AIMC Topic: Middle Aged

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Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, a...

Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans Following Omega-3 Fatty Acid Supplementation.

Nutrients
Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompted uncertainty about their cardioprotective effects. While the VITAL trial found no overall cardiovascular benefit from n-3 HUFA supplementation, its s...

Individualized prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with ≥ 3 positive sentinel lymph nodes based on machine-learning algorithms.

BMC cancer
BACKGROUND: Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without a...

Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA.

Scientific reports
Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and treated early enough. Optical coherence tomography angiography (OCTA) can non-invasively provide valua...

Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.

Nature communications
Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists to interpret. Here, we introduce a deep learning model incorporating co-plane attention across imag...

Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: To predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features and clinical data from pelvic magnetic resonance imaging (MRI).

Prediction of delayed breastfeeding initiation among mothers having children less than 2 months of age in East Africa: application of machine learning algorithms.

Frontiers in public health
BACKGROUND: Delayed breastfeeding initiation is a significant public health concern, and reducing the proportion of delayed breastfeeding initiation in East Africa is a key strategy for lowering the Child Mortality rate. However, there is limited evi...

Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram.

Journal of electrocardiology
INTRODUCTION: Deep learning (DL) models offer improved performance in electrocardiogram (ECG)-based classification over rule-based methods. However, for widespread adoption by clinicians, explainability methods, like saliency maps, are essential.

Grading facial aging: Comparing the clinical assessments made by three dermatologists with those obtained by an AI-based scoring system that analyses selfie pictures. A focus on Chinese subjects of both genders.

International journal of cosmetic science
OBJECTIVE: The objective of this study is to assess the correspondence, in live conditions, between clinical gradings of facial aging signs by three dermatologists and those afforded by an automatic AI-based algorithm that analyses smartphones' selfi...

Predicting Response to Neuromodulators or Prokinetics in Patients With Suspected Gastroparesis Using Machine Learning: The "BMI, Infectious Prodrome, Delayed GES, and No Diabetes" Model.

Clinical and translational gastroenterology
INTRODUCTION: Pharmacologic therapies for symptoms of gastroparesis (GP) have limited efficacy, and it is difficult to predict which patients will respond. In this study, we implemented a machine learning model to predict the response to prokinetics ...