AIMC Topic: Aged

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Dynamic readmission prediction using routine postoperative laboratory results after radical cystectomy.

Urologic oncology
OBJECTIVE: To determine if the addition of electronic health record data enables better risk stratification and readmission prediction after radical cystectomy. Despite efforts to reduce their frequency and severity, complications and readmissions fo...

Radical cystectomy in women: Impact of the robot-assisted versus open approach on surgical outcomes.

Urologic oncology
OBJECTIVES: To perform a comparison of complications following open versus robot-assisted radical cystectomy (RC) among women who undergo the procedure. Studies comparing robotic to open RC have been mixed without a clear delineation of which patient...

Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learning algorithm through comparing 3 convolutional neural networks (CNNs).

Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.

European journal of radiology
PURPOSE: To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary phase (HBP) adequacy of gadoxetate disodium (EOB)-enhanced MRI. Secondarily, we explored the potential ...

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy.

Journal of cardiovascular computed tomography
BACKGROUND: Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruc...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment.

Journal of visualized experiments : JoVE
Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effect...

Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system.

World journal of urology
PURPOSE: The delivery of precision medicine is a primary objective for both clinical and translational investigators. Patients with newly diagnosed prostate cancer (PCa) face the challenge of deciding among multiple initial treatment modalities. The ...

Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Our purpose was to assess the performance of full-dose (FD) PET image synthesis in both image and sinogram space from low-dose (LD) PET images and sinograms without sacrificing diagnostic quality using deep learning techniques. Clinical brain PET/CT...

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals.

PloS one
Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our ob...