Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol

Abstract

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 STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ Unmodified smartphone cameras and machine learning approaches are used to non- invasively predict blood haemoglobin (Hgb) and haematocrit (Hct) levels from an easily accessible site—the conjunctiva. ⇒ Development and validation of the model are tai lored to predict blood Hgb and Hct levels in a quan titative manner similar to clinical laboratory testing, rather than detecting anaemia as a binary outcome. ⇒ Study population is specifically designed to address healthcare disparities impacting Black African preg nant women. ⇒ Target gestation includes all three trimesters with approximately equal representation from each trimester. ⇒ Due to the observational nature of the study, there is no intervention administered. 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