AIMC Topic: Humans

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Deep Learning to Estimate Biological Age From Chest Radiographs.

JACC. Cardiovascular imaging
OBJECTIVES: The goal of this study was to assess whether a deep learning estimate of age from a chest radiograph image (CXR-Age) can predict longevity beyond chronological age.

Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network.

International journal of computer assisted radiology and surgery
PURPOSE: In quantitative computed tomography (CT), manual selection of the intensity calibration phantom's region of interest is necessary for calculating density (mg/cm) from the radiodensity values (Hounsfield units: HU). However, as this manual pr...

Semi-dilated convolutional neural networks for epileptic seizure prediction.

Neural networks : the official journal of the International Neural Network Society
Epilepsy is a neurological brain disorder that affects ∼75 million people worldwide. Predicting epileptic seizures holds great potential for improving the quality of life of people with epilepsy, but seizure prediction solely from the Electroencephal...

Volumetric quantitative measurement of hip effusions by manual versus automated artificial intelligence techniques: An OMERACT preliminary validation study.

Seminars in arthritis and rheumatism
OBJECTIVE: Preliminary assessment, via OMERACT filter, of manual and automated MRI hip effusion Volumetric Quantitative Measurement (VQM).

A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision.

World neurosurgery
OBJECTIVE: Computer vision (CV) is a subset of artificial intelligence that performs computations on image or video data, permitting the quantitative analysis of visual information. Common CV tasks that may be relevant to surgeons include image class...

Development of a deep learning-based image quality control system to detect and filter out ineligible slit-lamp images: A multicenter study.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Previous studies developed artificial intelligence (AI) diagnostic systems only using eligible slit-lamp images for detecting corneal diseases. However, images of ineligible quality (including poor-field, defocused, and poor...

A novel method for clinical risk prediction with low-quality data.

Artificial intelligence in medicine
In real-world data, predictive models for clinical risks (such as adverse drug reactions, hospital readmission, and chronic disease onset) are constantly struggling with low-quality issues, namely redundant and highly correlated features, extreme cat...

Machine Learning for Accurate Intraoperative Pediatric Middle Ear Effusion Diagnosis.

Pediatrics
OBJECTIVES: Misdiagnosis of acute and chronic otitis media in children can result in significant consequences from either undertreatment or overtreatment. Our objective was to develop and train an artificial intelligence algorithm to accurately predi...

Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation.

IEEE transactions on cybernetics
Automatic pancreas segmentation is crucial to the diagnostic assessment of diabetes or pancreatic cancer. However, the relatively small size of the pancreas in the upper body, as well as large variations of its location and shape in retroperitoneum, ...