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Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Cancer invasion depth is a critical factor affecting the choice of treatment in patients with superficial squamous cell carcinoma (SCC). However, the diagnosis of invasion depth is currently subjective and liable to interobserver...

A RR interval based automated apnea detection approach using residual network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, more and more patients suffer from complications of apnea. But most of them stay untreated due to th...

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

Journal of medical Internet research
BACKGROUND: Suicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motiva...

Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates.

Sensors (Basel, Switzerland)
Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP's normality based on oscillometric measurements, we use statistical...

Comparison of robot-assisted modified radical neck dissection using a bilateral axillary breast approach with a conventional open procedure after propensity score matching.

Surgical endoscopy
BACKGROUND: There is ongoing debate about whether or not robot-assisted thyroidectomy is appropriate for modified radical neck dissection (MRND). The purpose of this study was to compare the surgical outcomes of robot-assisted MRND with those of a co...

Effects of combining modern recovery techniques with neurotrophic medication and standard treatment in stroke patients.

The Science of the total environment
Stroke is the main cause of disability after age 65, leaving survivors with sequels that require care and recovery treatment lasting years. It is estimated that by the year 2030 this pathology will be leading cause of mortality. To determine the effi...

Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.

NeuroImage
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation m...

A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple s...

Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings.

Sensors (Basel, Switzerland)
The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms us...

Identifying Children With Clinical Language Disorder: An Application of Machine-Learning Classification.

Journal of learning disabilities
In this study, we identified child- and family-level characteristics most strongly associated with clinical identification of language disorder for preschool-aged children. We used machine learning to identify variables that best classified children ...