1
|
Mao Y, Zhu G, Zhai M, Ma Y, Li L, Jin P, Liu Y, Yang J. Transcatheter Aortic Valve Replacement and Coronary Protection Guided by Deep Learning and 3-Dimensional Printing. Surg Innov 2024; 31:256-262. [PMID: 38565982 DOI: 10.1177/15533506241244571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
OBJECTIVE In this case report, the auxiliary role of deep learning and 3-dimensional printing technology in the perioperative period was discussed to guide transcatheter aortic valve replacement and coronary stent implantation simultaneously. CASE PRESENTATION A 68-year-old man had shortness of breath and chest tightness, accompanied by paroxysmal nocturnal dyspnea, 2 weeks before presenting at our hospital. Echocardiography results obtained in the outpatient department showed severe aortic stenosis combined with regurgitation and pleural effusion. The patient was first treated with closed thoracic drainage. After 800 mL of pleural effusion was collected, the patient's symptoms were relieved and he was admitted to the hospital. Preoperative transthoracic echocardiography showed severe bicuspid aortic valve stenosis combined with calcification and aortic regurgitation (mean pressure gradient, 42 mmHg). Preoperative computed tomography results showed a type I bicuspid aortic valve with severe eccentric calcification. The leaflet could be seen from the left coronary artery plane, which indicated an extremely high possibility of coronary obstruction. After preoperative imaging assessment, deep learning and 3-dimensional printing technology were used for evaluation and simulation. Guided transcatheter aortic valve replacement and a coronary stent implant were completed successfully. Postoperative digital subtraction angiography showed that the bioprosthesis and the chimney coronary stent were in ideal positions. Transesophageal echocardiography showed normal morphology without paravalvular regurgitation. CONCLUSION The perioperative guidance of deep learning and 3-dimensional printing are of great help for surgical strategy formulation in patients with severe bicuspid aortic valve stenosis with calcification and high-risk coronary obstruction.
Collapse
Affiliation(s)
- Yu Mao
- Department of Cardiovascular Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Guangyu Zhu
- School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Mengen Zhai
- Department of Cardiovascular Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Yanyan Ma
- Department of Cardiovascular Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Lanlan Li
- Department of Cardiovascular Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Ping Jin
- Department of Cardiovascular Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Yang Liu
- Department of Cardiovascular Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Jian Yang
- Department of Cardiovascular Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China
| |
Collapse
|
2
|
Alnasser TN, Abdulaal L, Maiter A, Sharkey M, Dwivedi K, Salehi M, Garg P, Swift AJ, Alabed S. Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging. Front Cardiovasc Med 2024; 11:1323461. [PMID: 38317865 PMCID: PMC10839106 DOI: 10.3389/fcvm.2024.1323461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024] Open
Abstract
Background Segmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods-particularly deep learning (DL)-can be used to automate this process. Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT. Methods Embase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results 18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83-0.91), left ventricle (0.91, IQR 0.89-0.94), left ventricle myocardium (0.83, IQR 0.82-0.92), right atrium (0.88, IQR 0.83-0.90), right ventricle (0.91, IQR 0.85-0.92), and pulmonary artery (0.92, IQR 0.87-0.93). Compliance of studies with CLAIM was variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%). Conclusion Supervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values. Existing studies have been limited by the size and nature of the training datasets, inconsistent descriptions of ground truth annotations and lack of testing in external data or clinical settings. Systematic Review Registration [www.crd.york.ac.uk/prospero/], PROSPERO [CRD42023431113].
Collapse
Affiliation(s)
- Turki Nasser Alnasser
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- College of Applied Medical Science, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi Arabia
| | - Lojain Abdulaal
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Ahmed Maiter
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Pankaj Garg
- Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, United Kingdom
| | - Andrew James Swift
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
- Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom
| |
Collapse
|
3
|
Santaló-Corcoy M, Corbin D, Tastet O, Lesage F, Modine T, Asgar A, Ben Ali W. TAVI-PREP: A Deep Learning-Based Tool for Automated Measurements Extraction in TAVI Planning. Diagnostics (Basel) 2023; 13:3181. [PMID: 37892002 PMCID: PMC10606167 DOI: 10.3390/diagnostics13203181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery for treating severe aortic stenosis. Despite its benefits, the risk of procedural complications necessitates careful preoperative planning. METHODS This study proposes a fully automated deep learning-based method, TAVI-PREP, for pre-TAVI planning, focusing on measurements extracted from computed tomography (CT) scans. The algorithm was trained on the public MM-WHS dataset and a small subset of private data. It uses MeshDeformNet for 3D surface mesh generation and a 3D Residual U-Net for landmark detection. TAVI-PREP is designed to extract 22 different measurements from the aortic valvular complex. A total of 200 CT-scans were analyzed, and automatic measurements were compared to the ones made manually by an expert cardiologist. A second cardiologist analyzed 115 scans to evaluate inter-operator variability. RESULTS High Pearson correlation coefficients between the expert and the algorithm were obtained for most parameters (0.90-0.97), except for left and right coronary height (0.8 and 0.72, respectively). Similarly, the mean absolute relative error was within 5% for most measurements, except for left and right coronary height (11.6% and 16.5%, respectively). A greater consensus was observed among experts than when compared to the automatic approach, with TAVI-PREP showing no discernable bias towards either the lower or higher ends of the measurement spectrum. CONCLUSIONS TAVI-PREP provides reliable and time-efficient measurements of the aortic valvular complex that could aid clinicians in the preprocedural planning of TAVI procedures.
