Introduction: An urgent need exists for a noninvasive telemedicine approach to alleviate the burdensome reliance on frequent invasive blood draws for monitoring blood hemoglobin (Hgb) levels in sickle cell disease (SCD) patients. SCD is the most common genetic blood disorder, impacting over 20 million people worldwide, resulting in frequent emergency healthcare use. A blood Hgb level is pivotal in managing SCD and understanding the pathophysiology of SCD including anemia. Even low blood Hgb levels may indicate painful vaso-occlusion crises (Blood 2021;137:2010). Thus, a remote noninvasive monitoring ability of blood Hgb in at-home settings can potentially enhance SCD management without iatrogenic blood loss. Advanced machine learning and spectroscopic analyses can enable digital colorimetric diagnostics for noninvasive blood Hgb quantification with high fidelity, empowering SCD patients. However, one of the key challenges in diagnostic photography is that clinical photos exhibit significant variations in colors, depending on devices, light conditions, and image file formats, hampering reliable colorimetric diagnostics.
Methods: We have developed colorimetric analyses of peripheral tissue perfusion to offer remote monitoring and cost-effective blood Hgb readings for SCD patients. A specially designed diagnostic color reference chart, which is highly sensitive to blood Hgb and peripheral perfusion, allowed us to noninvasively predict blood Hgb levels from digital photos of the palpebral conjunctiva (inner eyelid) across diverse smartphone models, light conditions, and file formats. Specifically, a pilot human study of fifteen SCD patients aged 14 to 73 years generated 156 photos from both left and right eyelids juxtaposed with the diagnostic color reference chart, using Samsung Galaxy S21 and Apple iPhone 12 Pro, taken immediately before or after blood draws. Combining a blood Hgb computation (Optica 2020;7:563), blood Hgb levels were computed involving three primary machine learning algorithms (Figure 1): color recovery of the palpebral conjunctiva, hyperspectral learning to reconstruct a spectrum, and spectroscopic blood Hgb quantification.
Results: Using our specially designed color chart (Figure 2a), the linear correlation between the computed blood Hgb levels and venous blood Hgb levels shows a high correlation coefficient of 0.88 and 0.81 for Samsung Galaxy S21 and Apple iPhone 12 Pro, respectively. The Bland-Altman plot as a non-parametric statistical method reveal that the 95% limits of agreement (95% LOA) are [-2.68, 2.10 g dL -1] and [-2.74, 3.19 g dL -1] for the two smartphone models. Conversely, when using a conventional color chart for general photography (Macbeth ColorChecker) (Figure 2b), the computed blood Hgb levels exhibit a low accuracy for both Samsung Galaxy S21 (correlation coefficient of 0.69, 95% LOA = [-5.05, 2.60 g dL -1]) and Apple iPhone 12 Pro (correlation coefficient of 0.57, 95% LOA = [-5.14, 5.87 g dL -1]).
Conclusion: This study supports the feasibility of noninvasive blood Hgb assessments as colorimetric diagnostics for SCD patients in telemedical settings. The results correlate significantly with conventional laboratory tests based on venous blood draws and are highly comparable to capillary blood sampling (finger prick) tests (Respiratory Care 2019;64:1343). This study lays the foundation for realizing color accuracy in biological tissue for digital diagnostic colorimetric applications of blood Hgb.The proposed color accuracy-based digital health technology can potentially offer accessible, affordable, and reliable blood Hgb readings via smartphones in at-home or telemedicine settings.
Disclosures
No relevant conflicts of interest to declare.
