Purpose: To evaluate the learning curve of the simplified fluoroscopic biplanar (0-90º) puncture technique for percutaneous nephrolithotomy.
Methods: We prospectively evaluated patients with renal stones treated with percutaneous nephrolithotomy by a single institution's fellows employing the simplified bi-planar (0-90º) fluoroscopic puncture technique for renal access. The learning curve was assessed with the fluoroscopic screening time and the percutaneous renal puncture time. Data obtained were compared to a subset of patients operated by a senior surgeon.
Results: Eighty-nine patients were included in the study. Forty patients were operated by fellow-1, 39 by fellow-2, and 10 patients by the senior surgeon. Demographic data of all patients between groups were homogeneous, with no difference in gender (p = 0.432), age (p = 0.92), stone volume (p = 0.78), puncture laterality (p = 0.755), and body mass index (p = 0.365). The mean puncture time was 7.5, 4, and 3.1 min for fellow-1, fellow-2, and expert, respectively. The mean fluoroscopic screening time for the puncture was 10, 11, and 5.1 s for fellow-1, fellow-2, and the expert, respectively. Stone cases, both fellows needed to complete 10 procedures to match the senior surgeon in the mean puncture time (p = 0.046); meanwhile, the fluoroscopic screening time was equal even before to complete 10 procedures.
Conclusion: This study suggests that with the simplified biplanar (0-90º) puncture technique, the fluoroscopic screening time used in the learning process is brief. A novice fellow could require to complete ten cases to flatten the learning curve treating complex stone cases, and a flat learning curve is seen since the beginning when treating simple renal stones.
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http://dx.doi.org/10.1007/s00345-021-03669-7 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients.
View Article and Find Full Text PDFSci Rep
January 2025
Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Ultrasound, Chengdu Second People's Hospital, Chengdu 610000, China (X.L., X.Q.). Electronic address:
Rationale And Objectives: This study aims to develop a radiopathomics model based on preoperative ultrasound and fine-needle aspiration cytology (FNAC) images to enable accurate, non-invasive preoperative risk stratification for patients with papillary thyroid carcinoma (PTC). The model seeks to enhance clinical decision-making by optimizing preoperative treatment strategies.
Methods: A retrospective analysis was conducted on data from PTC patients who underwent thyroidectomy between October 2022 and May 2024 across six centers.
JMIR Res Protoc
January 2025
South African Medical Research Council/University of Johannesburg Pan African Centre for Epidemics Research Extramural Unit, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa.
Background: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing.
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