Background: Robotic-assisted peripheral nerve surgery (RASPN) has emerged as a promising advancement in microsurgery, offering enhanced precision and tremor reduction for nerve coaptations. This study investigated the largest published patient collective in RASPN and provided specific technical aspects, operative setups, and a learning curve.
Methods: Data collection involved creating a prospective database that recorded surgical details such as surgery type, duration, nerve coaptation time, and number of stitches. The experienced surgeon first underwent a 12-hour training program utilizing the Symani robot system in combination with optical magnification tools before using the system clinically.
Results: The study included 19 patients who underwent robot-assisted peripheral nerve reconstruction. The cohort included six men (31.6%) and 13 women (68.4%), with an average age of 53.8 ± 18.4 years. The procedures included nerve transfers, targeted muscle reinnervation, neurotized free flaps, and autologous nerve grafts. Learning curve analysis revealed no significant reduction in time per stitch over the initial nine coaptations (4.9 ± 0.5 min) compared with the last 10 coaptations (5.5 ± 1.5 min).
Conclusions: The learning curve for RASPN was compared with early experiences with other surgical robots, emphasizing the importance of surgical proficiency and assistant training. Obstacles such as instrument grip strength and blood clot formation were highlighted, and suggestions for future advancements were proposed. RASPN presents an exciting opportunity to enhance precision; however, ongoing research and optimization are necessary to fully harness its benefits.
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http://dx.doi.org/10.1097/GOX.0000000000006221 | DOI Listing |
BMC Oral Health
January 2025
Department of Stomatology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan, 528308, Guangdong, China.
Background: A comprehensive analysis of the occlusal plane (OP) inclination in predicting anteroposterior mandibular position (APMP) changes is still lacking. This study aimed to analyse the relationships between inclinations of different OPs and APMP metrics and explore the feasibility of OP inclination in predicting changes in APMP.
Methods: Overall, 115 three-dimensional (3D) models were reconstructed using deep learning-based cone-beam computed tomography (CBCT) segmentation, and their accuracy in supporting cusps was compared with that of intraoral scanning models.
BMC Infect Dis
January 2025
Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia.
Background: Early diagnosis of syphilis is vital for its effective control. This study aimed to develop an Artificial Intelligence (AI) diagnostic model based on radiomics technology to distinguish early syphilis from other clinical skin lesions.
Methods: The study collected 260 images of skin lesions caused by various skin infections, including 115 syphilis and 145 other infection types.
The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals.
View Article and Find Full Text PDFDig Dis Sci
January 2025
Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, USA.
Background: Eosinophilic esophagitis (EoE) is an increasingly common cause of food impaction.
Aims: This study aims to provide a nationwide analysis of food impaction in patients with or without EoE diagnosis, concentrating on patient demographics, interventions, outcomes, and development of predictive machine-learning models.
Methods: A retrospective assessment was conducted using Nationwide Emergency Department Sample data from January 1, 2018, to December 31, 2019.
J Cardiothorac Vasc Anesth
December 2024
Surgical Anesthesia Center, The First People's Hospital of Longquanyi District, Chengdu, China. Electronic address:
Background: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict the mortality of AHF patients.
Methods: The Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database was used to perform a retrospective study.
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