Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning

• URL: http://arxiv.org/abs/2511.02210v1
• Date: Tue, 04 Nov 2025 03:02:27 GMT
• Title: Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning
• Authors: Anders Austlid Taskén, Thierry Judge, Erik Andreas Rye Berg, Jinyang Yu, Bjørnar Grenne, Frank Lindseth, Svend Aakhus, Pierre-Marc Jodoin, Nicolas Duchateau, Olivier Bernard, Gabriel Kiss,
• Abstract summary: This study introduces the first automated pipeline, autoStrain, for SLS estimation in transesophageal echocardiography (TEE) using deep learning (DL) methods for motion estimation.<n>We present a comparative analysis of two DL approaches: TeeFlow, based on the RAFT optical flow model for dense frame-to-frame predictions, and TeeTracker, based on the CoTracker point trajectory model for sparse long-sequence predictions.<n>Our evaluation shows that TeeTracker outperforms TeeFlow in accuracy, achieving a mean distance error in motion estimation of 0.65 mm on a synTEE test dataset.
• Score: 4.266983119889826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmental longitudinal strain (SLS) of the left ventricle (LV) is an important prognostic indicator for evaluating regional LV dysfunction, in particular for diagnosing and managing myocardial ischemia. Current techniques for strain estimation require significant manual intervention and expertise, limiting their efficiency and making them too resource-intensive for monitoring purposes. This study introduces the first automated pipeline, autoStrain, for SLS estimation in transesophageal echocardiography (TEE) using deep learning (DL) methods for motion estimation. We present a comparative analysis of two DL approaches: TeeFlow, based on the RAFT optical flow model for dense frame-to-frame predictions, and TeeTracker, based on the CoTracker point trajectory model for sparse long-sequence predictions. As ground truth motion data from real echocardiographic sequences are hardly accessible, we took advantage of a unique simulation pipeline (SIMUS) to generate a highly realistic synthetic TEE (synTEE) dataset of 80 patients with ground truth myocardial motion to train and evaluate both models. Our evaluation shows that TeeTracker outperforms TeeFlow in accuracy, achieving a mean distance error in motion estimation of 0.65 mm on a synTEE test dataset. Clinical validation on 16 patients further demonstrated that SLS estimation with our autoStrain pipeline aligned with clinical references, achieving a mean difference (95\% limits of agreement) of 1.09% (-8.90% to 11.09%). Incorporation of simulated ischemia in the synTEE data improved the accuracy of the models in quantifying abnormal deformation. Our findings indicate that integrating AI-driven motion estimation with TEE can significantly enhance the precision and efficiency of cardiac function assessment in clinical settings.

Related papers

• Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering [94.37535002230504]
  We develop a training-free, inference-time control framework termed Semantically Decoupled Latent Steering.<n>Our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition.<n>We show that our approach significantly reduces the probability of historical hallucinations.
  arXiv  Detail & Related papers  (2026-02-27T04:49:01Z)
• Transparent Early ICU Mortality Prediction with Clinical Transformer and Per-Case Modality Attribution [42.85462513661566]
  We present a lightweight, transparent multimodal ensemble that fuses physiological time-series measurements with unstructured clinical notes from the first 48 hours of an ICU stay.<n>A logistic regression model combines predictions from two modality-specific models: a bidirectional LSTM for vitals and a finetuned ClinicalModernBERT transformer for notes.<n>On the MIMIC-III benchmark, our late-fusion ensemble improves discrimination over the best single model while maintaining well-calibrated predictions.
  arXiv  Detail & Related papers  (2025-11-19T20:11:49Z)
• Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals [0.5473229173811306]
  We present a Deep Learning (DL) model for the localization of single manifest APs across 24 cardiac regions.<n>Our model achieves localization accuracy above 95%, with a sensitivity of 94.32% and specificity of 99.78%.<n>This work demonstrates the potential of combining cardiac digital twins with explainable DL to enable accurate, transparent, and non-invasive AP localization.
  arXiv  Detail & Related papers  (2025-11-08T11:39:59Z)
• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)
• Explainable Parallel CNN-LSTM Model for Differentiating Ventricular Tachycardia from Supraventricular Tachycardia with Aberrancy in 12-Lead ECGs [4.263117296632119]
  We propose a computationally efficient deep learning solution to improve diagnostic accuracy and provide model interpretability for clinical deployment.<n>A novel lightweight parallel deep architecture is introduced. Each pipeline processes individual ECG leads using two 1D-CNN blocks to extract local features.<n>The model achieved $95.63%$ accuracy ($95%$ CI: $93.07-98.19%$), with sensitivity=$95.10%$, specificity=$96.06%$, and F1-score=$95.12%$.
  arXiv  Detail & Related papers  (2025-07-14T12:12:34Z)
• Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion [8.884066499888718]
  Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics.<n>Patient-specific digital twins (DTs) modeling temporally varying motion were created to assess the accuracy of DIR methods.
  arXiv  Detail & Related papers  (2025-07-02T17:22:47Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations.<n>We incorporate elements modeling effects to better align simulated data with real-world measurements.<n>The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior [1.8631187219907217]
  We propose a time-efficient surrogate model, powered by machine learning, for the estimation of pulsatile hemodynamics.<n>We show that our model produces accurate estimates of the pulsatile velocity and pressure.<n>This shows that deep vectorised operators are a powerful modelling tool for cardiovascular hemodynamics estimation.
  arXiv  Detail & Related papers  (2024-10-15T12:24:50Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images [2.6879908098704544]
  We propose a novel deep learning-based method for the automatic segmentation of coronary arteries in angiographic images.<n>The model combines the MedSAM and VM-UNet architectures to achieve high-performance results.<n>The stenosis detection algorithm achieved a true positive rate (TPR) of 0.5867 and a positive predictive value (PPV) of 0.5911.
  arXiv  Detail & Related papers  (2024-06-01T16:45:33Z)
• Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction [36.58987036154144]
  This paper proposes a new pipeline method based on deep neural networks and ensemble learning to quantify left ventricular ejection fraction (LVEF) The method was developed and validated in an open-source dataset containing 10,030 echocardiograms. This study demonstrates that an automated neural network-based calculation of LVEF is comparable to expert clinicians performing frame-by-frame manual evaluation of cardiac systolic function.
  arXiv  Detail & Related papers  (2024-03-18T18:09:22Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.