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SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network.

Md Kamrul Hasan¹, Md Munirul Haque², Riddhiman Adib¹

¹Marquette University, Milwaukee, WI, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 1, 2019

Summary

Smartphone videos and artificial neural networks (ANN) can estimate blood hemoglobin levels non-invasively. This method shows a 0.93 correlation, offering a promising new tool for health monitoring.

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Area of Science:

Biomedical Engineering
Medical Diagnostics
Artificial Intelligence in Healthcare

Background:

Blood hemoglobin level (Hgb) measurement is crucial for diagnosing and managing various diseases.
Current methods for Hgb measurement can be invasive or require specialized equipment.
There is a need for accessible, non-invasive Hgb estimation techniques.

Purpose of the Study:

To develop and validate a non-invasive method for estimating blood hemoglobin levels using smartphone video imaging.
To explore the utility of artificial neural networks (ANN) in analyzing video data for Hgb prediction.
To identify key visual features from fingertip videos indicative of Hgb levels.

Main Methods:

Collected 10-300 frame fingertip videos from 75 adult participants using a smartphone.
Extracted red, green, and blue pixel intensities from 100 area blocks per frame.
Developed an ANN model to predict hemoglobin levels based on extracted video features and patterns.

Main Results:

Achieved a 0.93 rank order correlation between the ANN model's Hgb estimations and gold standard measurements in the study sample (Hgb 7.6-13.5 g/dL).
Identified specific regions of interest within the video images that significantly reduced the feature space required for accurate prediction.
The model demonstrated effectiveness in estimating Hgb levels for adults aged 20-56 years.

Conclusions:

Smartphone video analysis combined with ANN offers a feasible and non-invasive approach for estimating blood hemoglobin levels.
This technology has the potential to improve accessibility to Hgb monitoring, particularly in resource-limited settings.
Further research can refine the model and explore its application in diverse populations and clinical scenarios.