Screening for anemia using multi-modal machine learning models on smartphones: protocol for a comparative accuracy study in rural India

Introduction

Anemia, or low blood hemoglobin (Hb), affects one third of the world population, and is particularly prevalent in women and children in lower resource settings. However, screening for anemia is limited by the availability of accurate, easy to use, lower cost and non-invasive methods. We aim to generate research to support potential development of a point of care test to detect anemia using smartphone images of conjunctivae, tongue and nail beds as well as photoplethysmogram (PPG) signals, and comparing their accuracy to a standard laboratory assay, and, a point of care Hb assay.

Methods & Analysis

Cross-sectional comparative accuracy study of Adults and children (>1 year) presenting to hospital and outpatient care at SEWA Rural, a non-governmental organization providing healthcare to a rural, tribal population in Gujarat, India. Patients whose clinician has requested a complete blood count (CBC) will be recruited. Images of conjunctivae, tongue and nail beds, and a 30 second recording of fingertip PPG will be obtained using up to three different smartphones. A point of care Hb (HemoCue Hb 301) will be performed. Machine learning will be used to derive algorithms from images and/or PPG that predict anemia across a range of severities, and age groups. We will determine the mean absolute error and standard deviation of algorithms, compared to the laboratory value of Hb. We will also compare estimates of model accuracy using sensitivity, specificity and 95%CI compared to the reference test for different severities of anemia, and also to the point of care Hb assay. A total sample size of 2322 is estimated to provide a mean absolute error of < 1g/dL. Accuracy will be explored in subgroups including age, gender, pregnancy, presence of clinical features of anemia, and skin tone.

Ethics and Dissemination

The study was approved by SEWA Rural Institutional Ethics Committee on 31 March, 2023. The Health Ministry Screening Committee (HMSC) approved the conduct of this study. Findings from the study will be presented in the peer reviewed journal, and data will be made available for external researchers.

Trial registration

The study is registered with the Clinical Trial Registry of India (Registration number: CTRI/2023/07/055313).

Strengths and Limitations

• Participants will include populations of high clinical importance, including children, pregnant women and those with moderate or severe anemia

• Smartphone data used to train models will include images from multiple body sites as well as photoplethysmogram (PPG data), in order to facilitate identifying the type and location of sensor data that provides most accuracy for hemoglobin estimation.

• Several smartphone models, with varying sensing capability and cost will be used to collect data, to understand potential impacts of smartphone sensor quality/signal processing on Hb estimation.

• Non-consecutive recruitment may overestimate estimates of diagnostic test performance that differ from when a test is used in consecutive sampling

• Study participants may differ from those in other parts of India and other countries in characteristics such as skin tone, cause(s) of anemia, appearance of hands or eyes which may limit generalizability of findings.