Machine Learning Identifies Abnormal Ca2+ Transients in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes



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Key Benefits

  • Potentially decreases the time and labor for evaluating transient calcium compared to current gold standard methods.
  • May accelerate the development of new drugs for cardiovascular diseases.
  • Could improve the basic understanding of cardiovascular diseases as essential research tool.

Market Summary

Heart disease is the leading cause of morbidity and mortality in the United States. Abnormal transient calcium is a high-risk factor for multiple heart conditions as it plays a central role in cardiomyocyte excitation-contraction coupling. While measuring calcium levels is essential to ensure optimal electrical impulse propagation and cardiac contraction, current diagnostic methods for identifying and quantifying abnormal calcium are limited. They are labor-intensive, time-consuming, and subjective large variability among patients. There is an unmet medical need to improve diagnosis of the disease while accelerating the development of new treatment options.

Technical Summary

Researchers have developed a novel machine-learning approach to evaluate and measure transient calcium utilizing human pluripotent stem cell training data. The current manual identification pf abnormal Ca(2+) transients is labor intensive and subjective to the assessor’s expertise. To address this the researchers adapted an extant machine learning based Ca(2+) transient peak analysis algorithm and vastly improved its capability and accuracy in Ca(2+) transient peak identification and variables quantification. The researchers found using two separate Support Vector Machine classifiers that when trained against human accessors the novel computational tool demonstrated an improved level of accuracy, sensitivity, and specificity when assessing peak abnormality and cell abnormality.

Publication: Hwang, H., Liu, R., Maxwell, J.T. et al. Machine learning identifies abnormal Ca 2+ transients in human induced pluripotent stem cell-derived cardiomyocytes . Sci Rep 10, 16977 (2020).(October 12, 2020)

Patent Information

Tech ID: 20184
Published: 5/13/2022