AIMC Topic: Female

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A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings.

Medical physics
PURPOSE: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer-aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses.

Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples.

International journal of speech-language pathology
: This research aimed to automatically predict intelligible speaking rate for individuals with Amyotrophic Lateral Sclerosis (ALS) based on speech acoustic and articulatory samples. Twelve participants with ALS and two normal subjects produced a tot...

More than just co-workers: Presence of humanoid robot co-worker influences human performance.

PloS one
Does the presence of a robot co-worker influence the performance of humans around it? Studies of motor contagions during human-robot interactions have examined either how the observation of a robot affects a human's movement velocity, or how it affec...

Learning Curve in Robot-Assisted Laparoscopic Liver Resection.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: The objective of this study was to evaluate the learning curve effect on the safety and feasibility of robot-assisted liver resection (RALR).

Determination of appropriate urine volume cutoff values for voided urine specimens to assess adequacy.

Journal of the American Society of Cytopathology
INTRODUCTION: Incorporating urine volume into adequacy assessment was recommended by The Paris System for Reporting Urinary Cytology. The concept was relatively new, however, and supportive studies were sparse. We accordingly aimed to determine the r...

Predicting drug-resistant epilepsy - A machine learning approach based on administrative claims data.

Epilepsy & behavior : E&B
Patients with drug-resistant epilepsy (DRE) are at high risk of morbidity and mortality, yet their referral to specialist care is frequently delayed. The ability to identify patients at high risk of DRE at the time of treatment initiation, and to sub...

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application.

Journal of medical Internet research
BACKGROUND: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This pro...

Forensic age estimation for pelvic X-ray images using deep learning.

European radiology
PURPOSE: To develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model.

Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Journal of affective disorders
BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine lea...

Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning.

Radiology
Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network learning of chest radiograph features in congestive heart failure (CHF). Materials and Methods A total of 103 489 frontal chest radiographs in 46 712 pati...