Machine Learning to Identify Regional and Segmental Dysfunction during Type 2 Diabetes Mellitus Progression

Diabetes mellitus results in numerous co‐morbidities, the most serious of which is cardiovascular disease (CVD). Speckle tracking strain‐based echocardiography (STE) can be used to assess regional and segmental dysfunction prior to the onset of clinically recognizable symptoms, providing a unique op...

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Bibliographic Details
Published in:	The FASEB journal Vol. 36; no. S1
Main Authors:	Rizwan, Saira, Durr, Andrya J., Korol, Anna, Hathaway, Quincy A., Kunovac Hathaway, Amina, Taylor, Andrew D., Pinti, Mark V., Hollander, John M.
Format:	Journal Article
Language:	English
Published:	United States The Federation of American Societies for Experimental Biology 01-05-2022
Online Access:	Get full text
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Summary:	Diabetes mellitus results in numerous co‐morbidities, the most serious of which is cardiovascular disease (CVD). Speckle tracking strain‐based echocardiography (STE) can be used to assess regional and segmental dysfunction prior to the onset of clinically recognizable symptoms, providing a unique opportunity to assess localized patterns of cardiac dysfunction. At present, STE has been minimally used to assess localized impacts of CVD in type 2 diabetes mellitus (T2DM). Therefore, the aim of this study was to utilize machine learning (ML) to determine the ability of STE parameters to predict T2DM and to elucidate regional and segmental impacts in a temporal fashion. Echocardiography data were collected in wild‐type and Db/Db mice at 5, 12, 20, and 25 weeks. Seventeen parameters were used for analyses: Complete, pulse‐wave Doppler, M‐mode, Global, Segmental, AntFree, PosteriorFree, Anterior, Posterior, Septal, Free, LatWall, PostWall, InfFreeWall, PostSeptal, and AntSeptum. Support vector machine analysis determined that STE parameters were the most accurate predictors of T2DM. Further, ReliefF feature selection was used to evaluate prevalence of each region and segment. We determined the Anterior region and AntSeptum segment to be the most prevalent (75% of the time), at weeks 5, 20, and 25 (82%, 93%, and 91% testing accuracies, respectively). The Septal region was shown to be most predictive (75% of the time), at weeks 5, 20, and 25 (84%, 93%, and 98%, respectively). Further, the InfFreeWall (76%) and LatWall segments (98%) were most predictive at 5 and 12 weeks, whereas the AntSeptum and AntFree segments (96% each) were most predictive at 20 and 25 weeks, respectively. Specifically, in some cases such as 25 weeks, the Septal region and the AntFree segment present identically, but the AntFree segment may provide a more focused target. In summary, these data demonstrate that patterns of regional and segmental dysfunction exist in the T2DM heart and can be observed in a spatiotemporal fashion using ML.
ISSN:	0892-6638 1530-6860
DOI:	10.1096/fasebj.2022.36.S1.R3408