Percutaneous Mitral Valve Repair with the MitraClip System in the Current Clinical Practice

Sorrentino, Sergio; Berardini, Alessandra; Statuto, Giovanni; Angeletti, Andrea; Massaro, Giulia; Capobianco, Claudio; Piemontese, Giuseppe Pio; Spadotto, Alberto; Toniolo, Sebastiano; Caponetti, Angelo Giuseppe; Ditaranto, Raffaello; Parisi, Vanda; Minnucci, Matteo; Ferrara, Valentina; Galiè, Nazzareno; Biagini, Elena

doi:10.3390/hearts2010007

Cited by 2 publications

(2 citation statements)

References 54 publications

(67 reference statements)

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“…Moreover, percutaneous approaches for MV repair have emerged as a valid alternative to openchest MV surgery. Among the available approaches, transcatheter edge-to-edge repair (TEER) is the most widespread one and is supported by the strongest evidence [13]. According to the guidelines of the European Society of Cardiology (ESC) [14] and the American Heart Association (AHA) [15], transthoracic echocardiography (TTE) is the standard imaging modality to diagnose and quantify MR, while transesophageal echocardiography (TEE) is used for morphologic assessment of pathologic MV and to guide MV procedures.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography

Munafò,

Saitta,

Ingallina

et al. 2024

IEEE Access

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Three-dimensional transesophageal echocardiography (3DTEE) is the recommended imaging technique for the assessment of mitral valve (MV) morphology and lesions in case of mitral regurgitation (MR) requiring surgical or transcatheter repair. Such assessment is key to thorough intervention planning and to intraprocedural guidance. However, it requires segmentation from 3DTEE images, which is timeconsuming, operator-dependent, and often merely qualitative. In the present work, a novel workflow to quantify the patient-specific MV geometry from 3DTEE is proposed. The developed approach relies on a 3D multi-decoder residual convolutional neural network (CNN) with a U-Net architecture for multi-class segmentation of MV annulus and leaflets. The CNN was trained and tested on a dataset comprising 55 3DTEE examinations of MR-affected patients. After training, the CNN is embedded into a fully automatic, and hence fully repeatable, pipeline that refines the predicted segmentation, detects MV anatomical landmarks and quantifies MV morphology. The trained 3D CNN achieves an average Dice score of 0.82 ± 0.06, mean surface distance of 0.95 ± 0.27 mm and 95% Hausdorff Distance (HD) of 3.57 ± 1.56 mm before segmentation refinement, outperforming a state-of-the-art baseline residual U-Net architecture, and provides an unprecedented multi-class segmentation of the annulus, anterior and posterior leaflet. The automatic 3D linear morphological measurements of the annulus and leaflets, specifically diameters and lengths, exhibit differences of less than 1.45 mm when compared to ground truth values. These measurements also demonstrate strong overall agreement with analyses conducted by semi-automated commercial software. The whole process requires minimal user interaction and requires approximately 15 seconds.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography

Munafò,

Saitta,

Ingallina

et al. 2024

IEEE Access

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“…Recently, atrial functional mitral regurgitation (aFMR) has been increasingly recognized and is characterized by severe mitral annular dilatation, insufficient leaflet remodeling and decreased annular contractility in patients with longstanding atrial fibrillation (AF) [5]. Clinically, different treatment methods are adopted for different types of MR [6,7].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Echocardiography Based on Deep Learning Model

Huang

Wang

et al. 2022

Electronics

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In order to achieve the classification of mitral regurgitation, a deep learning network VDS-UNET was designed to automatically segment the critical regions of echocardiography with three sections of apical two-chamber, apical three-chamber, and apical four-chamber. First, an expert-labeled dataset of 153 echocardiographic videos and 2183 images from 49 subjects was constructed. Then, the convolution layer in the VGG16 network was used to replace the contraction path in the original UNet network to extract image features, and depth supervision was added to the expansion path to achieve the segmentation of LA, LV, and MV. The results showed that the Dice coefficients of LA, LV, and MV were 0.935, 0.915, and 0.757, respectively. The proposed deep learning network can achieve simultaneous and accurate segmentation of LA, LV, and MV in multi-section echocardiography, laying a foundation for quantitative measurement of clinical parameters related to mitral regurgitation.

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Percutaneous Mitral Valve Repair with the MitraClip System in the Current Clinical Practice

Cited by 2 publications

References 54 publications

A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography

A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography

Segmentation of Echocardiography Based on Deep Learning Model

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