranga Jung Woo Leem Shaun G Hong Peter J Lane Edwin O Were Martin C Were Young L Kim |
| author_sort | Haripriya Sakthivel |
| collection | DOAJ |
| description | Introduction Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected or is detected too late, worsening pregnancy complications. The gold standard for diagnosing anaemia is a clinical laboratory blood haemoglobin (Hgb) or haematocrit (Hct) test involving a venous blood draw. However, this approach presents several challenges in resource-limited settings regarding accessibility and feasibility. Although non-invasive blood Hgb testing technologies are gaining attention, they remain limited in availability, affordability and practicality. This study aims to develop and validate a mobile health (mHealth) machine learning model to reliably predict blood Hgb and Hct levels in Black African pregnant women using smartphone photos of the conjunctiva.Methods and analysis This is a single-centre, cross-sectional and observational study, leveraging existing antenatal care services for pregnant women aged 15 to 49 years in Kenya. The study involves collecting smartphone photos of the conjunctiva alongside conventional blood Hgb tests. Relevant clinical data related to each participant’s anaemia status will also be collected. The photo acquisition protocol will incorporate diverse scenarios to reflect real-world variability. A clinical training dataset will be used to refine a machine learning model designed to predict blood Hgb and Hct levels from smartphone images of the conjunctiva. Using a separate testing dataset, comprehensive analyses will assess its performance by comparing predicted blood Hgb and Hct levels with clinical laboratory and/or finger-prick readings.Ethics and dissemination This study is approved by the Moi University Institutional Research and Ethics Committee (Reference: IREC/585/2023 and Approval Number: 004514), Kenya’s National Commission for Science, Technology, and Innovation (NACOSTI Reference: 491921) and Purdue University’s Institutional Review Board (Protocol Number: IRB-2023-1235). Participants will include emancipated or mature minors. In Kenya, pregnant women aged 15 to 18 years are recognised as emancipated or mature minors, allowing them to provide informed consent independently. The study poses minimal risk to participants. Findings and results will be disseminated through submissions to peer-reviewed journals and presentations at the participating institutions, including Moi Teaching and Referral Hospital and Kenya’s Ministry of Health. On completion of data collection and modelling, this study will demonstrate how machine learning-driven mHealth technologies can reduce reliance on clinical laboratories and complex equipment, offering accessible and scalable solutions for resource-limited and at-home settings. |
| format | Article |
| id | doaj-art-c4e9f8f6a96340cd868285150040aa1a |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMJ Publishing Group |
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| spelling | doaj-art-c4e9f8f6a96340cd868285150040aa1a2025-08-20T02:28:36ZengBMJ Publishing GroupBMJ Open2044-60552025-05-0115510.1136/bmjopen-2024-097342Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocolHaripriya Sakthivel0Sang Mok Park1Semin Kwon2Eunice Kaguiri3Elizabeth Nyaranga4Jung Woo Leem5Shaun G Hong6Peter J Lane7Edwin O Were8Martin C Were9Young L Kim10The Charles Draper Stark Laboratory, Cambridge, Massachusetts, USAWeldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USAWeldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USAAcademic Model Providing Access to Healthcare, Eldoret, KenyaAcademic Model Providing Access to Healthcare, Eldoret, KenyaWeldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USAWeldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USAVanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, Tennessee, USAAcademic Model Providing Access to Healthcare, Eldoret, KenyaVanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, Tennessee, USAWeldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USAIntroduction Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected or is detected too late, worsening pregnancy complications. The gold standard for diagnosing anaemia is a clinical laboratory blood haemoglobin (Hgb) or haematocrit (Hct) test involving a venous blood draw. However, this approach presents several challenges in resource-limited settings regarding accessibility and feasibility. Although non-invasive blood Hgb testing technologies are gaining attention, they remain limited in availability, affordability and practicality. This study aims to develop and validate a mobile health (mHealth) machine learning model to reliably predict blood Hgb and Hct levels in Black African pregnant women using smartphone photos of the conjunctiva.Methods and analysis This is a single-centre, cross-sectional and observational study, leveraging existing antenatal care services for pregnant women aged 15 to 49 years in Kenya. The study involves collecting smartphone photos of the conjunctiva alongside conventional blood Hgb tests. Relevant clinical data related to each participant’s anaemia status will also be collected. The photo acquisition protocol will incorporate diverse scenarios to reflect real-world variability. A clinical training dataset will be used to refine a machine learning model designed to predict blood Hgb and Hct levels from smartphone images of the conjunctiva. Using a separate testing dataset, comprehensive analyses will assess its performance by comparing predicted blood Hgb and Hct levels with clinical laboratory and/or finger-prick readings.Ethics and dissemination This study is approved by the Moi University Institutional Research and Ethics Committee (Reference: IREC/585/2023 and Approval Number: 004514), Kenya’s National Commission for Science, Technology, and Innovation (NACOSTI Reference: 491921) and Purdue University’s Institutional Review Board (Protocol Number: IRB-2023-1235). Participants will include emancipated or mature minors. In Kenya, pregnant women aged 15 to 18 years are recognised as emancipated or mature minors, allowing them to provide informed consent independently. The study poses minimal risk to participants. Findings and results will be disseminated through submissions to peer-reviewed journals and presentations at the participating institutions, including Moi Teaching and Referral Hospital and Kenya’s Ministry of Health. On completion of data collection and modelling, this study will demonstrate how machine learning-driven mHealth technologies can reduce reliance on clinical laboratories and complex equipment, offering accessible and scalable solutions for resource-limited and at-home settings.https://bmjopen.bmj.com/content/15/5/e097342.full |
| spellingShingle | Haripriya Sakthivel Sang Mok Park Semin Kwon Eunice Kaguiri Elizabeth Nyaranga Jung Woo Leem Shaun G Hong Peter J Lane Edwin O Were Martin C Were Young L Kim Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol BMJ Open |
| title | Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol |
| title_full | Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol |
| title_fullStr | Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol |
| title_full_unstemmed | Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol |
| title_short | Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol |
| title_sort | machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in kenyan pregnant women a clinical study protocol |
| url | https://bmjopen.bmj.com/content/15/5/e097342.full |
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