Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms

• URL: http://arxiv.org/abs/2203.13038v1
• Date: Thu, 24 Mar 2022 12:33:58 GMT
• Title: Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms
• Authors: Hanna Ragnarsdottir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan and Julia Vogt
• Abstract summary: Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases. We present an interpretable multi-view video-based deep learning approach to predict PH for a cohort 194 newborns using echocardiograms.
• Score: 2.770437783544638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Therefore, accurate and early detection of PH is crucial for successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we present an interpretable multi-view video-based deep learning approach to predict PH for a cohort of 194 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice.

Related papers

• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• Adversarial Contrastive Learning Based Physics-Informed Temporal Networks for Cuffless Blood Pressure Estimation [37.94387581519217]
  We introduce a novel physics-informed temporal network(PITN) with adversarial contrastive learning to enable precise BP estimation with very limited data. We then employ adversarial training to generate extra physiological time series data, improving PITN's robustness in the face of sparse subject-specific data.
  arXiv  Detail & Related papers  (2024-08-16T02:17:21Z)
• Echocardiogram Foundation Model -- Application 1: Estimating Ejection Fraction [2.4164193358532438]
  We introduce EchoAI, an echocardiogram foundation model, that is trained using self-supervised learning (SSL) on 1.5 million echocardiograms. We evaluate our approach by fine-tuning EchoAI to estimate the ejection fraction achieving a mean absolute percentage error of 9.40%.
  arXiv  Detail & Related papers  (2023-11-21T13:00:03Z)
• Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography [53.38589633687604]
  We propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it.
  arXiv  Detail & Related papers  (2023-04-26T14:11:04Z)
• Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning [0.0]
  Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart. The goal of this project is to create a neural network model which can process an ECG signal and detect the presence of PH with a confidence probability.
  arXiv  Detail & Related papers  (2023-04-24T21:00:16Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records [46.024501445093755]
  Temporal modelling of medical history can be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications. We present Foresight, a novel GPT3-based pipeline that uses NER+L tools (i.e. MedCAT) to convert document text into structured, coded concepts.
  arXiv  Detail & Related papers  (2022-12-13T19:06:00Z)
• AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography [1.0082848901582044]
  Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. Recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction.
  arXiv  Detail & Related papers  (2022-03-21T10:59:48Z)
• Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening [7.496518691842825]
  We propose a pipeline for automated data curation and classification. We exploit an auxiliary view classification task to bias features toward relevant cardiac structures. This bias helps to improve in F1-scores from 0.72 and 0.77 to 0.87 and 0.85 for healthy and CHD classes respectively.
  arXiv  Detail & Related papers  (2020-08-16T17:53:41Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation [5.050648346154715]
  Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI.
  arXiv  Detail & Related papers  (2020-01-08T10:04:42Z)

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.