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A fast monocular 6D pose estimation method for textureless objects based on perceptual hashing and template matching.
Front Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
View Article and Find Full Text PDFImproving long-term postoperative survival in a porcine cardiac valve surgery model utilizing cardiopulmonary bypass via left thoracotomy: a single-center experience sharing insights.
Front Cardiovasc Med
January 2025
Department of Cardiovascular Surgery, Gaozhou People's Hospital, Gaozhou, Guangdong, China.
Objective: The objective of this study was to improve long-term postoperative survival in a porcine cardiac valve surgery model by utilizing cardiopulmonary bypass (CPB) via left thoracotomy. The study aimed to share refined techniques and insights accumulated over years at a single-center animal clinical trial facility.
Method: A total of 196 Chinese Large White pigs weighing between 60 and 75 kg were used in the study.
Anthropometric and metabolic parameters associated with visceral fat in non-obese type 2 diabetes individuals.
Diabetol Metab Syndr
January 2025
Department of Endocrinology, The Second People's Hospital of Yunnan Province, The Affiliated Hospital of Yunnan University, Kunming, Yunnan, 650021, China.
Background And Aim: Visceral fat (VF) was proved to be a more precise predictor of atherosclerotic cardiovascular disease (ASCVD) risk in individuals with type 2 diabetes mellitus (T2DM) than body mass index (BMI) itself. Even when the BMI was normal, visceral fat area (VFA) ≥ 90 cm² could raise the ten-year risk of developing ASCVD. Therefore, it was worth evaluating the association of influencing factors with high VF in non-obese T2DM individuals.
View Article and Find Full Text PDFArtificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.
EBioMedicine
January 2025
CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea.
Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.
Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation.
Image quality assessment and white matter hyperintensity quantification in two accelerated high-resolution 3D FLAIR techniques: Wave-CAIPI and deep learning-based SPACE.
Clin Radiol
December 2024
Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Aim: To compare the image quality obtained using two accelerated high-resolution 3D fluid-attenuated inversion recovery (FLAIR) techniques for the brain-deep learning-reconstruction SPACE (DL-SPACE) and Wave-CAIPI FLAIR.
Materials And Methods: A total of 123 participants who underwent DL-SPACE and Wave-CAIPI FLAIR brain imaging were retrospectively reviewed. In a qualitative analysis, two radiologists rated the quality of each image, including the overall image quality, artifacts, sharpness, fine-structure conspicuity, and lesion conspicuity based on Likert scales.
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