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Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.
Eur Spine J
January 2025
Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Purpose: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing it with X-ray or CT scans during the diagnostic work-up. However, manual grading of stenosis is time-consuming and induces inter-reader variability as a standardized grading system is lacking.
View Article and Find Full Text PDFMultiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.
Eur Radiol
January 2025
Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Objectives: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.
Methods: The MRI features were extracted for molecular subgroups by a novel multi-parameter convolutional neural network (CNN) called Bi-ResNet-MB. Then, MR features were used to establish a prognosis model based on XGBoost.
The impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study.
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 PDFDevelopment and Validation of Machine Learning Models for Risk Prediction of Postpartum Stress Urinary Incontinence: A Prospective Observational Study.
Int Urogynecol J
January 2025
School of Nursing, Binzhou Medical University, Bincheng District, No. 522, Huanghe Third Road, Binzhou, Shandong, China.
Introduction And Hypothesis: This study aims to develop a postpartum stress urinary incontinence (PPSUI) risk prediction model based on an updated definition of PPSUI, using machine learning algorithms. The goal is to identify the best model for early clinical screening to improve screening accuracy and optimize clinical management strategies.
Methods: This prospective study collected data from 1208 postpartum women, with the dataset randomly divided into training and testing sets (8:2).
The Correlation Between Gender, Age, Curvature, and Symptom-related Changes in C6 and C7 Slope in 10,000 Subjects.
Spine (Phila Pa 1976)
January 2025
school of Life Sciences, Beijing University of Chinese Medicine, Beijing, P.R. China.
Study Design: A cross-sectional analysis of 10,000 cervical spine X-rays.
Objective: This study investigates the variations in C6S and C7S across demographic factors (gender, age, cervical curvature, symptoms) and explores their correlation. Additionally, machine learning models are applied to improve the accuracy of C7S prediction.
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