AIMC Topic: Middle Aged

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Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.

Journal of minimally invasive gynecology
OBJECTIVE: To evaluate the predictive value of clinical features in the diagnosis of endometriosis by utilizing machine learning algorithms (MLAs), aiming to develop an accurate, explainable prediction model.

Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6 months.

AIDS (London, England)
OBJECTIVE: The purpose of this study was to use machine learning models to predict the risk of hyperlipidemia in people with HIV (PWH) for 6 months after starting HAART, to improve early intervention efforts and prevent further progression to cardiov...

Improving Deep Learning-Based Grading of Partial-thickness Supraspinatus Tendon Tears with Guided Diffusion Augmentation.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a deep learning system with guided diffusion-based data augmentation for grading partial-thickness supraspinatus tendon (SST) tears and to compare its performance with experienced radiologists, includ...

The impact of artificial intelligence usage on employee moonlighting intention: A moderated mediation model.

Work (Reading, Mass.)
BackgroundIn recent years, the integration of artificial intelligence (AI) into the contemporary workplace has transformed the landscape of numerous industries. Despite its benefits, AI usage has also brought about significant controversies, particul...

Age and sex-specific differences of the intrafemoral and intratibial morphology using the Citak classification in patients undergoing total knee arthroplasty.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PURPOSE: Unlike established knee phenotype classifications, the recently introduced Citak classifications describe the intrafemoral and intratibial knee morphology. The aim of this study was to evaluate the distribution of Citak types A, B and C of t...

Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning (DL) models for accurate renal tumor characterization may benefit from multi-center datasets for improved generalizability; however, data-sharing constraints necessitate privacy-preserving solutions like federated learning (...

Decision support system based on ensemble models in distinguishing epilepsy types.

Epilepsy & behavior : E&B
This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EEG findings using artificial intelligence models. The study included 575 patients followed in the Neu...

External Validation of a CT-Based Radiogenomics Model for the Detection of EGFR Mutation in NSCLC and the Impact of Prevalence in Model Building by Using Synthetic Minority Over Sampling (SMOTE): Lessons Learned.

Academic radiology
RATIONALE AND OBJECTIVES: Radiogenomics holds promise in identifying molecular alterations in nonsmall cell lung cancer (NSCLC) using imaging features. Previously, we developed a radiogenomics model to predict epidermal growth factor receptor (EGFR) ...

Estimation of patient safety culture in private and public hospitals using machine learning methods.

Work (Reading, Mass.)
BackgroundPatient safety is a critical component of health care systems. Large groups of patients, as a result of medical errors, are at risk of harm. OBJECTIVE: This study evaluated the patient safety culture (PSC) between different work groups in b...