AIMC Topic: Middle Aged

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Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence.

The Knee
BACKGROUND: This study proposed an automatic surgical planning system for high tibial osteotomy (HTO) using deep learning-based artificial intelligence and validated its accuracy. The system simulates osteotomy and measures lower-limb alignment param...

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks.

Sensors (Basel, Switzerland)
Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of p...

Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study.

Magnetic resonance imaging
PURPOSE: To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC).

Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex.

Journal of affective disorders
Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical asse...

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.

Ultraschall in der Medizin (Stuttgart, Germany : 1980)
PURPOSE: To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis.

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay.

Breast cancer research and treatment
PURPOSE: To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth ...

Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging.

Neuroradiology
PURPOSE: Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous studies have primarily or exclusively relied on anterior circulation stroke cases to build machine learning (ML) models for predicting onset time. To d...

Predicting the conversion from clinically isolated syndrome to multiple sclerosis: An explainable machine learning approach.

Multiple sclerosis and related disorders
INTRODUCTION: Predicting the conversion of clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) is critical to personalizing treatment planning and benefits for patients. The aim of this study is to develop an explainab...

Predicting post-treatment symptom severity for adults receiving psychological therapy in routine care for generalised anxiety disorder: a machine learning approach.

Psychiatry research
Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist to inform individuals or clinicians of potential treatment benefits. This study aimed to develop and...

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

International immunopharmacology
BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-c...