Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Objective: Transventricular beating-heart mitral valve repair (TBMVR) with artificial chordae implantation is a technique to treat mitral valve prolapse. Two-dimensional (2D) echocardiography completed with simultaneous biplane view during surgeon finger pushing on the left ventricular (LV) wall (finger test [FT]) is currently used to localize the desired LV access, on the inferior-lateral wall, between the papillary muscles (PMs). We aimed to compare a new three-dimensional (3D) method with conventional FT in terms of safety and better localization of LV access.

Methods: During TBMVR, conventional FT was completed using 3D transesophageal echocardiography by placing the sample box in the bicommissural view of the LV, including the PMs and the apex. The 3D volume was subsequently edited to visualize the LV from above (surgical view) to localize the bulge of the operator's finger pushing on the LV. We asked the first operator, the second operator, and the cardiac surgery fellow, separately, to evaluate the location of their finger pushing, both with the 2D method and the 3D method, to estimate the interoperator concordance.

Results: From 2019 to 2021, 42 TBMVRs were performed without complications related to access using FT completed with the 3D method. Regarding the choice of the right and safe entry site, the operator's agreement was higher using 3D rendering compared with conventional FT (mean agreement 0.59 ± 0.29 for 2D vs 0.83 ± 0.20 for 3D), while full operator agreement was 10 of 42 for 2D and 23 of 42 for 3D ( = 0.004).

Conclusions: Three-dimensional FT is easy to perform and facilitates surgeons choosing the best access for TBMVR in term of anatomical localization and safety.

Download full-text PDF

Source
http://dx.doi.org/10.1177/15569845231185346DOI Listing

Publication Analysis

Top Keywords

finger pushing
12
finger test
8
best access
8
mitral valve
8
method
5
three-dimensional finger
4
test echocardiographic
4
echocardiographic method
4
method locate
4
locate best
4

Similar Publications

Intraneural vascular anomalies are rarely encountered specimens as these are not commonly resected. To the best of our knowledge, this is the first report of the histologic findings in an arteriovenous malformation (AVM) within a digital nerve. We report a rare case of an 18-year-old man with a painful mass in the left hand and middle finger who was referred to our hospital for a treatment strategy consultation.

View Article and Find Full Text PDF

The paper introduces Professor 's clinical experience in treatment of Parkinson's disease with constipation by the combined therapy of acupuncture and . Professor believes that constipation in Parkinson's disease involves pathological changes in (triple energizers) system, i.e.

View Article and Find Full Text PDF

Emergency medical service (EMS) plays a vital role in the healthcare system by delivering rapid response and acute care in critical situations. However, limited information exists regarding the prevalence and associated factors of work-related musculoskeletal disorder (WMSD) symptoms amongst EMS workers. This study aimed to examine the prevalence of WMSD symptoms and identify associated risk factors through a cross-sectional survey conducted in Hong Kong.

View Article and Find Full Text PDF

When observing individuals in action, we often infer their goals and intentions. Yet, in situations where actions are ambiguous and could be either intentionally generated or not, there is a tendency to perceive these actions as internally driven. This intentionality bias is influenced by individual differences in schizotypal cognitive style.

View Article and Find Full Text PDF

Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold.

Sensors (Basel)

April 2025

Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.

Currently, finger vein recognition (FVR) stands as a pioneering biometric technology, with convolutional neural networks (CNNs) and Transformers, among other advanced deep neural networks (DNNs), consistently pushing the boundaries of recognition accuracy. Nevertheless, these DNNs are inherently characterized by static, continuous-valued neuron activations, necessitating intricate network architectures and extensive parameter training to enhance performance. To address these challenges, we introduce an adaptive firing threshold-based spiking neural network (ATSNN) for FVR.

View Article and Find Full Text PDF