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Laumer F, Amrani M, Manduchi L, Beuret A, Rubi L, Dubatovka A, Matter CM, Buhmann JM. Weakly supervised inference of personalized heart meshes based on echocardiography videos. Med Image Anal 2023; 83:102653. [PMID: 36327655 DOI: 10.1016/j.media.2022.102653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 08/27/2022] [Accepted: 10/08/2022] [Indexed: 12/12/2022]
Abstract
Echocardiography provides recordings of the heart chamber size and function and is a central tool for non-invasive diagnosis of heart diseases. It produces high-dimensional video data with substantial stochasticity in the measurements, which frequently prove difficult to interpret. To address this challenge, we propose an automated framework to enable the inference of a high resolution personalized 4D (3D plus time) surface mesh of the cardiac structures from 2D echocardiography video data. Inferring such shape models arises as a key step towards accurate personalized simulation that enables an automated assessment of the cardiac chamber morphology and function. The proposed method is trained using only unpaired echocardiography and heart mesh videos to find a mapping between these distinct visual domains in a self-supervised manner. The resulting model produces personalized 4D heart meshes, which exhibit a high correspondence with the input echocardiography videos. Furthermore, the 4D heart meshes enable the automatic extraction of echocardiographic variables, such as ejection fraction, myocardial muscle mass, and volumetric changes of chamber volumes over time with high temporal resolution.
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Affiliation(s)
- Fabian Laumer
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland.
| | - Mounir Amrani
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Laura Manduchi
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Ami Beuret
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Lena Rubi
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
| | - Alina Dubatovka
- Institute for Machine Learning at ETH Zürich, Zürich, Switzerland
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Laumer F, Di Vece D, Cammann VL, Würdinger M, Petkova V, Schönberger M, Schönberger A, Mercier JC, Niederseer D, Seifert B, Schwyzer M, Burkholz R, Corinzia L, Becker AS, Scherff F, Brouwers S, Pazhenkottil AP, Dougoud S, Messerli M, Tanner FC, Fischer T, Delgado V, Schulze PC, Hauck C, Maier LS, Nguyen H, Surikow SY, Horowitz J, Liu K, Citro R, Bax J, Ruschitzka F, Ghadri JR, Buhmann JM, Templin C. Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction. JAMA Cardiol 2022; 7:494-503. [PMID: 35353118 PMCID: PMC8968683 DOI: 10.1001/jamacardio.2022.0183] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Importance Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied. Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI. Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021. Exposure Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis. Results In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%). Conclusions and Relevance In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.
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Affiliation(s)
- Fabian Laumer
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Davide Di Vece
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Victoria L Cammann
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Würdinger
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Vanya Petkova
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | | | | | - Julien C Mercier
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - David Niederseer
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Burkhardt Seifert
- Division of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Moritz Schwyzer
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | | | - Luca Corinzia
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Anton S Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Frank Scherff
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Sofie Brouwers
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Aju P Pazhenkottil
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland.,Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Svetlana Dougoud
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Felix C Tanner
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Thomas Fischer
- Department of Cardiology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Victoria Delgado
- Department of Cardiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - P Christian Schulze
- Department of Internal Medicine I, University Hospital Jena, Friedrich-Schiller-University Jena, Jena, Germany
| | - Christian Hauck
- Klinik und Poliklinik für Innere Medizin II, Universitätsklinikum Regensburg, Regensburg, Germany
| | - Lars S Maier
- Klinik und Poliklinik für Innere Medizin II, Universitätsklinikum Regensburg, Regensburg, Germany
