AIMC Topic: Humans

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Interstitial Lung Abnormalities at CT in the Korean National Lung Cancer Screening Program: Prevalence and Deep Learning-based Texture Analysis.

Radiology
Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quantitatively assessed. Purpose To determine the prevalence of ILA at CT examinations from the Korean N...

Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.

Scientific reports
Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalogr...

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy.

International journal of molecular sciences
In this proof-of-principle study, we systematically studied the potential of Raman spectroscopy for detecting pre-analytical delays in blood serum samples. Spectra from 330 samples from a liver cirrhosis cohort were acquired over the course of eight ...

Artificial Intelligence-Based Ethical Hacking for Health Information Systems: Simulation Study.

Journal of medical Internet research
BACKGROUND: Health information systems (HISs) are continuously targeted by hackers, who aim to bring down critical health infrastructure. This study was motivated by recent attacks on health care organizations that have resulted in the compromise of ...

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

Physics in medicine and biology
. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor volume segmentation method is warranted for H&N cancer therapeutic management. The purpose of this ...

The potential for clinical application of automatic quantification of olfactory bulb volume in MRI scans using convolutional neural networks.

NeuroImage. Clinical
The olfactory bulbs (OBs) play a key role in olfactory processing; their volume is important for diagnosis, prognosis and treatment of patients with olfactory loss. Until now, measurements of OB volumes have been limited to quantification of manually...

Quantum spin models for numerosity perception.

PloS one
Humans share with animals, both vertebrates and invertebrates, the capacity to sense the number of items in their environment already at birth. The pervasiveness of this skill across the animal kingdom suggests that it should emerge in very simple po...

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

The Journal of physiology
Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods s...

FAST skill assessment from kinematics data using convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: FAST is a point of care ultrasound study that evaluates for the presence of free fluid, typically hemoperitoneum in trauma patients. FAST is an essential skill for Emergency Physicians. Thus, it requires objective evaluation tools that can r...