AIMC Topic: Humans

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Re-examining the association between region-specific pain recurrence and muscle force strategies in patients with patellofemoral pain via OpenSim and artificial intelligence: a prospective cohort study toward targeted rehabilitation.

Journal of neuroengineering and rehabilitation
BACKGROUND: This study utilized artificial intelligence (AI)-based machine learning algorithms, alongside the shapley additive explanations (SHAP) framework, to identify lower-limb muscle force patterns associated with recurrent patellofemoral pain (...

Explainable AI for infection prevention and control: modeling CPE acquisition and patient outcomes in an Irish hospital with transformers.

BMC medical informatics and decision making
BACKGROUND: Carbapenemase-Producing Enterobacteriace (CPE) poses a critical concern for infection prevention and control in hospitals. However, predictive modeling of previously highlighted CPE-associated risks such as readmission, mortality, and ext...

Disruption of low-frequency narrowband EEG microstate networks in Parkinson's disease with mild cognitive impairment.

Journal of neuroengineering and rehabilitation
BACKGROUND: Electroencephalogram (EEG) microstates provide insights into large-scale brain network coordination, revealing distinct neural dynamics within specific frequency bands associated with cognitive processes and neurological disorders. Critic...

Ischemic heart disease mortality due to fine particulate matter in Seoul between 2016 and 2020.

BMC public health
BACKGROUND: Ischemic heart disease (IHD) continues to rank among the leading global causes of mortality, consistently linked to long-term exposure to fine particulate matter (PM). Despite a declining trend in the annual average PM concentration in Se...

Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.

BMC ophthalmology
BACKGROUND: This study aimed to develop an artificial intelligence-based deep learning (DL) algorithm using near-infrared reflectance (NIR) images to differentiate between optic disc edema and pseudopapilledema, and to evaluate the diagnostic perform...

A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.

BMC cancer
PURPOSE: Breast cancer remains a leading cause of mortality in women worldwide, with notable disparities in incidence and prognosis across regions. This systematic review explores the application of Deep Learning-based computer-aided diagnostic (CAD)...

Deep multi-instance learning model based on gadoxetic acid-enhanced MRI for predicting microvascular invasion of hepatocellular carcinoma: a multicenter, retrospective study.

BMC cancer
OBJECTIVE: Microvascular invasion (MVI) is of great significance for the individualized treatment of hepatocellular carcinoma (HCC) and preoperative noninvasive prediction of MVI is still an urgent clinical problem. To explore the effects of differen...

Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling.

Scientific reports
Pathology diagnosis of colorectal cancer is time-consuming and requires a high level of expertise. However, it is an essential step towards establishing the adequate treatment. The need to analyse a large number of these histopathological images call...

Early detection of self-care impairments in children with disabilities using an enhanced SE network optimized by ISCO algorithm.

Scientific reports
Children with disabilities frequently encounter considerable obstacles in acquiring self-care skills, which are vigorous for developing their independence and overall quality of life. The early detection of self-care deficits is important for prompt ...