AIMC Topic: Machine Learning

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Unveiling the potential of artificial intelligence in orthopaedic surgery.

British journal of hospital medicine (London, England : 2005)
Artificial intelligence is paving the way in contemporary medical advances, with the potential to revolutionise orthopaedic surgical care. By harnessing the power of complex algorithms, artificial intelligence yields outputs that have diverse applica...

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

Analysis and comparison of machine learning methods for species identification utilizing ATR-FTIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate identification of insect species holds paramount significance in diverse fields as it facilitates a comprehensive understanding of their ecological habits, distribution range, and impact on both the environment and humans. While morphologica...

Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices.

Journal of biomedical informatics
INTRODUCTION: Advances in wearable sensor technology have enabled the collection of biomarkers that may correlate with levels of elevated stress. While significant research has been done in this domain, specifically in using machine learning to detec...

Achalasia phenotypes and prediction of peroral endoscopic myotomy outcomes using machine learning.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: High-resolution manometry (HRM) and esophagography are used for achalasia diagnosis; however, achalasia phenotypes combining esophageal motility and morphology are unknown. Moreover, predicting treatment outcomes of peroral endoscopic myo...

Predicting incomplete occlusion of intracranial aneurysms treated with flow diverters using machine learning models.

Journal of neurosurgery
OBJECTIVE: Intracranial saccular aneurysms are vascular malformations responsible for 80% of nontraumatic brain hemorrhage. Recently, flow diverters have been used as a less invasive therapeutic alternative for surgery. However, they fail to achieve ...

Machine Learning Model with Computed Tomography Radiomics and Clinicobiochemical Characteristics Predict the Subtypes of Patients with Primary Aldosteronism.

Academic radiology
RATIONALE AND OBJECTIVES: Adrenal venous sampling (AVS) is the primary method for differentiating between primary aldosterone (PA) subtypes. The aim of study is to develop prediction models for subtyping of patients with PA using computed tomography ...

Learning to See.

Journal of general internal medicine

Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification.

Acta tropica
Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) em...

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability.

Proceedings of the National Academy of Sciences of the United States of America
Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal...