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In the present research we tackled the classification of seven genetic cardiac diseases and control subjects by using an extensive set of machine learning algorithms with their variations from simple K-nearest neighbor searching method to support vector machines. The research was based on calcium transient signals measured from induced pluripotent stem cell-derived cardiomyocytes. All in all, 55 different machine learning alternatives were used to model eight classes by applying the principle of 10-fold crossvalidation with the peak data of 1626 signals. The best classification accuracy of approximately 69% was given by random forests, which can be seen high enough here to show machine learning to be potential for the differentiation of the eight disease classes., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Cardiomyocytes differentiated from human induced pluripotent stem cells (iPSC-CMs) can be used to study genetic cardiac diseases. In patients these diseases are manifested e.g. with impaired contractility and fatal cardiac arrhythmias, and both of these can be due to abnormal calcium transients in cardiomyocytes. Here we classify different genetic cardiac diseases using Ca 2+ transient data and different machine learning algorithms., Methods: By studying calcium cycling of disease-specific iPSC-CMs and by using calcium transients measured from these cells it is possible to classify diseases from each other and also from healthy controls by applying machine learning computation on the basis of peak attributes detected from calcium transient signals., Results: In the current research we extend our previous study having Ca-transient data from four different genetic diseases by adding data from two additional diseases (dilated cardiomyopathy and long QT Syndrome 2). We also study, in the light of the current data, possible differences and relations when machine learning modelling and classification accuracies were computed by using either leave-one-out test or 10-fold cross-validation., Conclusions: Despite more complex classification tasks compared to our earlier research and having more different genetic cardiac diseases in the analysis, it is still possible to attain good disease classification results. As excepted, leave-one-out test and 10-fold cross-validation achieved virtually equal results., Competing Interests: Declaration of Competing Interests As to the manuscript of ‘On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes’ submitted to Computer Methods and Programs in Biomedicine, there is no conflict of interest., (Copyright © 2021. Published by Elsevier B.V.)

Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient signals and drug effects by means of machine learning, a central part of artificial intelligence. Drug effects were assessed in six iPSC-lines carrying different mutations causing catecholaminergic polymorphic ventricular tachycardia (CPVT), a highly malignant inherited arrhythmogenic disorder. The antiarrhythmic effect of dantrolene, an inhibitor of sarcoplasmic calcium release, was studied in iPSC-CMs after adrenaline, an adrenergic agonist, stimulation by machine learning analysis of calcium transient signals. First, beats of transient signals were identified with our peak recognition algorithm previously developed. Then 12 peak variables were computed for every identified peak of a signal and by means of this data signals were classified into different classes corresponding to those affected by adrenaline or, thereafter, affected by a drug, dantrolene. The best classification accuracy was approximately 79% indicating that machine learning methods can be utilized in analysis of iPSC-CM drug effects. In the future, data analysis of iPSC-CM drug effects together with machine learning methods can create a very valuable and efficient platform to individualize medication in addition to drug screening and cardiotoxicity studies.

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of them k -Nearest Neighbor ( k -NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design and k -NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem.

Comprehensive functioning of Ca2+ cycling is crucial for excitation-contraction coupling of cardiomyocytes (CMs). Abnormal Ca2+ cycling is linked to arrhythmogenesis, which is associated with cardiac disorders and heart failure. Accordingly, we have generated spontaneously beating CMs from induced pluripotent stem cells (iPSC) derived from patients with catecholaminergic polymorphic ventricular tachycardia (CPVT), which is an inherited and severe cardiac disease. Ca2+ cycling studies have revealed substantial abnormalities in these CMs. Ca2+ transient analysis performed manually lacks accepted analysis criteria, and has both low throughput and high variability. To overcome these issues, we have developed a software tool, AnomalyExplorer based on interactive visualization, to assist in the classification of Ca2+ transient patterns detected in CMs. Here, we demonstrate the usability and capability of the software, and we also compare the analysis efficiency to manual analysis. We show that AnomalyExplorer is suitable for detecting normal and abnormal Ca2+ transients; furthermore, this method provides more defined and consistent information regarding the Ca2+ abnormality patterns and cell line specific differences when compared to manual analysis. This tool will facilitate and speed up the analysis of CM Ca2+ transients, making it both more accurate and user-independent. AnomalyExplorer can be exploited in Ca2+ cycling analysis to study basic disease pathology and the effects of different drugs.

