Fully automated analysis of cardiotoxicity surveillance echocardiograms: A direct test-retest precision comparison with experts

Real-life clinical validation of AI-assisted echocardiography for aortic root and LVOT measurements against human readers and cardiac magnetic resonance

AI algorithms may improve echocardiographic measurement standardization, but direct validation against cardiovascular magnetic resonance (CMR) remains limited. This study aimed to evaluate the agreement between AI-assisted transthoracic echocardiography (TTE), expert manual TTE, and CMR as a gold standard for aortic root and left ventricular outflow tract (LVOT) diameters. Out of 183 patients with analyzable TTE and CMR recordings performed within seven days, 169 had complete measurements of LVOT, sinotubular junction (SoV) and sinus of Valsalva (STJ) by AI and were included in the primary analysis. Automated AI measurements were obtained using the US2.AI platform and compared with those of expert echocardiographers and CMR. Agreement was assessed using intraclass correlation coefficients (ICC) and Bland-Altman analysis. Agreement between Expert 1 and CMR, and Expert 2 and CMR, yielded ICCs: 0.63 (95% CI: 0.50–0.73) and 0.51 (95% CI: 0.36–0.65) for LVOT, 0.71 (95% CI: 0.63–0.78) and 0.82 (95% CI: 0.73–0.87) for STJ, and 0.85 (95% CI: 0.79–0.89) to 0.85 (95% CI: 0.78–0.89) for SoV, respectively. AI-derived measurements demonstrated comparable agreement with CMR: ICC 0.70 (95% CI: 0.61–0.77) for LVOT, 0.77 (95% 0.70–0.83) for STJ, and 0.77 (95% 0.70–0.83) for SoV. Clinically significant differences (≥2 mm for LVOT and ≥4 mm for STJ and SoV) between AI vs CMR measurements were observed in 36.7% of LVOT, 20% of STJ, and 11.8% of SoV measurements. AI-assisted echocardiography showed comparable agreement for aortic root and LVOT measurements, but physician oversight remains necessary.

Agreement of AI-assisted semi-automated right ventricular function analysis using 3D transthoracic echocardiography with artificial intelligence.

Layer-specific myocardial strain for early detection of anthracycline-induced cardiotoxicity: the CARDIAC-STRAIN prospective diagnostic study protocol

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 measurements, which detect damage only after significant myocardial injury has occurred. Histopathological evidence consistently shows that the subendocardial layer suffers earliest and most severely from anthracycline exposure—often weeks before any decline in global ventricular function becomes apparent. This temporal gap represents a missed opportunity for timely cardioprotective intervention. We designed the CARDIAC-STRAIN study to determine whether layer-specific strain analysis by cardiac magnetic resonance can identify subclinical cardiotoxicity substantially earlier than conventional echocardiographic surveillance, potentially enabling earlier initiation of cardioprotective treatment. This prospective single-centre diagnostic cohort study will recruit 120 consecutive patients scheduled for anthracycline-based chemotherapy (sample size calculated to detect 20% sensitivity difference, power 80%, α=0.05). Eligible participants are aged 18–75 years with preserved baseline ejection fraction (≥50%) and no prior anthracycline exposure or known cardiomyopathy. Each patient undergoes blinded comprehensive cardiac evaluation at four timepoints: baseline, after the fourth chemotherapy cycle, four weeks post-treatment, and at six months' follow-up. The protocol includes 1.5T cardiac MRI with cine sequences, native and post-contrast T1 mapping, T2 mapping, and late gadolinium enhancement. Layer-specific strain is quantified at subendocardial, midmyocardial, and subepicardial levels using dedicated feature-tracking software. Parallel assessments include three-dimensional echocardiography and cardiac biomarkers (troponin, NT-proBNP). Primary endpoints: ejection fraction decline >10% to below 50%, decline >15% with preserved function, or layer-specific strain deterioration >15% from baseline. Study hypothesis: We hypothesize that layer-specific strain analysis will detect subclinical myocardial injury approximately 2–4 weeks earlier than conventional ejection fraction monitoring, with significantly improved diagnostic sensitivity compared to standard surveillance protocols. Expected outcomes: If layer-specific strain analysis proves capable of reliably identifying subclinical cardiotoxicity before irreversible damage occurs, this approach may help shift clinical practice from heart failure treatment to early prevention in cardio-oncology. The study received ethics committee approval in November 2025, and patient recruitment is underway.

