A machine learning approach in the non-invasive prediction of intracranial pressure using Modified Photoplethysmography

The ideal Intracranial pressure (ICP) estimation method should be accurate, reliable, cost-effective, compact, and associated with minimal morbidity/mortality. To this end several described non-invasive methods in ICP estimation have yielded promising results, however the reliability of these techniques have yet to supersede invasive methods of ICP measurement. Over several publications, we described a novel imaging method of Modified Photoplethysmography in the evaluation of the retinal vascular pulse parameters decomposed in the Fourier domain, which enables computationally efficient information filtering of the retinal vascular pulse wave. We applied this method in a population of 21 subjects undergoing lumbar puncture manometry. A regression model was derived by applying an Extreme Gradient Boost (XGB) machine learning algorithm using retinal vascular pulse harmonic regression waveform amplitude (HRWa), first and second harmonic cosine and sine coefficients (an1,2, bn1,2) among other features. Gain and SHapley Additive exPlanation (SHAP) values ranked feature importance in the model. Agreement between the predicted ICP mean, median and peak density with measured ICP was assessed using Bland-Altman bias±standard error. Feature gain of intraocular pressure (IOPi) (arterial = 0.6092, venous = 0.5476), and of the Fourier coefficients, an1 (arterial = 0.1000, venous = 0.1024) ranked highest in the XGB model for both vascular systems. The arterial model SHAP values demonstrated the importance of the laterality of the tested eye (1.2477), which was less prominent in the venous model (0.8710). External validation was achieved using seven hold-out test cases, where the median venous predicted ICP showed better agreement with measured ICP. Although the Bland-Altman bias from the venous model (0.034±1.8013 cm water (p