AIMC Topic: Male

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Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To develop machine learning algorithms to predict failure to achieve clinically significant satisfaction after hip arthroscopy.

Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing.

Journal of emergency nursing
INTRODUCTION: Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acuity-based patient prioritization. The purpose of this retrospective study was to determine whether hi...

Comparing the Approach to Radical Prostatectomy Using the Multiport da Vinci Xi and da Vinci SP Robots: A Propensity Score Analysis of Perioperative Outcomes.

European urology
BACKGROUND: Use of the single-port da Vinci SP robotic platform for various urological procedures has been described by several groups. However, the comparative performance of the SP robot in relation to earlier models such as the da Vinci Xi is stil...

The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood.

Neuroscience letters
BACKGROUND: Schizophrenia (SCZ) is a highly heritable mental disorder with a substantial disease burden. Machine learning (ML) method can be used to identify individuals with SCZ on the basis of blood gene expression data with high accuracy.

Sexual Satisfaction in Men Suffering From Erectile Dysfunction After Robot-Assisted Radical Prostatectomy for Prostate Cancer: An Observational Study.

The journal of sexual medicine
BACKGROUND: Preservation of erectile function is an important postoperative quality of life concern for patients after robot-assisted radical prostatectomy (RARP) for prostate cancer. Although erectile function may recover, many men continue to suffe...

Characterization of Antiphospholipid Syndrome Atherothrombotic Risk by Unsupervised Integrated Transcriptomic Analyses.

Arteriosclerosis, thrombosis, and vascular biology
OBJECTIVE: Our aim was to characterize distinctive clinical antiphospholipid syndrome phenotypes and identify novel microRNA (miRNA)-mRNA-intracellular signaling regulatory networks in monocytes linked to cardiovascular disease. Approach and Results:...

Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.

Annals of emergency medicine
STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-lea...

Automated Cerebral Hemorrhage Detection Using RAPID.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) is an important event that is diagnosed on head NCCT. Increased NCCT utilization in busy hospitals may limit timely identification of ICH. RAPID ICH is an automated hybrid 2D-3D convolutional neur...

Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination.

Sensors (Basel, Switzerland)
We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the pro...

Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

Annals of surgery
OBJECTIVE: We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (ES) patients.