AIMC Topic: Aged

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State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators.

Neurosurgical review
The objective of this study was to develop and evaluate automated machine learning (aML) models for predicting short-term (1-month) and medium-term (3-month) functional outcomes [Modified Rankin Scale (mRS)] in patients suffering from poor-grade aneu...

Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study.

Journal of the Egyptian National Cancer Institute
BACKGROUND: Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models for differentiating between prostate biopsy outcomes. This study aims to develop a novel decision-su...

Real time artificial intelligence assisted carotid artery stenting: a preliminary experience.

Journal of neurointerventional surgery
BACKGROUND: Neurointerventionalists must pay close attention to multiple devices on multiple screens simultaneously, which can lead to oversights and complications. Artificial intelligence (AI) has potential application in recognizing and monitoring ...

Predictive modelling of knee osteoporosis.

BMC research notes
OBJECTIVE: The objective of this research was to develop a machine learning-based predictive model for osteoporosis screening using demographic and clinical data, including T-scores derived from calcaneus Quantitative Ultrasound (QUS). The study aime...

Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model.

Journal of Parkinson's disease
BackgroundFreezing of gait (FoG) is a complex, frequent, and disabling motor symptom of Parkinson's disease (PD). Wearable technology has the potential to improve FoG assessment by providing objective, quantitative, and continuous monitoring.Objectiv...

Defining lipedema's molecular hallmarks by multi-omics approach for disease prediction in women.

Metabolism: clinical and experimental
Lipedema is a chronic disease in females characterized by pathologic subcutaneous adipose tissue expansion and hitherto remains without druggable targets. In this observational study, we investigated the molecular hallmarks of lipedema using an unbia...

Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). Artificial intelligence (AI) could increase evidence-based treatment by generating additional treat...

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.

Computers in biology and medicine
Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threatening condition and a major cause of cancer-related mortality. Patients with neutropenic sepsis are...

Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.

Journal for immunotherapy of cancer
BACKGROUND: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial for tailoring patient care in esophageal squamous cell carcinoma (ESCC). This study aimed to develo...