AIMC Topic: Female

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Movement- and Posture-based Measures of Sedentary Patterns and Associations with Metabolic Syndrome in Hispanic/Latino and non-Hispanic Adults.

Journal of racial and ethnic health disparities
BACKGROUND: Sedentary behavior has been identified as a significant risk factor for Metabolic Syndrome (MetS). However, it is unclear if the sedentary pattern measurement approach (posture vs. movement) impacts observed associations or if association...

Real-world evaluation of RetCAD deep-learning system for the detection of referable diabetic retinopathy and age-related macular degeneration.

Clinical & experimental optometry
CLINICAL RELEVANCE: The challenges of establishing retinal screening programs in rural settings may be mitigated by the emergence of deep-learning systems for early disease detection.

Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Fully automatic analysis of myocardial perfusion cardiovascular magnetic resonance imaging datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning t...

Simulation training in mammography with AI-generated images: a multireader study.

European radiology
OBJECTIVES: The interpretation of mammograms requires many years of training and experience. Currently, training in mammography, like the rest of diagnostic radiology, is through institutional libraries, books, and experience accumulated over time. W...

Comparative Analysis of the Diagnostic Value of S-Detect Technology in Different Planes Versus the BI-RADS Classification for Breast Lesions.

Academic radiology
RATIONALE AND OBJECTIVES: S-Detect, a deep learning-based Computer-Aided Detection system, is recognized as an important tool for diagnosing breast lesions using ultrasound imaging. However, it may exhibit inconsistent findings across multiple imagin...

Revisiting the nature and strength of the personality-job performance relations: New insights from interpretable machine learning.

The Journal of applied psychology
Prior research on the relations between the five-factor model (FFM) of personality traits and job performance has suggested mixed findings: Some studies pointed to linear relations, while other studies revealed nonlinear relations. This study address...

Pupillometry as a biomarker of postural control: Deep-learning models reveal side-specific pupillary responses to increased intensity of balance tasks.

Psychophysiology
Pupillometry has been used in the studies of postural control to assess cognitive load during dual tasks, but its response to increased balance task intensity has not been investigated. Furthermore, it is unknown whether side-specific changes in pupi...

Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence.

Journal of affective disorders
BACKGROUND: Prevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predicting suicide attempts across different ages would enhance our understanding of how suicidal behaviour manifests in this period of rapid development. T...

Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

Journal of affective disorders
BACKGROUND: Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indica...

Deep learning radiomics based on ultrasound images for the assisted diagnosis of chronic kidney disease.

Nephrology (Carlton, Vic.)
AIM: This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images.