Calcium cycling is crucial in the excitation-contraction coupling of cardiomyocytes, and therefore has a key role in cardiac functionality. Cardiac disorders and different drugs alter the calcium transients of cardiomyocytes and can cause serious dysfunction of the heart. New insights into this biochemical phenomena can be achieved by studying and analyzing calcium transients. Calcium transients of spontaneously beating human induced pluripotent stem cell-derived cardiomyocytes were recorded for a data set of 280 signals. Our objective was to develop and program procedures: (1) to automatically detect cycling peaks from signals and to classify the peaks of signals as either normal or abnormal, and (2) on the basis of the preceding peak detection results, to classify the entire signals into either a normal class or an abnormal class. We obtained a classification accuracy of approximately 80% compared to class decisions made separately by an experienced researcher, which is promising for the further development of an automatic classification approach. Automated classification software would be beneficial in the future for analyzing cardiomyocyte functionality on a large scale when screening for the adverse cardiac effects of new potential compounds, and also in future clinical applications., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Induced pluripotent stem cells (iPSC) can be derived from fully differentiated cells of adult individuals and used to obtain any other cell type of the human body. This implies numerous prospective applications of iPSCs in regenerative medicine and drug development. In order to obtain valid cell culture, a quality control process must be applied to identify and discard abnormal iPSC colonies. Computer vision systems that analyze visual characteristics of iPSC colony health can be especially useful in automating and improving the quality control process. In this paper, we present an ongoing research that aims at the development of local spatially-enhanced descriptors for classification of iPSC colony images. For this, local oriented edges and local binary patterns are extracted from the detected colony regions and used to represent structural and textural properties of the colonies, respectively. We preliminary tested the proposed descriptors in classifying iPSCs colonies according to the degree of colony abnormality. The tests showed promising results for both, detection of iPSC colony borders and colony classification.

In this article, we consider data complexity in the context of calcium transient signal data collected from induced pluripotent stem cell‐derived cardiomyocytes. We present a novel way to measure data complexity based on the nearest neighbour searching method. Data complexity here is seen as overlapping and mixed data classes in addition to a relatively great number of data cases. Complexity affects classification results, which were run with nearest neighbour searching, feedforward artificial neural networks and random forests for seven genetic cardiological disease classes and healthy controls. The data are obtained from individuals carrying mutations for genetic cardiac diseases with induced pluripotent stem cell (iPSC) technology and the diseases include hypertrophic cardiomyopathy with two different founder gene mutations, dilated cardiomyopathy, long QT syndrome type 1 and 2, Brugada syndrome, a severe genetic ventricular arrhythmia (CPVT) and healthy controls. The data are from calcium transients from spontaneously beating iPSC‐derived cardiomyocytes cultured in a biotechnology laboratory. When the genotype of the iPSC‐derived cardiomyocytes is the same as the donor of the tissue sample and based on the characteristics of the calcium transients, it was possible to classify the seven diseases and healthy controls with machine learning. Peak data first detected before actual pre‐processing from calcium transient signals corresponded to beats (repeating excitation–contraction coupling) of induced stem cell‐derived cardiomyocytes and formed the basis of classification. During pre‐processing of the calcium transient signals, we found that such techniques among others as even strong outlier cleaning or class size balancing by generating artificial cases improved only slightly or not at all classification accuracies. Therefore, the current data set was sufficiently complicated for our data complexity study. Random forests produced the best classification accuracies, 68% for all eight classes. [ABSTRACT FROM AUTHOR]