| | - Ha Nguyen
- Department of Cardiology, Basil Hetzel Institute, Queen Elizabeth Hospital, University of Adelaide, Adelaide, Australia
| | - Sven Y Surikow
- Department of Cardiology, Basil Hetzel Institute, Queen Elizabeth Hospital, University of Adelaide, Adelaide, Australia
| | - John Horowitz
- Department of Cardiology, Basil Hetzel Institute, Queen Elizabeth Hospital, University of Adelaide, Adelaide, Australia
| | - Kan Liu
- Division of Cardiology, Heart and Vascular Center, University of Iowa, Iowa City
| | - Rodolfo Citro
- Heart Department, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", Salerno, Italy.,IRCCS Neuromed, Pozzilli, (Isernia) Italy
| | - Jeroen Bax
- Department of Cardiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Frank Ruschitzka
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | - Jelena-Rima Ghadri
- Department of Cardiology, University Hospital Zurich, Zurich, Switzerland
| | | | - Christian Templin
- Division of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Meyer A, Geyer R, Lanmüller P, Laumer F, Beuret A, Pfahringer B, Hommel M, O'brien B, Buhmann J, Falk V. Ambient Intelligence in Postoperative Critical Care: First Observations of a Novel Monitoring Approach. Thorac Cardiovasc Surg 2022. [DOI: 10.1055/s-0042-1742906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- A. Meyer
- Augustenburger Platz 1, Berlin, Deutschland
| | | | | | | | | | | | - M. Hommel
- Augustenburger Platz 1, Berlin, Deutschland
| | - B. O'brien
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Deutschland
| | | | - V. Falk
- Department of Cardiovascular Surgery, Charité – Universitätsmedizin Berlin, Berlin, Deutschland
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Di Vece D, Laumer F, Schwyzer M, Burkholz R, Corinzia L, Cammann V, Citro R, Bax J, Ghadri J, Buhmann J, Templin C. Artificial intelligence in echocardiography diagnostics – detection of takotsubo syndrome. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.1221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Machine learning allows classifying diseases based only on raw echocardiographic imaging data and is therefore a landmark in the development of computer-assisted decision support systems in echocardiography.
Purpose
The present study sought to determine the value of deep (machine) learning systems for automatic discrimination of takotsubo syndrome and acute myocardial infarction.
Methods
Apical 2- and 4-chamber echocardiographic views of 110 patients with takotsubo syndrome and 110 patients with acute myocardial infarction were used in the development, training and validation of a deep learning approach, i.e. a convolutional autoencoder (CAE) for feature extraction followed by classical machine learning models for classification of the diseases.
Results
The deep learning model achieved an area under the receiver operating curve (AUC) of 0.801 with an overall accuracy of 74.5% for 5-fold cross validation evaluated on a clinically relevant dataset. In comparison, experienced cardiologists achieved AUCs in the range 0.678–0.740 and an average accuracy of 64.5% on the same dataset.
Conclusions
A real-time system for fully automated interpretation of echocardiographic videos was established and trained to differentiate takotsubo syndrome from acute myocardial infarction. The framework provides insight into the algorithms' decision process for physicians and yields new and valuable information on the manifestation of disease patterns in echocardiographic data. While our system was superior to cardiologists in echocardiography-based disease classification, further studies should be conducted in a larger patient population to prove its clinical application.
Funding Acknowledgement
Type of funding source: None
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Affiliation(s)
- D Di Vece
- University Hospital Zurich, Zurich, Switzerland
| | - F Laumer
- Swiss Federal Institute of Technology Zurich (ETH Zurich), Department of Computer Science, Zurich, Switzerland
| | - M Schwyzer
- University Hospital Zurich, Institute of Diagnostic and Interventional Radiology, Zurich, Switzerland
| | - R Burkholz
- Swiss Federal Institute of Technology Zurich (ETH Zurich), Department of Computer Science, Zurich, Switzerland
| | - L Corinzia
- Swiss Federal Institute of Technology Zurich (ETH Zurich), Department of Computer Science, Zurich, Switzerland
| | - V.L Cammann
- University Hospital Zurich, Zurich, Switzerland
| | - R Citro
- AOU S. Giovanni di Dio e Ruggi d'Aragona, Heart Department, Salerno, Italy
| | - J Bax
- Leiden University Medical Center, Department of Cardiology, Leiden, Netherlands (The)
| | - J.R Ghadri
- University Hospital Zurich, Zurich, Switzerland
| | - J.M Buhmann
- Swiss Federal Institute of Technology Zurich (ETH Zurich), Department of Computer Science, Zurich, Switzerland
| | - C Templin
- University Hospital Zurich, Zurich, Switzerland
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