AIMC Topic: Algorithms

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Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform.

Annals of medicine
BACKGROUND: Early diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity.

CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.

Medical & biological engineering & computing
Functional near-infrared spectroscopy (fNIRS), an optical neuroimaging technique, has been widely used in the field of brain activity recognition and brain-computer interface. Existing works have proposed deep learning-based algorithms for the fNIRS ...

Classification tree obtained by artificial intelligence for the prediction of heart failure after acute coronary syndromes.

Medicina clinica
BACKGROUND: Coronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of developing HF after an acute coronary syndrome (ACS). Artificial intelligence (AI) has proven to be ...

Equivariant score-based generative diffusion framework for 3D molecules.

BMC bioinformatics
BACKGROUND: Molecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing gener...

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.

Scientific reports
Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorith...

Machine-Learning Assisted Screening of Correlated Covariates: Application to Clinical Data of Desipramine.

The AAPS journal
Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to appl...

Laparoscopic distal gastrectomy skill evaluation from video: a new artificial intelligence-based instrument identification system.

Scientific reports
The advent of Artificial Intelligence (AI)-based object detection technology has made identification of position coordinates of surgical instruments from videos possible. This study aimed to find kinematic differences by surgical skill level. An AI a...

Multiscale knowledge distillation with attention based fusion for robust human activity recognition.

Scientific reports
Knowledge distillation is an effective approach for training robust multi-modal machine learning models when synchronous multimodal data are unavailable. However, traditional knowledge distillation techniques have limitations in comprehensively trans...

Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks.

Physics in medicine and biology
Fast and accurate deformable image registration (DIR), including DIR uncertainty estimation, is essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challeng...

The application of impulse oscillometry system based on machine learning algorithm in the diagnosis of chronic obstructive pulmonary disease.

Physiological measurement
. Diagnosing chronic obstructive pulmonary disease (COPD) using impulse oscillometry (IOS) is challenging due to the high level of clinical expertise it demands from doctors, which limits the clinical application of IOS in screening. The primary aim ...