{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:37:09Z","timestamp":1787017029696,"version":"build-2736575974"},"reference-count":78,"publisher":"BMJ","issue":"5","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"unspecified","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006353","name":"Charles Stark Draper Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006353","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB033788"],"award-info":[{"award-number":["R01EB033788"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21TW012486"],"award-info":[{"award-number":["R21TW012486"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R33TW012486"],"award-info":[{"award-number":["R33TW012486"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["Technology Accelerator Challenge Prize"],"award-info":[{"award-number":["Technology Accelerator Challenge Prize"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100021066","name":"Gates Ventures","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100021066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2025,4,23]]},"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>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\u2019s 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>This study is approved by the Moi University Institutional Research and Ethics Committee (Reference: IREC\/585\/2023 and Approval Number: 004514), Kenya\u2019s National Commission for Science, Technology, and Innovation (NACOSTI Reference: 491921) and Purdue University\u2019s 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\u2019s 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2024-097342","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T01:35:37Z","timestamp":1746754537000},"page":"e097342","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol"],"prefix":"10.1136","volume":"15","author":[{"given":"Haripriya","family":"Sakthivel","sequence":"first","affiliation":[{"name":"Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, 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Model Providing Access to Healthcare, Eldoret, Kenya"},{"name":"Division of Obstetrics and Gynecology, Moi University College of Health Sciences, Eldoret, Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung Woo","family":"Leem","sequence":"additional","affiliation":[{"name":"Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaun G","family":"Hong","sequence":"additional","affiliation":[{"name":"Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J","family":"Lane","sequence":"additional","affiliation":[{"name":"Vanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edwin O","family":"Were","sequence":"additional","affiliation":[{"name":"Academic Model Providing Access to Healthcare, Eldoret, Kenya"},{"name":"Division of Obstetrics and Gynecology, Moi University College of Health Sciences, Eldoret, Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin C","family":"Were","sequence":"additional","affiliation":[{"name":"Vanderbilt Institute for Global Health, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3796-9643","authenticated-orcid":false,"given":"Young L","family":"Kim","sequence":"additional","affiliation":[{"name":"Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, 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