Collapse
Affiliation(s)
- Marcel Santaló-Corcoy
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
- Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Denis Corbin
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
| | | | - Frédéric Lesage
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
- Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
- Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada
| | | | - Anita Asgar
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
- Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Walid Ben Ali
- Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
- Faculty of Medicine, University of Montreal, Montreal, QC H3T 1J4, Canada
| |
Collapse
|
4
|
Cho Y, Park S, Hwang SH, Ko M, Lim DS, Yu CW, Park SM, Kim MN, Oh YW, Yang G. Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography. J Korean Med Sci 2023; 38:e306. [PMID: 37724499 PMCID: PMC10506901 DOI: 10.3346/jkms.2023.38.e306] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 07/03/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). METHODS This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). RESULTS In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. CONCLUSION Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.
Collapse
Affiliation(s)
- Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
- AI Center, Korea University Anam Hospital, Seoul, Korea
| | - Soojung Park
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea.
| | - Minseok Ko
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Do-Sun Lim
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Cheol Woong Yu
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Seong-Mi Park
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Mi-Na Kim
- Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea
| | - Yu-Whan Oh
- Department of Radiology, Korea University Anam Hospital, Seoul, Korea
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom
- Bioengineering Department and Imperial-X, Imperial College London, London, United Kingdom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
5
|
Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence. J Interv Cardiol 2022; 2022:5797431. [PMID: 35571991 PMCID: PMC9068333 DOI: 10.1155/2022/5797431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 03/29/2022] [Indexed: 11/20/2022] Open
Abstract
Background The number of multislice computed tomography (MSCT) analyses performed for planning structural heart interventions is rapidly increasing. Further automation is required to save time, increase standardization, and reduce the learning curve. Objective The purpose of this study was to investigate the feasibility of a fully automated artificial intelligence (AI)-based MSCT analysis for planning structural heart interventions, focusing on left atrial appendage occlusion (LAAO) as the selected use case. Methods Different deep learning models were trained, validated, and tested using a cohort of 583 patients for which manually annotated data were available. These models were used independently or in combination to detect the anatomical ostium, the landing zone, the mitral valve annulus, and the fossa ovalis and to segment the left atrium (LA) and left atrial appendage (LAA). The accuracy of the models was evaluated through comparison with the manually annotated data. Results The automated analysis was performed on 25 randomly selected patients of the test cohort. The results were compared to the manually identified landmarks. The predicted segmentation of the LA(A) was similar to the manual segmentation (dice score of 0.94 ± 0.02). The difference between the automatically predicted and manually measured perimeter-based diameter was −0.8 ± 1.3 mm (anatomical ostium), −1.0 ± 1.5 mm (Amulet landing zone), and −0.1 ± 1.3 mm (Watchman FLX landing zone), which is similar to the operator variability on these measurements. Finally, the detected mitral valve annulus and fossa ovalis were close to the manual detection of these landmarks, as shown by the Hausdorff distance (3.9 ± 1.2 mm and 4.8 ± 1.8 mm, respectively). The average runtime of the complete workflow, including data pre- and postprocessing, was 57.5 ± 34.5 seconds. Conclusions A fast and accurate AI-based workflow is proposed to automatically analyze MSCT images for planning LAAO. The approach, which can be easily extended toward other structural heart interventions, may help to handle the rapidly increasing volumes of patients.
Collapse
|
6
|
Abstract
PURPOSE OF REVIEW Artificial intelligence is the ability for machines to perform intelligent tasks. Artificial intelligence is already penetrating many aspects of medicine including cardiac surgery. Here, we offer a platform introduction to artificial intelligence for cardiac surgeons to understand the implementations of this transformative tool. RECENT FINDINGS Artificial intelligence has contributed greatly to the automation of cardiac imaging, including echocardiography, cardiac computed tomography, cardiac MRI and most recently, in radiomics. There are also several artificial intelligence based clinical prediction tools that predict complex outcomes after cardiac surgery. Waveform analysis, specifically, automated electrocardiogram analysis, has seen significant strides with promise in wearables and remote monitoring. Experimentally, artificial intelligence has also entered the operating room in the form of augmented reality and automated robotic surgery. SUMMARY Artificial intelligence has many potential exciting applications in cardiac surgery. It can streamline physician workload and help make medicine more human again by placing the physician back at the bedside. Here, we offer cardiac surgeons an introduction to this transformative tool so that they may actively participate in creating clinically relevant implementations to improve our practice.
