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A real-time approach for surgical activity recognition and prediction based on transformer models in robot-assisted surgery.
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning.
Bioinformatics
January 2025
Biocomputing Group, University of Bologna, Italy.
Motivation: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations.
Results: In this work we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations.
A Semi-Supervised Fracture-Attention Model for Segmenting Tubular Objects with Improved Topological Connectivity.
Bioinformatics
January 2025
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
Motivation: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets.
View Article and Find Full Text PDFBreakthroughs in deep tumour penetrating nano-phototheranostics for tumour ablation.
Clin Transl Med
January 2025
College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea.
Musculoskeletal differences in the craniomandibular region of children and adolescents with and without bruxism: A comparative study.
Cranio
January 2025
Department of Physiatry and Nursing, Universidad de Zaragoza, Zaragoza, Spain.
Objective: To analyse the relationship between bruxism and musculoskeletal parameters of the cervical and mandibular regions in children and adolescents.
Methods: A comparative cross-sectional study was performed with subjects from 6 to 16 years. Subjects were divided into bruxism (any type) and control groups.
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