AIMC Topic: Adult

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Development and validation of automated three-dimensional convolutional neural network model for acute appendicitis diagnosis.

Scientific reports
Rapid, accurate preoperative imaging diagnostics of appendicitis are critical in surgical decisions of emergency care. This study developed a fully automated diagnostic framework using a 3D convolutional neural network (CNN) to identify appendicitis ...

Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.

Scientific reports
Assessment of the morphology of red blood cells (RBCs) can improve clinical benefits following blood transfusion. Deep machine learning surpasses traditional microscopy-based classification methods, offering more accurate and consistent results while...

Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians.

Journal of neurology
BACKGROUND: Acute vestibular syndrome usually represents either vestibular neuritis (VN), an innocuous viral illness, or posterior circulation stroke (PCS), a potentially life-threatening event. The video head impulse test (VHIT) is a quantitative me...

Epidemiology and risk factors of Clonorchis sinensis infection in the mountainous areas of Longsheng County, Guangxi: insights from automated machine learning.

Parasitology research
Clonorchis sinensis (C. sinensis) is mainly prevalent in Northeast and South China, with Guangxi being the most severely affected region. This study aimed to evaluate the prevalence and identify the risk factors of C. sinensis infection in Longsheng ...

Machine Learning-Based Prediction of Delirium and Risk Factor Identification in Intensive Care Unit Patients With Burns: Retrospective Observational Study.

JMIR formative research
BACKGROUND: The incidence of delirium in patients with burns receiving treatment in the intensive care unit (ICU) is high, reaching up to 77%, and has been associated with increased mortality rates. Therefore, early identification of patients at high...

AI-determined similarity increases likability and trustworthiness of human voices.

PloS one
Modern artificial intelligence (AI) technology is capable of generating human sounding voices that could be used to deceive recipients in various contexts (e.g., deep fakes). Given the increasing accessibility of this technology and its potential soc...

Cell-free DNA in ex-vivo lung perfusate is associated with low-quality lungs and lung transplant outcome.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
BACKGROUND: Cell-free DNA (cfDNA) in ex-vivo lung perfusion (EVLP) perfusate has been shown to potentially reflect lung injury; however, the relationship between cfDNA concentration with clinical EVLP lung outcomes has not been elucidated.

Morphological alterations of the thymus gland in individuals with schizophrenia.

Molecular psychiatry
Despite its critical function in the immune system and accumulating evidence of immunological abnormalities in schizophrenia, the thymus has long been overlooked. We aimed to investigate thymic morphological alterations and their corresponding hetero...

Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry.

Orthodontics & craniofacial research
OBJECTIVE: Surface-based three-dimensional (3D) cephalometry provides detailed clinical information for the analysis of craniofacial structures. This study aimed to develop an automated 3D surface cephalometry system using mesh fitting based on landm...

An early prediction model for gestational diabetes mellitus created using machine learning algorithms.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: To investigate high-risk factors for gestational diabetes mellitus (GDM) in early pregnancy through an analysis of demographic and clinical data, and to develops a machine-learning-based prediction model to enhance early diagnosis and inte...