Backgroud: The learning curve and midterm results of aortoiliac occlusive disease (AIOD) revascularization by robot-assisted laparoscopic (RAL) surgery may be known.
Methods: A prospective single-center study was conducted in the vascular surgery department of Georges Pompidou European Hospital (Paris, France). Patients with AIOD treated by RAL from February 2014 to February 2019 were included. Demographic characteristics, past medical history, Trans-Atlantic Inter-Society Consensus (TASC) lesions classifications, mortality, primary and secondary patency, as well as complication rates were collected. Safety was analyzed by the cumulative sum control chart method with a conversion rate of 10%, operative time by cumulative average-time model, and primary and secondary patency by the Kaplan-Meier method.
Results: Seventy patients were included, 18 (25.7%) with TASC C lesions and 52 (74.3%) with TASC D lesions. Before discharge, 14 (24.3%) patients had surgical complications. Among them, 10 (14.3%) required at least one reintervention. One (1.4%) patient died during the hospitalization. The learning curve in terms of safety (conversion rate) was 13 cases with an operating time of 220 minutes after 35 patients. During follow-up (median 37 months [21; 49]), 63 patients (91.3%) improved their symptoms, 53 (76.8%) became asymptomatic, and 3 graft limb occlusions occurred. The primary patency at 12, 24, 36, and 48 months was 94%, 92%, 92%, and 92%, respectively, while the secondary patency for the same intervals was 100%, 98.1%, 98.1%, and 98.1%, respectively.
Conclusions: Robotic surgery in AIOD revascularization seems safe and effective; allowing to treat patients with few comorbidities and severe lesions, in a dedicated center experienced in RAL, with excellent patency. Prospective clinical trials should be performed to confirm safety.
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http://dx.doi.org/10.1016/j.avsg.2024.02.018 | 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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