Efficient color correction method for smartphone camera-based health monitoring application.

Smartphone health monitoring applications are recently highlighted due to the rapid development of hardware and software performance of smartphones. However, color characteristics of images captured by different smartphone models are dissimilar each other and this difference may give non-identical health monitoring results when the smartphone health monitoring applications monitor physiological information using their embedded smartphone cameras. In this paper, we investigate the differences in color properties of the captured images from different smartphone models and apply a color correction method to adjust dissimilar color values obtained from different smartphone cameras. Experimental results show that the color corrected images using the correction method provide much smaller color intensity errors compared to the images without correction. These results can be applied to enhance the consistency of smartphone camera-based health monitoring applications by reducing color intensity errors among the images obtained from different smartphones.

Computational modeling of atrial fibrillation

With the aging society, the prevalence of atrial fibrillation (AF) continues to increase. Nevertheless, there are still limitations in antiarrhythmic drugs (AAD) or catheter interventions for AF. If it is possible to predict the outcome of AF management according to various AADs or ablation lesion sets through computational modeling, it will be of great clinical help. AF computational modeling has been utilized for in-silico arrhythmia research and enabled high-density entire chamber mapping, reproducible condition control, virtual intervention, not possible clinically or experimentally, in-depth mechanistic research. With the recent development of computer science and technology, more sophisti‑ cated and faster computational modeling has become available for clinical application. In particular, it can be applied to determine the extra-PV target of persistent AF catheter ablation or to select the AAD with the best effect. AF com‑ putational modeling combined with artificial intelligence is expected to contribute to precision medicine for more diverse uses in the future. Therefore, in this review, we will deal with the history, development, and various applica‑ tions of computation modeling.

Accuracy and clinical feasibility of 3D‑myocardial thickness map measured by cardiac computed tomogram

Background@#Although myocardial thickness is an important variable for therapeutic catheter ablation of cardiac arrhythmias, quantification of wall thickness has been overlooked. We developed a software (AMBER) that measures 3D-myocardial thickness using a cardiac computed tomogram (CT) image, verified its accuracy, and tested its clinical feasibility. @*Methods@#We generated 3D-thickness maps by calculating wall thickness (WT) from the CT images of 120 patients’ hearts and a 3D-phantom model (PhM). The initial vector field of the Laplace equation was oriented to calculate WT with the field lines derived from the 3D mesh. We demonstrate the robustness of the Laplace WT algorithm by comparing with the real thickness of 3D-PhM, echocardiographically measured left ventricular (LV) WT, and regional left atrial (LA) WT reported from previous studies. We conducted a pilot case of catheter ablation for atrial fibrillation (AF) utilizing real-time LAWT map-guided radiofrequency (RF) energy titration. @*Results@#AMBER 3D-WT had excellent correlations with the real thickness of the PhM (R = 0.968, p < 0.001) and echocardiographically measured LVWT in 10 patients (R = 0.656, p = 0.007). AMBER 3D-LAWT (n = 120) showed a relatively good match with 12 previously reported regional LAWT. We successfully conducted pilot AF ablation utilizing AMBER 3D-LAWT map-guided real-time RF energy titration. @*Conclusion@#We developed and verified an AMBER 3D-cardiac thickness map measured by cardiac CT images for LAWT and LVWT, and tested its feasibility for RF energy titration during clinical catheter ablation.