Echocardiographic assessment of left atrioventricular coupling index: technical approaches and clinical implementation strategies

Background The Left Atrioventricular Coupling Index (LACI) is an emerging echocardiographic parameter for assessing left atrial and ventricular diastolic function. While accumulating evidence supports its prognostic value, technical standardization for clinical implementation remains lacking. Methods This narrative review compares four LACI measurement approaches: two-dimensional echocardiography (2DE), real-time three-dimensional echocardiography (RT-3DE), speckle-tracking echocardiography (STE), and artificial intelligence (AI)-assisted analysis. We evaluate technical characteristics, standardization challenges, and evidence gaps. Results 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. STE aids mechanistic exploration but requires algorithmic standardization. AI demonstrates potential but needs multicenter validation. Key limitations include absent cross-platform calibration, fragmented reference values, and lack of externally validated cutoffs. Conclusion LACI is a promising marker of diastolic function with proven prognostic value; however, technical heterogeneity and inconsistent thresholds have hindered its clinical application. Future efforts should focus on cross-manufacturer standardization, the establishment of reference values for specific populations, and randomized controlled trials evaluating LACI as a guide for treatment.

Artificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A proof-of-concept study.

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 to develop and internally validate an AI-based algorithm to predict image quality from selected echocardiographic frames in candidates for implantable cardioverter-defibrillator and cardiac resynchronization therapy with a defibrillator implantation. METHODS In this retrospective cross-sectional study, 248 patients (297 echocardiographic examinations) were included. Demographic, electrocardiographic, echocardiographic, and clinical data were collected. Apical 2-, 3-, and 4-chamber views were extracted for image quality analysis, yielding a total of 909 echocardiograms. Image quality was assessed using end-diastolic frames. An internally validated scoring framework was applied, demonstrating high interclass correlation. RESULTS Regression models provided more clinically relevant information than classification models. Visual transformer models achieved Pearson correlation coefficients similar to those of convolutional neural networks (up to 0.812 and 0.772, respectively; P = 0.31). Architectures trained on end-diastolic frames achieved comparable Pearson correlation coefficients to those trained on combined end-diastolic and end-systolic frames. Compared with human experts, the models showed significantly higher absolute percentage errors, with values of 11%-12% (median) vs. 7.9% (mean) for the total image quality score and 13%-14% (median) vs. 8.8% (mean) for the border quality score. CONCLUSIONS Regression models demonstrated the highest performance. An internally validated AI model can predict an echocardiographic image quality score in a small cohort of cardiac resynchronization therapy with a defibrillator/implantable cardioverter-defibrillator candidates. However, external and prospective validation will be required to establish its generalizability, reliability, and utility before clinical application.

Validation of a deep learning-based workflow for the interpretation of the echocardiogram in a cardiooncology population

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 workflow efficiency(2, 3). However, this technology has not yet been validated in a dedicated cohort of patients with cancer. To evaluate the accuracy and reliability of AI-guided echocardiography in assessing left ventricular ejection fraction (LVEF) and additional parameters, compared with conventional echocardiography, in a cardio-oncology population. This study included patients identified retrospectively from a cardio-oncology registry. Studies, that had already been analysed manually by expert sonographers and reported using AGFA PACS system, were uploaded to the US2.ai platform for automated analysis. The primary outcome was the level of agreement (LoA) between AI-guided and standard echocardiography for LVEF. Secondary outcomes included LoA for additional echocardiographic parameters and LoA between AI- LVEF and 3D LVEF. The performance of the deep learning (DL) algorithm in identifying LVEF <50% was evaluated using the area under the receiver operating characteristic curve (ROC-AUC). Subgroup analyses were performed in predefined populations clinically relevant in cardio-oncology. A total of 282 patients were included. Mean age was 60 ± 16 years, and 61% were women. Breast cancer was the most frequent malignancy (30.5%), followed by haematological malignancies (16.7%) and gastrointestinal tumours (10.6%). Manual median 2D LVEF was 60% (IQR: 55-64) and AI-derived LVEF was 59.2% (IQR: 53-64) showing good agreement and correlation (bias: −0.138, SD: 5.38, 95% LoA: −10.7 to 10.4, ICC: 0.791, 95% CI: 0.742–0.831, Spearman ρ: 0.718,), Table 1. Comparison between 3D echocardiography LVEF and AI-derived 2D LVEF showed similar agreement with narrower limits (bias: −0.13, 95% LoA: −9.51 to 9.26). The DL algorithm accurately identified LVEF <50% (ROC-AUC: 0.918, 95% CI: 0.875–0.961), Figure 1. Subgroup analyses demonstrated consistent agreement in patients with breast cancer, body mass index >30, prior radiotherapy and pericardial effusion. In a large real-world cardio-oncology cohort, AI-guided echocardiography demonstrated strong agreement with conventional echocardiography for LVEF assessment and high accuracy for detecting clinically relevant LV dysfunction. Performance was consistent across key subgroups, supporting the feasibility, reliability, and potential clinical value of integrating DL-based analysis into routine cardio-oncology echocardiographic workflows.Agreement between manual and AI-echo  AUC-ROC curve for LVEF<50%