Key result
Machine learning methods achieved a best classification accuracy of 87% in differentiating iPSC-CM Ca2+ transient signals of genetic cardiac diseases from healthy controls.
Why the study?
To investigate whether machine learning methods can separate iPSC-CM Ca2+ signals of any genetic cardiac disease as one group from those of healthy individuals.
Can machine learning methods differentiate iPSC-CM Ca2+ signals of genetic cardiac diseases from those of healthy individuals?
Can machine learning methods differentiate iPSC-CM Ca2+ signals of genetic cardiac diseases from those of healthy individuals?
Machine learning methods can efficiently identify diseased iPSC-CMs based on Ca2+ transient signals, potentially aiding in identifying individuals at risk for cardiac events.
May aid research phenotyping of iPSC-CM signals; leaves open clinical diagnostic translation.
Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have been shown to be useful to improve techniques that are developed for the study of cardiac disease. Abnormalities in Ca2+ transients are commonly present in iPSC-CMs derived from individuals with a cardiac disease. We previously observed that Ca2+ transient signals of healthy CMs can be distinguished from transients of CMs derived from individuals having different genetic cardiac diseases. Machine learning was used to distinguish different diseases from each other as well as from controls. We wanted further to investigate whether we are able to separate iPSC-CM Ca2+ signals of any genetic cardiac disease as one group from those of healthy individuals by utilizing machine learning methods. A total number of 593 CM transient signals from healthy individuals and from patients were analyzed. We obtained a best classification accuracy of 87% between the disease group and controls. This finding provides evidence that machine learning methods are efficient for identifying iPSC-CMs derived from individuals with a disease phenotype, and that iPSC-CMs may be useful to identify individuals at risk for a cardiac event.
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Juhola et al. (2019) studied Genetic cardiac diseases (n=593). Machine learning vs. Healthy controls was evaluated on Classification accuracy between the disease group and controls. Machine learning methods achieved a best classification accuracy of 87% in differentiating iPSC-CM Ca2+ transient signals of genetic cardiac diseases from healthy controls.
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