Use of artificial intelligence and I-Score for prediction of recurrence before catheter ablation of atrial fibrillation.

BACKGROUND: Based solely on pre-ablation characteristics, previous risk scores have demonstrated variable predictive performance. This study aimed to predict the recurrence of AF after catheter ablation by using artificial intelligence (AI)-enabled pre-ablation computed tomography (PVCT) images and pre-ablation clinical data. METHODS: A total of 638 drug-refractory paroxysmal atrial fibrillation (AF) patients undergone ablation were recruited. For model training, we used left atria (LA) acquired from pre-ablation PVCT slices (126,288 images). A total of 29 clinical variables were collected before ablation, including baseline characteristics, medical histories, laboratory results, transthoracic echocardiographic parameters, and 3D reconstructed LA volumes. The I-Score was applied to select variables for model training. For the prediction of one-year AF recurrence, PVCT deep-learning and clinical variable machine-learning models were developed. We then applied machine learning to ensemble the PVCT and clinical variable models. RESULTS: The PVCT model achieved an AUC of 0.63 in the test set. Various combinations of clinical variables selected by I-Score can yield an AUC of 0.72, which is significantly better than all variables or features selected by nonparametric statistics (AUCs of 0.66 to 0.69). The ensemble model (PVCT images and clinical variables) significantly improved predictive performance up to an AUC of 0.76 (sensitivity of 86.7% and specificity of 51.0%). CONCLUSIONS: Before ablation, AI-enabled PVCT combined with I-Score features was applicable in predicting recurrence in paroxysmal AF patients. Based on all possible predictors, the I-Score is capable of identifying the most influential combination.

Inter-subject registration-based one-shot segmentation with alternating union network for cardiac MRI images.

Medical image segmentation based on deep-learning networks makes great progress in assisting disease diagnosis. However, currently, the training of most networks still requires a large amount of data with labels. In reality, labeling a considerable number of medical images is challenging and time-consuming. In order to tackle this challenge, a new one-shot segmentation framework for cardiac MRI images based on an inter-subject registration model called Alternating Union Network (AUN) is proposed in this study. The label of the source image is warped with deformation fields discovered from AUN to segment target images directly. Initially, the volumes are pre-processed by aligning affinely and adjusting the global intensity to simplify subsequent deformation registration. AUN consists of two kinds of subnetworks trained alternately to optimize segmentation gradually. The first kind of subnetwork takes a pair of volumes as inputs and registers them using global intensity similarity. The second kind of subnetwork, which takes the predicted labels generated from the previous subnetwork and the labels refined using the information of intrinsic anatomical structures of interest as inputs, is intensity-independent and focuses attention on registering structures of interest. Specifically, the input of AUN is a pair of a labeled image with the texture in regions of interest removed and a target image. Additionally, a new similarity measurement more appropriate for registering such image pair is defined as Local Squared Error (LSE). The proposed registration-based one-shot segmentation pays attention to the problem of the lack of labeled medical images. In AUN, only one labeled volume is required and a large number of unlabeled ones can be leveraged to improve segmentation performance, which has great advantages in clinical application. In addition, the intensity-independent subnetwork and LSE proposed in this study empower the framework to segment medical images with complicated intensity distribution.

Automated Object Detection in Experimental Data Using Combination of Unsupervised and Supervised Methods.

Deep neural networks (DNN) have shown their success through computer vision tasks such as object detection, classification, and segmentation of image data including clinical and biological data. However, supervised DNNs require a large volume of labeled data to train and great effort to tune hyperparameters. The goal of this study is to segment cardiac images in movie data into objects of interest and a noisy background. This task is one of the essential tasks before statistical analysis of the images. Otherwise, the statistical values such as means, medians, and standard deviations can be erroneous. In this study, we show that the combination of unsupervised and supervised machine learning can automatize this process and find objects of interest accurately. We used the fact that typical clinical/biological data contain only limited kinds of objects. We solve this problem at the pixel level. For example, if there is only one object in an image, there are two types of pixels: object pixels and background pixels. We can expect object pixels and background pixels are quite different and they can be grouped using unsupervised clustering methods. In this study, we used the k-means clustering method. After finding object pixels and background pixels using unsupervised clustering methods, we used these pixels as training data for supervised learning. In this study, we used logistic regression and support vector machine. The combination of the unsupervised method and the supervised method can find objects of interest and segment images accurately without predefined thresholds or manually labeled data.

Anomalous pulmonary venous connections and related anomalies: nomenclature, embryology, anatomy, and morphology.

This article combines material from three complementary overviews presented in the Symposium on Pulmonary Venous Anomalies during the Joint Meeting of the World Society for Pediatric and Congenital Heart Surgery and Sociedad Latina de Cardiologia y Cirugia Cardiovascular Pediátrica in Lima, Peru. We discuss the embryologic basis for nomenclature, the hierarchical diagnostic categories, and the important anatomic and morphologic characteristics of anomalous pulmonary venous connections. The anatomic descriptions help to guide an understandable and sensible approach to the diagnosis and surgical management of these various disorders.

Report from the international society for nomenclature of paediatric and congenital heart disease: creation of a visual encyclopedia illustrating the terms and definitions of the international pediatric and congenital cardiac code.

Tremendous progress has been made in the field of pediatric heart disease over the past 30 years. Although survival after heart surgery in children has improved dramatically, complications still occur, and optimization of outcomes for all patients remains a challenge. To improve outcomes, collaborative efforts are required and ultimately depend on the possibility of using a common language when discussing pediatric and congenital heart disease. Such a universal language has been developed and named the International Pediatric and Congenital Cardiac Code (IPCCC). To make the IPCCC more universally understood, efforts are under way to link the IPCCC to pictures and videos. The Archiving Working Group is an organization composed of leaders within the international pediatric cardiac medical community and part of the International Society for Nomenclature of Paediatric and Congenital Heart Disease (www.ipccc.net). Its purpose is to illustrate, with representative images of all types and formats, the pertinent aspects of cardiac diseases that affect neonates, infants, children, and adults with congenital heart disease, using the codes and definitions associated with the IPCCC as the organizational backbone. The Archiving Working Group certifies and links images and videos to the appropriate term and definition in the IPCCC. These images and videos are then displayed in an electronic format on the Internet. The purpose of this publication is to report the recent progress made by the Archiving Working Group in establishing an Internet-based, image encyclopedia that is based on the standards of the IPCCC.