In this paper, to harness the possibility of real-time guidance of MRI, a robotic system has been developed to perform transrectal prostate biopsy inside a 1.5-T closed bore scanner. A specially developed MR pulse sequence is capable of tracking the needle location in real time while dynamically updating the scan planes to always include the needle and target.
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http://dx.doi.org/10.1109/MEMB.2009.935709 | DOI Listing |
Strahlenther Onkol
January 2025
Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland.
Purpose: This study aimed to evaluate the prognostic significance of magnetic resonance imaging (MRI) parameters on biochemical failure-free survival (BFS) in patients diagnosed with intermediate-risk prostate cancer and treated with robotic ultrahypofractionated stereotactic body radiotherapy (SBRT) without androgen deprivation therapy (ADT).
Methods: A retrospective analysis was conducted in patients with intermediate-risk prostate cancer undergoing robotic SBRT delivered in five fractions with a total radiation dose of 35-36.25 Gy.
NPJ Digit Med
January 2025
Department of Plastic and Reconstructive Surgery, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea.
System-level wearable electronics require to be flexible to ensure conformal contact with the skin, but they also need to integrate rigid and bulky functional components to achieve system-level functionality. As one of integration methods, folding integration offers simplified processing and enhanced functionality through rigid-soft region separation, but so far, it has mainly been applied to modality of electrical sensing and stimulation. This paper introduces a vialess heterogeneous skin patch with multi modalities that separates the soft region and strain-robust region through folded structure.
View Article and Find Full Text PDFEur Psychiatry
January 2025
Department of Child and Adolescent Psychopathology, Amiens University Hospital, Amiens, France.
Neural Netw
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, South China University of Technology, Guangzhou, 510640, China; Institute for Super Robotics (Huangpu), Guangzhou, 510555, China; Nanchang University, Nanchang, 330031, China; College of Computer Science and Engineering, Jishou University, Jishou, 416000, China; Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, 510335, China; School of Electronical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China; School of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China; Institute of Artificial Intelligence and Automation, Guangdong University of Petrochemical Technology, Maoming, 525000, China. Electronic address:
To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is proposed to realize multi-classification of transformer faults based on dissolved gas data. Then, a swarm budorcas taxicolor optimization (SBTO) algorithm is proposed to iteratively search the optimal model parameters during MSV model training, so as to obtain the most effective transformer fault diagnosis model.
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