AIMC Topic: Humans

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Rethinking model prototyping through the MedMNIST+ dataset collection.

Scientific reports
The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, the field has increasingly prioritized marginal performance gains on a few, narrowly...

Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.

Scientific reports
Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to mortality. We developed a recurrence prediction system for HCC patients before and after LT. Data from patients with HCC who underwent LT were retros...

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 ...

Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed.

Scientific reports
Long short-term memory (LSTM) networks are widely used in biomechanical data analysis but have the significant limitations in interpretability and decision transparency. Combining graph neural networks (GNN) with gate recurrent units (GRU) may offer ...

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...

Assessing operative variability in robot-assisted radical prostatectomy (RARP) through AI.

Journal of robotic surgery
Robotic-assisted radical prostatectomy (RARP) is the most commonly performed robotic procedure in urology. Using artificial intelligence (AI), surgical steps and practices can be assessed and validated through surgical video, and connected to patient...

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 ...

Generative Artificial Intelligence in Nuclear Medicine Education.

Journal of nuclear medicine technology
Generative artificial intelligence (genAI) has become assimilated into the education, research, and clinical domains of nuclear medicine and health care. Understanding the principles, limitations, and applications of genAI is important for capitalizi...

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...