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A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection.

Computational intelligence and neuroscience
Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with tr...

A novel machine learning approach to shorten depression risk assessment for convenient uses.

Journal of affective disorders
BACKGROUND: Depression is a mental disorder affecting many people worldwide which has been exacerbated by the current pandemic. There is an urgent need for a reliable yet short scale for individuals to self-assess the risk of depression conveniently ...

Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning.

International journal of offender therapy and comparative criminology
The link between schizophrenia and homicide has long been the subject of research with significant impact on mental health policy, clinical practice, and public perception of people with psychiatric disorders. The present study investigates factors c...

Machine learning methods to predict attrition in a population-based cohort of very preterm infants.

Scientific reports
The timely identification of cohort participants at higher risk for attrition is important to earlier interventions and efficient use of research resources. Machine learning may have advantages over the conventional approaches to improve discriminati...

Colposcopic multimodal fusion for the classification of cervical lesions.

Physics in medicine and biology
: Cervical cancer is one of the two biggest killers of women and early detection of cervical precancerous lesions can effectively improve the survival rate of patients. Manual diagnosis by combining colposcopic images and clinical examination results...

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.

PloS one
OBJECTIVE: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.

Perfusion Maps Acquired From Dynamic Angiography MRI Using Deep Learning Approaches.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: A typical stroke MRI protocol includes perfusion-weighted imaging (PWI) and MR angiography (MRA), requiring a second dose of contrast agent. A deep learning method to acquire both PWI and MRA with single dose can resolve this issue.

A Synopsis of Artificial Intelligence and its Applications in Surgery.

The American surgeon
Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, ...

Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?

Reproductive biomedicine online
RESEARCH QUESTION: Can models based on artificial intelligence predict embryonic ploidy status or implantation potential of euploid transferred embryos? Can the addition of clinical features into time-lapse monitoring (TLM) parameters as input data i...