Deep Learning for Echocardiography

Objectives: The aim of this study was to evaluate whether a deep convolutional neural network (DCNN) could detect regional wall motion abnormalities (RWMAs) and differentiate groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with cardiologist/sonographer or resident readers. Background: An effective intervention for reduction of misreading of RWMAs is needed. We hypothesized that a DCNN trained with echocardiographic images may provide improved detection of RWMAs in the clinical setting. Methods: A total of 300 patients with history of myocardial infarction were enrolled. In this cohort, 100 each had infarctions of the left anterior descending branch (LAD), left circumflex branch (LCX), and right coronary artery (RCA). The age-matched 100 control patients with normal wall motion were selected from our database. Each case contained cardiac ultrasound images from short axis views at end-diastolic, mid-systolic and end-systolic phases. After 100 steps of training, diagnostic accuracies were calculated on the test set. We independently trained 10 versions of the same model, and performed ensemble predictions with them. Results: For detection of the presence of wall motion abnormality, the area under the receiver-operating characteristic curve (AUC) by deep learning algorithm was similar to that by cardiologist/sonographer readers (0.99 vs. 0.98, p =0.15), and significantly higher than the AUC by resident readers (0.99 vs. 0.90, p =0.002). For detection of territories of wall motion abnormality, the AUC by the deep learning algorithm was similar to the AUC by cardiologist/sonographer readers (0.97 vs. 0.95, p =0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, p =0.003). In a validation group from an independent site (n=40), the AUC by the DL algorithm was 0.90. Conclusions: Our results support the possibility of DCNN use for automated diagnosis of RWMAs in the field of echocardiography

Drastic change of local stiffness distribution correlating to cell migration in living fibroblasts.

Sequential images of the local stiffness distribution of living fibroblasts (NIH3T3) were captured under a culture condition using scanning probe microscopy in a force modulation mode. We found a clear relation between cell migration and local stiffness distribution on the cell: When cells were stationary at one position, the stiffness distribution of their cellular surface was quite stable. On the other hand, once the cells started to move, the stiffness in their nuclear regions drastically decreased. Possible explanations for the correlation between the cell migration and the cell stiffness are proposed. Cell Motil. Cytoskeleton 50:173-179, 2001. © 2001 Wiley-Liss, Inc

Contribution of cellular contractility to spatial and temporal variations in cellular stiffness

Scanning probe microscopy and immunofluorescence observations indicated that cellular stiffness was attributed to a contractile network structure consisting of stress fibers. We measured temporal variations in cellular stiffness when cellular contractility was regulated by dosing with lysophosphatidic acid or Y-27632. This experiment reveals a clear relation between cellular stiffness and contractility: Increases in contractility cause cells to stiffen. On the other hand, decreases in contractility reduce cellular stiffness. In both cases, not only the stiffness of the stress fibers but also that of the whole of the cell varies. Immunofluorescence observations of myosin II and vinculin indicated that the stiffness variations induced by the regulation of cellular contractility were mainly due to rearrangements of the contractile actin network on the dorsal surface. Taken together, our findings provide evidence that the actin cytoskeletal network and its contractility features provide and modulate the mechanical stability of adherent cells

Improvement of Force Modulation Mode with Scanning Probe Microscopy for Imaging Viscoelasticity of Living Cells

We improved the force modulation mode with scanning probe microscopy (SPM) in order to make a quantitative evaluation of the viscoelasticity of living cells. Taking account of the viscosity of liquid medium, the vibration frequency of the cantilever was selected to be 500 Hz, and analysis of cantilever vibration was adopted for evaluation of the viscoelasticity of the samples. Consequently, we have succeeded in determining viscoelasticity distribution on living cells. The values of Young's modulus and the coefficient of viscosity vary from 10 to 50 kPa and from 20 to 40 Pa·s on a cell, depending on its internal cellular structure