Background: Many cardiac surgeons receive training for sternotomy-based cardiac surgical operations in residency programs and only a few education programs offer training specifically in minimally invasive cardiac surgery. In this report, we aimed to search and analyze the learning curve for robotic-assisted mitral valve (MV) repair in cardiac surgeons.
Method: Between January 2010 and July 2019, 60 robotic-assisted isolated MV repair surgeries were performed with DaVinci Robotic Systems in our center. Different kinds of surgical techniques were used. The assessment of the learning curve was based on cardiopulmonary bypass (CPB) and transthoracic aortic clamp (CC) times.
Result: There were 23 (38.3%) men and 37 (61.7%) women with a mean age of 48.3 years. The lesions of the MV were posterior leaflet prolapsus (n = 42, 70.0%), anterior leaflet prolapsus (n = 8, 13.3%), Barlow disease (n = 3, 5%), and annular dilatation (n = 7, 11.6%). The patients underwent notochordal implantation (n = 27, 45%), quadrangular or triangular resection (n = 23, 38.3%), isolated ring annuloplasty (n = 7, 11.7%), resection, and leaflet reduction (n = 2, 3.3%) or edge to edge repair (n = 1, 1.7%). The maturation of the learning curve appeared to be about 30 cases. The statistical analysis showed that the mean CPB and CC times for the first 30 cases were greater compared with the 30 after learning curve (155.3 vs. 118.9 min [p = .00], 102.3 vs. 80 min [p = .00], respectively). There was no case of conversion to open surgery. No perioperative mortality was observed.
Conclusion: The maturation of the learning curve for robotic-assisted MV repair appeared to be about 30 cases in our group of patients. This study had encouraging results for surgeons who desire to start a robotic mitral surgery program.
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http://dx.doi.org/10.1111/jocs.15281 | DOI Listing |
Surg Innov
January 2025
Division of General, Minimally Invasive, and Robotic Surgery, Department of Surgery, University of Illinois at Chicago, Chicago, IL, USA.
Background: Transabdominal pre-peritoneal inguinal hernia repair using the da Vinci Single-Port robot (SP-TAPP) is currently performed in few centers. We aimed to define the learning curve for SP-TAPP by analyzing operative times.
Methods: The operative times of 122 SP-TAPP performed between 2019 and 2024 were retrospectively analyzed.
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, Hunan, 410008, P.R. China.
Purpose: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pelvic lymph node dissection (ePLND) by preoperative nomograms.
Methods: [Ga]Ga-PSMA-617 PET/CT scan of 116 eligible PCa patients (82 in the training cohort and 34 in the test cohort) who underwent radical prostatectomy with ePLND were analyzed in our study. The Med3D deep learning network was utilized to extract discriminative features from the entire prostate volume of interest on the PET/CT images.
Sci Rep
January 2025
Imaging Department, Yantaishan Hospital, Yantai, China.
Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
Background: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.
Objective: This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications.
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