Radiomics-Based Model Using Tumor and Peritumoral Features with Semi-Supervised and Privileged Learning for Metastatic Risk Prediction in Lung Cancer: A Multi-Site Study.

Background: Lung cancer is the leading cause of cancer-related mortality globally. Early detection of high-risk patients for local or distant metastasis is challenging for better monitoring and treatment planning. Machine learning models have been proposed for diagnosis and prediction of metastasis risk.

AI potential in PET/CT cancer imaging.

Positron emission tomography/computed tomography (PET/CT) is a hybrid medical imaging technique that combines PET and CT to provide detailed images of the body's anatomical structures and metabolic activity. It is frequently used for oncology and other medical diagnoses. This overview aims to examine how artificial intelligence (AI) has been used in PET/CT, based on recent state-of-art.

Association of beat-to-beat P-wave analysis index to the extent of left atrial low-voltage areas in patients with paroxysmal atrial fibrillation.

Background: Left atrial (LA) fibrosis has been shown to be associated with atrial fibrillation (AF) recurrence. Beat-to-beat (B2B) index is a non-invasive classifier, based on B2B P-wave morphological and wavelet analysis, shown to be associated with AF incidence and recurrence. In this study, we tested the hypothesis that the B2B index is associated with the extent of LA low-voltage areas (LVAs) on electroanatomical mapping.

P-wave Indices as Predictors of Atrial Fibrillation: The Lion from a Claw.

The P wave, representing the electrical fingerprint of atrial depolarization, contains information regarding spatial and temporal aspects of atrial electrical-and potentially structural-properties. However, technical and biological reasons, including-but not limited to-the low amplitude of the P wave and large interindividual variations in normal or pathologic atrial electrical activity, make gathering and utilizing this information for clinical purposes a rather cumbersome task. However, even crude ECG descriptors, such as P-wave dispersion, have been shown to be of predictive value for assessing the probability that a patient already has or will shortly present with AF.

The potential role of ChatGPT and artificial intelligence in anatomy education: a conversation with ChatGPT.

Purpose: A recent study published in the JMIR Med Educ Journal explored the potential impact of the Generative Pre-Train (ChatGPT), a generative language model, on medical education, research, and practice. In the present study, an interview with ChatGPT was conducted to determine its capabilities and potential for use in anatomy education (AE) and anatomy research (AR).

Methods: The study involved 18 questions asked of ChatGPT after obtaining an online subscription to the 4th edition.

Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool.

Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles.

P-Wave Beat-to-Beat Analysis to Predict Atrial Fibrillation Recurrence after Catheter Ablation.

The identification of patients prone to atrial fibrillation (AF) relapse after catheter ablation is essential for better patient selection and risk stratification. The current prospective cohort study aims to validate a novel P-wave index based on beat-to-beat (B2B) P-wave morphological and wavelet analysis designed to detect patients with low burden AF as a predictor of AF recurrence within a year after successful catheter ablation. From a total of 138 consecutive patients scheduled for AF ablation, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained.

Towards Data Integration for AI in Cancer Research.

Cancer research is increasing relying on data-driven methods and Artificial Intelligence (AI), to increase accuracy and efficiency in decision making. Such methods can solve a variety of clinically relevant problems in cancer diagnosis and treatment, provided that an adequate data availability is ensured. The generation of multicentric data repositories poses a series of integration and harmonization challenges.

Beat-to-Beat P-Wave Analysis Outperforms Conventional P-Wave Indices in Identifying Patients with a History of Paroxysmal Atrial Fibrillation during Sinus Rhythm.

Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history.

Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI.

Background And Objective: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses.

BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment.

Obesity is a complex disease and its prevalence depends on multiple factors related to the local socioeconomic, cultural and urban context of individuals. Many obesity prevention strategies and policies, however, are horizontal measures that do not depend on context-specific evidence. In this paper we present an overview of BigO (http://bigoprogram.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies.

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization.

Beat-to-beat P-wave morphology as a predictor of paroxysmal atrial fibrillation.

Background And Objectives: Atrial Fibrillation (AF) is the most common cardiac arrhythmia. The initiation and the perpetuation of AF is linked with phenomena of atrial remodeling, referring to the modification of the electrical and structural characteristics of the atrium. P-wave morphology analysis can reveal information regarding the propagation of the electrical activity on the atrial substrate.

P wave analysis with wavelets identifies hypertensive patients at risk of recurrence of atrial fibrillation: A case-control study and 1year follow-up.

Aims: Hypertension is a major risk factor for atrial fibrillation (AF); however, reliable non-invasive tools to assess AF risk in hypertensive patients are lacking. We sought to evaluate the efficacy of P wave wavelet analysis in predicting AF risk recurrence in a hypertensive cohort.

Methods: We studied 37 hypertensive patients who presented with an AF episode for the first time and 37 age- and sex-matched hypertensive controls without AF.