In cancer patients at risk of cardiotoxicity, the precision of 2D echocardiographic AI analysis exceeds that of experts, matching that of 3D LVEF assessment.
Abstract
Serial imaging demands measurement precision – particularly for cardiotoxicity screening where small changes gatekeeper major management decisions. Automated measurement using artificial intelligence (AI)-based techniques should reduce variability and increase confidence for detection of true interval change. We directly compared the precision of fully-automated AI versus expert manual analysis of left ventricular function by echocardiography.
Consecutive cancer patients referred for cardiotoxicity monitoring underwent same day repeat 2D echocardiography (n=60, 83% female, mean age 52±12years, mean LVEF 57±7%), with 3D acquisition where feasible (n=45). All 2D images underwent blinded manual analysis by fully automated analysis software and four individual experts. Quantitative 2D LVEF measurement was feasible in 97% scans by both methods. Mean absolute difference (MAD) between repeat scans was significantly lower for AI than experts for both 2D LVEF (3.6% [CI 2.8–4.4] vs 6.6% [CI 5.3–7.9], p=0.011) and global longitudinal strain (1.5% [CI 1.1–1.9] vs 2.5% [CI 2.0–3.0], p=0.006). In participants with 3DE datasets, 3D LVEF MAD was lower than manual 2D LVEF (3.6% [CI 2.8-4.6] vs 6.4% [CI 5.0-8.0], p=0.006) but comparable to AI-derived 2D LVEF (3.5% [CI 2.9-4.2], p=0.4). In a subset (n=48) also with same day cardiovascular magnetic resonance studies (CMR), AI 2DE analysis demonstrated stronger agreement with CMR than expert 2DE analysis (LVEF MAD 5.0% [CI 4.1-5.9] vs 7.8% ([CI 6.6-9.0] p=0.006).
In cancer patients at risk of cardiotoxicity, the precision of 2D echocardiographic AI analysis exceeds that of experts, matching that of 3D LVEF assessment.
AI-based 3D TTE semi-automated measurement has the potential to enhance the efficiency and reproducibility of RV function assessment, reducing examiner workload and improving clinical workflow.
Noriko Shiokawa, M. Izumo, Yukio Sato et al.· Journal of Echocardiography· 0 citations
Anthracyclines remain cornerstone agents in oncology, yet their cardiotoxic potential poses a substantial clinical challenge. Up to 30% of treated patients develop some degree of cardiac dysfunction, with overt heart failure occurring in 2–5% of cases. Current surveillance relies on serial ejection fraction measu...
N. Kavelashvili, N. Sharashidze, F. Schiedat et al.· European Heart Journal, Supp...· 0 citations
Two-dimensional ultrasound is optimal for screening; RT-3DE shows highest consistency with cardiovascular magnetic resonance for precise diagnosis, and 3DE-LACI has demonstrated independent prognostic value in cardiac amyloidosis.
He-Yang Liu, Quan Li, Wenjie Han et al.· Frontiers in Cardiovascular...· 0 citations
BACKGROUND
High-quality echocardiography is essential for accurate and reproducible assessment of cardiac functional indices, which are highly dependent on adequate image quality and proper probe alignment. An artificial intelligence (AI)-based approach may enable automated image quality assessment.
AIMS
Our aim was...
Wojciech Nazar, D. Kaufmann, E. Wabich et al.· Kardiologia polska· 0 citations
Echocardiography is the cornerstone for risk stratification, diagnosis, and monitoring of cancer therapy–related cardiac dysfunction (CTRCD)(1). Artificial intelligence (AI)–guided echocardiography has shown high accuracy and reliability in diverse cardiac populations and may reduce variability while improving wo...
M. Andres, V. Maharajan, M. C. Llamedo et al.· European Heart Journal, Supp...· 0 citations
Related blog posts
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.