AIMC Topic: Female

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Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins.

Computer assisted surgery (Abingdon, England)
PURPOSE: Couinaud's liver segment classification has been widely adopted for liver surgery planning, yet its rigid anatomical boundaries often fail to align precisely with individual patient anatomy. This study proposes a novel patient-specific liver...

Local large arterial perivascular adipose tissue metabolic and anatomical features are associated with hypertensive clinical outcomes: a PET/CT-based study.

Annals of medicine
OBJECTIVE: This study investigated the relationship between anatomical and metabolic characteristics of large arterial perivascular adipose tissue (PVAT) and hypertensive clinical outcomes using positron emission tomography-computed tomography (PET/C...

Primary tumor resection: a new hope or an old illusion for patients with metastatic non-small cell lung neuroendocrine tumors?

World journal of surgical oncology
OBJECTIVES: This study aimed to investigate the impact of primary tumor resection (PTR) on survival outcomes for patients with metastatic non-small cell neuroendocrine tumors (mNSCLC-NETs), develop a predictive model to identify which patients may be...

A machine learning model including pentraxin-3 as predictor of outcomes in community-acquired pneumonia.

Journal of translational medicine
BACKGROUND: The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient ma...

Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.

BMC oral health
BACKGROUND: Estimating craniofacial patterns is essential for successful orthodontic treatment. However, conventional static measurements are inadequate for capturing dynamic changes, and manual cephalometric analysis is labor-intensive and requires ...

An MRI-based radiomics framework for early identification and progression stratification in knee osteoarthritis: data from the osteoarthritis initiative.

BMC musculoskeletal disorders
OBJECTIVES: To develop a cascaded machine learning model based on MRI radiomics features from cartilage and subchondral bone to predict the incidence and progression of knee osteoarthritis (KOA), thereby addressing the need for early intervention in ...

Factors associated with allergic diseases in Chinese children aged 6-14 years.

BMC public health
BACKGROUND AND OBJECTIVES: We aimed to identify and optimize contributing factors associated with allergic diseases by machine/deep learning algorithms among school-age children aged 6-14 years.

Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.

BMC cancer
PURPOSE: Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) s...

Characteristics of brain glucose metabolism in Parkinson's disease patients with freezing of gait: a study based on F-FDG PET imaging and deep learning.

BMC neurology
OBJECTIVE: Freezing of gait (FOG) is a common gait disorder in the advanced stages of Parkinson's disease (PD), closely associated with impaired balance and executive function. This study aimed to investigate specific changes in brain glucose metabol...

Application of interpretable machine learning to predict activities of daily living disability in sarcopenia: insights from the CHARLS dataset.

BMC geriatrics
PURPOSE: The decline in activities of daily living (ADL) among older persons is a significant public health concern. Sarcopenia is a major risk factor for ADL disability. This study aimed to develop and validate an interpretable machine learning (IML...