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Observer Variation

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Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.

PloS one
Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. S...

Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network.

Circulation. Cardiovascular imaging
BACKGROUND: Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Currently, late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. However, widespread adoption of atrial LGE ...

Diagnosis and risk stratification in hypertrophic cardiomyopathy using machine learning wall thickness measurement: a comparison with human test-retest performance.

The Lancet. Digital health
BACKGROUND: Left ventricular maximum wall thickness (MWT) is central to diagnosis and risk stratification of hypertrophic cardiomyopathy, but human measurement is prone to variability. We developed an automated machine learning algorithm for MWT meas...

Strategies to improve deep learning-based salivary gland segmentation.

Radiation oncology (London, England)
BACKGROUND: Deep learning-based delineation of organs-at-risk for radiotherapy purposes has been investigated to reduce the time-intensiveness and inter-/intra-observer variability associated with manual delineation. We systematically evaluated ways ...

Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Observation of the entire stomach during esophagogastroduodenoscopy (EGD) is important; however, there is a lack of effective evaluation tools.

A convolutional neural network-based anthropomorphic model observer for signal-known-statistically and background-known-statistically detection tasks.

Physics in medicine and biology
The purpose of this study is implementation of an anthropomorphic model observer using a convolutional neural network (CNN) for signal-known-statistically (SKS) and background-known-statistically (BKS) detection tasks. We conduct SKS/BKS detection ta...

Classification of femur trochanteric fracture: Evaluating the reliability of Tang classification.

Injury
INTRODUCTION: Given the drawbacks of a femoral intertrochanteric fracture classification based on 2-dimensional radiographic imaging, an artificial intelligence-based classification system- the Tang classification system-which uses 3-dimensional imag...

Ischemic Lesion Segmentation using Ensemble of Multi-Scale Region Aligned CNN.

Computer methods and programs in biomedicine
The first and foremost step in the diagnosis of ischemic stroke is the delineation of the lesion from radiological images for effective treatment planning. Manual delineation of the lesion by radiological experts is generally laborious and time-consu...