AIMC Topic: Adult

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Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.

Clinical and experimental medicine
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,8...

Cardiac patients' surgery outcome and associated factors in Ethiopia: application of machine learning.

BMC pediatrics
INTRODUCTION: Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed natio...

Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians.

BMC emergency medicine
BACKGROUNDS: Acute Appendicitis (AA) is one of the most common surgical emergencies worldwide. This study aims to investigate the predictive performances of 6 different Machine Learning (ML) algorithms for simple and complicated AA.

Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.

Scientific reports
Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learning and population data to evaluate the association between prepregnancy or perinatal risk factors an...

Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists.

Clinical oral investigations
OBJECTIVES: Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However...

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are already applications for thyroid and breast lesions that enable AI-assis...

Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.

Neural networks : the official journal of the International Neural Network Society
Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification per...

Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data.

Psychiatry research. Neuroimaging
BACKGROUND: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpo...

Uncovering the most robust predictors of problematic pornography use: A large-scale machine learning study across 16 countries.

Journal of psychopathology and clinical science
Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in...

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BMC infectious diseases
BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two wee...