Technology & Digital Applications Flashcards

Artificial intelligence algorithms applied to a 12-lead ECG obtained in normal sinus rhythm have demonstrated the ability to detect:

Answer: Subclinical or paroxysmal atrial fibrillation not present on the current tracing

AI-ECG models can identify a 'fingerprint' of subclinical atrial cardiopathy in sinus rhythm that predicts current or future paroxysmal AF.

In AI-driven clinical decision support for chest pain evaluation, which input combination provides the most accurate early risk stratification?

Answer: Integration of high-sensitivity troponin, ECG findings, and structured clinical history

Validated tools such as the HEART score integrate troponin, ECG, and clinical history into a calibrated risk prediction that outperforms individual data elements alone.

Which machine learning architecture is most widely used for automated segmentation of cardiac chambers from echocardiographic and cardiac MRI images?

Answer: Convolutional neural networks (CNNs)

CNNs excel at image-based tasks by learning hierarchical spatial features, making them the dominant architecture for cardiac image segmentation.

In the context of multicenter AI research in cardiology, 'federated learning' is best described as:

Answer: Training AI models locally at each institution and sharing only model parameters, not patient data

Federated learning trains models locally and aggregates only weight updates, allowing collaborative AI development without transferring protected health information.

At the population scale, natural language processing (NLP) of electronic health record clinical notes is most valuable for identifying patients with:

Answer: Undiagnosed atrial fibrillation mentioned in free-text notes but not coded

NLP extracts clinically relevant diagnoses and risk factors from unstructured text, capturing AF diagnoses and symptoms that were never assigned a billing code.

A deep learning model trained on non-contrast cardiac CT data can predict which outcome that is NOT directly visible on the images?

Answer: Five-year risk of major adverse cardiovascular events (MACE)

AI models extract subtle imaging biomarkers (e.g., pericardial fat, myocardial texture) that encode long-term prognostic information beyond what human readers directly measure.

In digital phenotyping for heart failure management, continuous passive data collected from smartphones and wearables is primarily used to:

Answer: Detect subtle changes in activity, sleep, and heart rate that may signal impending decompensation

Passive wearable signals such as declining step count, altered sleep patterns, and reduced HRV can identify early decompensation days before overt symptoms appear.