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Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures.

Scientific reports
Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and they are still evolving. However, there are few studies comparing these architectures in the field of...

Multi-modular AI Approach to Streamline Autism Diagnosis in Young Children.

Scientific reports
Autism has become a pressing healthcare challenge. The instruments used to aid diagnosis are time and labor expensive and require trained clinicians to administer, leading to long wait times for at-risk children. We present a multi-modular, machine l...

Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients.

BMC oral health
BACKGROUND: Artificial intelligence (AI) is a branch of computer science concerned with building smart software or machines capable of performing tasks that typically require human intelligence. We present a protocol for the use of AI to fabricate im...

Deep learning approaches for sleep disorder prediction in an asthma cohort.

The Journal of asthma : official journal of the Association for the Care of Asthma
OBJECTIVE: Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined 1 million samples included in the Taiwan National Health Insurance ...

Prediction of admission in pediatric emergency department with deep neural networks and triage textual data.

Neural networks : the official journal of the International Neural Network Society
Emergency department (ED) overcrowding is a global condition that severely worsens attention to patients, increases clinical risks and affects hospital cost management. A correct and early prediction of ED's admission is of high value and a motivatio...

Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

JACC. Cardiovascular imaging
OBJECTIVES: The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE)...

Toward a unified framework for interpreting machine-learning models in neuroimaging.

Nature protocols
Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evalu...

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning.

Scientific reports
Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). ...

Hierarchical Analysis of Factors Associated with T Staging of Gastric Cancer by Endoscopic Ultrasound.

Digestive diseases and sciences
BACKGROUND: Size, ulcer, differentiation, and location are known to be factors affecting the T stage accuracy of EUS in gastric cancer. However, whether an interaction exists among recognized variables is poorly understood. The aim of this study was ...