AIMC Topic: Retrospective Studies
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MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning.
Journal of X-ray science and technology
Nov 27, 2024
BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging c...
Clinical feasibility of a deep learning approach for conventional and synthetic diffusion-weighted imaging in breast cancer: Qualitative and quantitative analyses.
European journal of radiology
Nov 27, 2024
PURPOSE: In this study, we aimed to investigate the clinical feasibility of deep learning (DL)-based reconstruction applied to conventional diffusion-weighted imaging (cDWI) and synthetic diffusion-weighted imaging (sDWI) by comparing the DL reconstr...
Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis.
Cardiovascular and interventional radiology
Nov 27, 2024
PURPOSE: To predict survival and tumor recurrence following image-guided thermal ablation (IGTA) of lung tumors segmented using a deep learning approach.
Artificial intelligence measured 3D lumbosacral body composition and clinical outcomes in rectal cancer patients.
ANZ journal of surgery
Nov 27, 2024
INTRODUCTION: Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algorithms have allowed for easier acquisition of BC measurements, creating a comprehensive BC profile in ...
F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.
BMC cancer
Nov 27, 2024
BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in F-FDG PET/CT images.
Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction: insights from a retrospective cohort study.
Scientific reports
Nov 27, 2024
This study aims to develop physician support software for determining ovulation time and assess its impact on pregnancy outcomes in natural cycle frozen embryo transfers (NC-FET). To develop, assess, and validate an ovulation prediction model, three ...
Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study.
Global heart
Nov 27, 2024
BACKGROUND: Mortality due to ischemic heart disease (IHD) is heterogeneously distributed globally, and identifying the sites most affected by it is essential in developing strategies to mitigate the impact of the disease, despite the complexity resul...
[The critique of an artificial intelligence tool in the assessment of peripheral facial paralysis].
Annales de chirurgie plastique et esthetique
Nov 26, 2024
Peripheral facial palsy (PFP) is an alteration in the functioning of some facial muscles following an injury to the facial nerve. This pathology has functional and aesthetic consequences that impact the quality of life of patients. Their care is esse...
A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.
The Thoracic and cardiovascular surgeon
Nov 26, 2024
BACKGROUND: Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrat...
Neoplasm Invasiveness
Predictive Value of Tests
Middle Aged
Lung Neoplasms
Multiple Pulmonary Nodules
Solitary Pulmonary Nodule
Decision Support Techniques
Retrospective Studies
Tomography, X-Ray Computed
Aged
Female
Humans
Biomarkers, Tumor
Reproducibility of Results
Artificial Intelligence
Male
Risk Factors
Constructing small for gestational age prediction models: A retrospective machine learning study.
European journal of obstetrics, gynecology, and reproductive biology
Nov 26, 2024
OBJECTIVE: To develop machine learning prediction models for small for gestational age with baseline characteristics and biochemical tests of various pregnancy stages individually and collectively and compare predictive performance.
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