Background: To compare the results of a series of microendoscopic discectomies done at a government medical college in South India, with other published series and to analyze the learning curve for the procedure.
Methods: Microendoscopic discectomy (MED) was performed in 40 patients. The cases were in the age group between 20-50 years (mean age, 32.3 years). The period of study was 3 years. The most common level operated was L5-S1 (27 cases) followed by L4-L5 (12 cases). Double level disc herniation was observed in 1 patient, at L4-L5 and L5-S1. Patients with bilateral involvement and lumbar stenosis were excluded from the study. Diagnosis was based on clinical neurological examination, X-ray, CT and MRI. The MED was performed, following Destandau's procedure using Storz endoscopic microdiscectomy system. All patients were followed up regularly on 10 postoperative day, 1 month, 3 months and 1 year. Mean follow up of all patients were 14.1 months. The learning curve for the procedure was also analyzed.
Results: In our case series comprising of 40 cases, it was observed that as compared to other established studies, the mean operative duration, intraoperative blood loss, mean hospital stay and complication rate was largely reduced, with good experience and training. The outcome based upon modified Macnab criteria, showed that in maximum number of patients had excellent outcome and only 3 out of the 40 cases had poor outcome. Moreover, since the procedure was technically demanding, it took initial 20 cases to complete our learning curve and in the next 20 cases it was observed that we had improved our technique, operating time, blood loss, and outcome.
Conclusions: MED in properly trained hands is an excellent technique that could replace the conventional open surgery, in the management of lumbar disc disease if the learning curve could be overcome.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261768 | PMC |
http://dx.doi.org/10.21037/jss.2018.06.14 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu City, Sichuan Province, 610041, China.
Background: Pathological grade is a critical determinant of clinical outcomes and decision-making of follicular lymphoma (FL). This study aimed to develop a deep learning model as a digital biopsy for the non-invasive identification of FL grade.
Methods: This study retrospectively included 513 FL patients from five independent hospital centers, randomly divided into training, internal validation, and external validation cohorts.
Arch Gynecol Obstet
January 2025
Department of Obstetrics and Gynecology, Breast Cancer Center, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany.
Purpose: Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance of AI models is unclear.
View Article and Find Full Text PDFArch Gynecol Obstet
January 2025
Department of Radiology, First People's Hospital of Shangqiu, Shangqiu, 476000, China.
Objective: To assess and compare the diagnostic accuracy of radiologist, MR findings, and radiomics-clinical models in the diagnosis of placental implantation disorders.
Methods: Retrospective collection of MR images from patients suspected of having placenta accreta spectrum (PAS) was conducted across three institutions: Institution I (n = 505), Institution II (n = 67), and Institution III (n = 58). Data from Institution I were utilized to form a training set, while data from Institutions II and III served as an external test set.
Front Neurol
January 2025
Department of Neurology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Objective: To develop and validate an explainable machine learning (ML) model predicting the risk of hemorrhagic transformation (HT) after intravenous thrombolysis.
Methods: We retrospectively enrolled patients who received intravenous tissue plasminogen activator (IV-tPA) thrombolysis within 4.5 h after symptom onset to form the original modeling cohort.
J Anus Rectum Colon
January 2025
Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
Objectives: This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients.
Methods: We included 150 CRLM patients who underwent first-line doublet chemotherapy, dividing them into a training cohort (n=112) and a test cohort (n=38). We manually delineated three-dimensional tumor volumes, selecting the largest liver metastasis for measurement, using pretreatment portal-phase CT images and extracted 107 radiomics features.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!