AIMC Topic: Female

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Data-driven prediction of continuous renal replacement therapy survival.

Nature communications
Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty whether they wi...

Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.

Journal of neural engineering
Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended spe...

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

Frontiers in immunology
BACKGROUND: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outc...

Non-destructive prediction of fertility and sex in chicken eggs using the short wave near-infrared region.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The objective of this study was to evaluate the ability of a handheld near-infrared device (900-1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and ...

Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: the role of human/artificial intelligence interaction.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Computer-aided diagnosis (CADx) for the optical diagnosis of colorectal polyps is thoroughly investigated. However, studies on human-artificial intelligence interaction are lacking. Our aim was to investigate endoscopists' trust ...

A Comparative Study of a Nomogram and Machine Learning Models in Predicting Early Hematoma Expansion in Hypertensive Intracerebral Hemorrhage.

Academic radiology
RATIONALE AND OBJECTIVES: Early identification for hematoma expansion can help improve patient outcomes. Presently, there are many methods to predict hematoma expansion. This study compared a variety of models to find a model suitable for clinical pr...

Artificial Intelligence for Otosclerosis Detection: A Pilot Study.

Journal of imaging informatics in medicine
The gold standard for otosclerosis diagnosis, aside from surgery, is high-resolution temporal bone computed tomography (TBCT), but it can be compromised by the small size of the lesions. Many artificial intelligence (AI) algorithms exist, but they ar...

Deep Learning for Describing Breast Ultrasound Images with BI-RADS Terms.

Journal of imaging informatics in medicine
Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this pr...

Predicting lower limb lymphedema after cervical cancer surgery using artificial neural network and decision tree models.

European journal of oncology nursing : the official journal of European Oncology Nursing Society
PURPOSE: This study aimed to develop and validate accessible artificial neural network and decision tree models to predict the risk of lower limb lymphedema after cervical cancer surgery.

Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.

NeuroImage
BACKGROUND: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investi...