AIMC Topic: Machine Learning

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No code machine learning: validating the approach on use-case for classifying clavicle fractures.

Clinical imaging
PURPOSE: We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle...

Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models.

The American journal of clinical nutrition
BACKGROUND: Sarcopenia is known as a decline in skeletal muscle quality and function that is associated with age. Sarcopenia is linked to diverse health problems, including endocrine-related diseases. Environmental chemicals (ECs), a broad class of c...

Predicting treatment resistance in schizophrenia patients: Machine learning highlights the role of early pathophysiologic features.

Schizophrenia research
Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, wi...

Navigating the future: machine learning's role in revolutionizing antimicrobial stewardship and infection prevention and control.

Current opinion in infectious diseases
PURPOSE OF REVIEW: This review examines the current state and future prospects of machine learning (ML) in infection prevention and control (IPC) and antimicrobial stewardship (ASP), highlighting its potential to transform healthcare practices by enh...

Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.

Journal of assisted reproduction and genetics
PURPOSE: This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese popula...

Machine-Learning-Assisted Rational Design of Si─Rhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence Navigation.

Advanced materials (Deerfield Beach, Fla.)
High-performance fluorescent probes stand as indispensable tools in fluorescence-guided imaging, and are crucial for precise delineation of focal tissue while minimizing unnecessary removal of healthy tissue. Herein, machine-learning-assisted strateg...

Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video.

Journal of endourology
Chemical composition analysis is important in prevention counseling for kidney stone disease. Advances in laser technology have made dusting techniques more prevalent, but this offers no consistent way to collect enough material to send for chemical...

Machine-learning based detection of marine mammal vocalizations in snapping-shrimp dominated ambient noise.

Marine environmental research
Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise o...

Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap.

ACS applied materials & interfaces
DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread di...