AIMC Topic: Machine Learning

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A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization.

PloS one
Real-time endoscopic rectal lesion characterization employing artificial intelligence (AI) and near-infrared (NIR) imaging of the fluorescence perfusion indicator agent Indocyanine Green (ICG) has demonstrated promise. However, commercially available...

NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Artificial intelligence (AI) has shown great promise in analyzing nasal endoscopic images for disease detection. However, current AI systems require extensive expert-labeled data for each specific medical condition, limiting their applications. In th...

Machine learning methods to study sequence-ensemble-function relationships in disordered proteins.

Current opinion in structural biology
Recent years have seen tremendous developments in the use of machine learning models to link amino-acid sequence, structure, and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences th...

Molecular structure and mechanism of protein MSMB, TPPP3, SPI1: Construction of novel 4 pancreatic cancer-related protein signatures model based on machine learning.

International journal of biological macromolecules
The high mortality rate of pancreatic cancer is closely related to its inconspicuous early symptoms and difficult diagnosis. In recent years, with the rapid development of proteomics and bioinformatics, the use of machine learning technology to analy...

Machine learning prediction of overall survival in prostate adenocarcinoma using ensemble techniques.

Computers in biology and medicine
Prostate adenocarcinoma (PAC) is a complex and common cancer in males and is one of the leading causes of cancer-related death globally. PAC is a multifaceted disease that encompasses different subtypes, including acinar and ductal adenocarcinoma, sm...

Dynamic Bonding for In-Situ Welding of Multilayer Elastomers Enables High-Performance Wearable Electronics for Machine Learning-Assisted Active Rehabilitation.

Advanced materials (Deerfield Beach, Fla.)
Patients with hand dysfunction require joint rehabilitation for functional restoration, and wearable electronics can provide physical signals to assess and guide the process. However, most wearable electronics are susceptible to failure under large d...

Enhanced personalized prediction of baseball-related upper extremity injuries through novel features and explainable artificial intelligence.

Journal of sports sciences
Upper extremity injuries in baseball players demand advanced prevention. Our study analyzed clinical features using machine learning techniques to provide precise and individualized injury risk assessment and prediction. We recruited 98 baseball play...

Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management.

ACS applied materials & interfaces
Sports cardiology focuses on athletes' cardiovascular health, yet sudden cardiac death remains a significant concern despite preventative measures. Prolonged physical activity leads to notable cardiovascular adaptations, known as the athlete's heart,...

Portable and Self-Powered Sensing AI-Enabled Mask for Emotional Recognition in Virtual Reality.

ACS applied materials & interfaces
With the increasing development of metaverse and human-computer interaction (HMI) technologies, artificial intelligence (AI) applications in virtual reality (VR) environments are receiving significant attention. This study presents a self-sensing fac...

Machine Learning for Quantitative Prediction of Protein Adsorption on Well-Defined Polymer Brush Surfaces with Diverse Chemical Properties.

Langmuir : the ACS journal of surfaces and colloids
Polymer informatics has attracted increasing attention because machine learning can establish quantitative structure-property relationships in polymer materials. Understanding and controlling protein adsorption on polymer surfaces are crucial for var...