Study Design: Retrospective review of prospectively collected data.
Objective: To describe the learning curve for percutaneous transforaminal endoscopic discectomy (PTED) and demonstrate its efficacy in treating lumbar disc herniation.
Summary Of Background Data: The learning curve for PTED has not yet been standardized in the literature.
Patients And Methods: Consecutive patients who underwent lumbar PTED by a single surgeon between December 2020 and 2022 were included. Cumulative sum analysis was applied to operative and fluoroscopy time to assess the learning curve. Inflection points were used to divide cases into early and late phases. The 2 phases were analyzed for differences in operative and fluoroscopy time, length of stay, complications, and patient-reported outcome measures (PROMs). Patient characteristics and operative levels were also compared. PROMs entailed the Oswestry Disability Index, Patient-Reported Outcomes Measurement Information System, Visual Analog Scale Back/Leg, and 12-item Short Form Survey at preoperative, early postoperative (<6 mo), and late postoperative (≥6 mo) time points. PROMs between PTED cases and a comparable cohort of tubular microdiscectomy cases, performed by the same surgeon, were compared.
Results: Fifty-five patients were included. Cumulative sum analysis indicated that both operative and fluoroscopy time diminished rapidly after case 31, suggesting a learning curve of 31 cases (early phase: n = 31; late phase: n = 24). Late-phase cases exhibited significantly lower operative times (85.7 vs . 62.2 min, P = 0.001) and fluoroscopy times (131.0 vs . 97.2 s, P = 0.001) compared with the early-phase cases. Both early and late-phase cases showed significant improvement in all PROMs. There were no differences in PROMs between the patients who underwent PTED and tubular microdiscectomy.
Conclusion: The PTED learning curve was found to be 31 cases and did not impact PROMs or complication rates. Although this learning curve reflects the experiences of a single surgeon and may not be broadly applicable, PTED can serve as an effective modality for the treatment of lumbar disc herniation.
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http://dx.doi.org/10.1097/BRS.0000000000004730 | 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.
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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.
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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.
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January 2025
Department of Radiology, Xinhua Hospital, Shanghai Jiaotong University Medical School, Shanghai 200092, China (Z.H.W., Y.Q.M., X.Y.W., N.X.Y., X.Y.W., G.R.). Electronic address:
Rationale And Objectives: The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on computed tomography (CT). Additionally, the study evaluates the robustness of the proposed model.
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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.
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