Collapse
|
7
|
Silva B, Pessanha I, Correia-Pinto J, Fonseca JC, Queiros S. Automatic assessment of Pectus Excavatum severity from CT images using deep learning. IEEE J Biomed Health Inform 2021; 26:324-333. [PMID: 34152992 DOI: 10.1109/jbhi.2021.3090966] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Pectus excavatum (PE) is the most common abnormality of the thoracic cage, whose severity is evaluated by extracting three indices (Haller, correction and asymmetry) from computed tomography (CT) images. To date, this analysis is performed manually, which is tedious and prone to variability. In this paper, a fully automatic framework for PE severity quantification from CT images is proposed, comprising three steps: (1) identification of the sternue's greatest depression point; (2) detection of 8 anatomical keypoints relevant for severity assessment; and (3) measurements' geometric regularization and extraction. The first two steps rely on heatmap regression networks based on the Unet++ architecture, including a novel variant adapted to predict 1D confidence maps. The framework was evaluated on a database with 269 CTs. For comparative purposes, intra-observer, inter-observer and intra-patient variability of the estimated indices were analyzed in a subset of patients. The developed system showed a good agreement with the manual approach (a mean relative absolute error of 4.41%, 5.22% and 1.86% for the Haller, correction, and asymmetry indices, respectively), with limits of agreement comparable to the inter-observer variability. In the intrapatient analysis, the proposed framework outperformed the expert, showing a higher reproducibility between indices extracted from distinct CTs of the same patient. Overall, these results support the feasibility of the developed framework for the automatic, accurate and reproducible quantification of PE severity in a clinical context.
Collapse
|
8
|
Deep learning method for aortic root detection. Comput Biol Med 2021; 135:104533. [PMID: 34139438 DOI: 10.1016/j.compbiomed.2021.104533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 05/25/2021] [Accepted: 05/25/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. The approach is based on the use of deep learning techniques that attempt to mimic expert behavior. METHODS A total of 69 CTA scans (39 for training and 30 for validation) with different pathology types were selected to train the network. Furthermore, a total of 71 CTA scans were selected independently and applied as the test set to assess their performance. RESULTS The accuracy was evaluated by comparing the locations marked by the method with benchmark locations (which were manually marked by two experts). The interobserver error was 4.6 ± 2.3 mm. On an average, the differences between the locations marked by the two experts and those detected by the computer were 6.6 ± 3.0 mm and 6.8 ± 3.3 mm, respectively, when calculated using the test set. CONCLUSIONS From an analysis of these results, we can conclude that the proposed method based on pre-trained CNN models can accurately detect the aortic root in CTA images without prior segmentation.
Collapse
|
9
|
Dowling C, Gooley R, McCormick L, Firoozi S, Brecker SJ. Patient-specific Computer Simulation: An Emerging Technology for Guiding the Transcatheter Treatment of Patients with Bicuspid Aortic Valve. Interv Cardiol 2021; 16:e26. [PMID: 34721665 PMCID: PMC8419845 DOI: 10.15420/icr.2021.09] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/19/2021] [Indexed: 12/21/2022] Open
Abstract
Transcatheter aortic valve implantation (TAVI) is increasingly being used to treat younger, lower-risk patients, many of whom have bicuspid aortic valve (BAV). As TAVI begins to enter these younger patient cohorts, it is critical that clinical outcomes from TAVI in BAV are matched to those achieved by surgery. Therefore, the identification of patients who, on an anatomical basis, may not be suitable for TAVI, would be desirable. Furthermore, clinical outcomes of TAVI in BAV might be improved through improved transcatheter heart valve sizing and positioning. One potential solution to these challenges is patient-specific computer simulation. This review presents the methodology and clinical evidence surrounding patient-specific computer simulation of TAVI in BAV.
Collapse
Affiliation(s)
- Cameron Dowling
- MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash UniversityMelbourne, Australia
| | - Robert Gooley
- MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash UniversityMelbourne, Australia
| | - Liam McCormick
- MonashHeart, Monash Health and Monash Cardiovascular Research Centre, Monash UniversityMelbourne, Australia
| | - Sami Firoozi
- Cardiology Clinical Academic Group, St George’s, University of London and St George’s University Hospitals NHS Foundation TrustLondon, UK
| | - Stephen J Brecker
- Cardiology Clinical Academic Group, St George’s, University of London and St George’s University Hospitals NHS Foundation TrustLondon, UK
| |
Collapse
|