Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network’s biological function—oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample’s morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.
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This material is supported by an American Heart Association Predoctoral Fellowship under Grant No. 906239, a National Science Foundation Graduate Research Fellowship under Grant No. 1650114, and a Texas A&M Biomedical Engineering Department National Excellence Fellowship to J.J.T.; and by the NHLBI of NIH under Award Number R01HL157790, NSF CAREER Award Number 1944322, and Texas A&M University President’s Excellence in Research Award (T32/X-Grant) to A.J.
Associate Editor Aleksander S. Popel oversaw the review of this article.
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Tronolone, J.J., Mathur, T., Chaftari, C.P. et al. Evaluation of the Morphological and Biological Functions of Vascularized Microphysiological Systems with Supervised Machine Learning. Ann Biomed Eng 51, 1723–1737 (2023). https://doi.org/10.1007/s10439-023-03177-2