AIMC Topic: Machine Learning

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Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To evaluate the value of radiomics based on ultrasonography in differentiating follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) and construct a tool for preoperative noninvasive predicting FTC and FTA.

Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post-hoc analysis of the WARCEF trial.

European journal of clinical investigation
BACKGROUND: The prediction of ischaemic stroke in patients with heart failure with reduced ejection fraction (HFrEF) but without atrial fibrillation (AF) remains challenging. Our aim was to evaluate the performance of machine learning (ML) in identif...

Reconsidering learnable fine-grained text prompts for few-shot anomaly detection in visual-language models.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Anomaly Detection (FSAD) in industrial images aims to identify abnormalities using only a few normal images, which is crucial for industrial scenarios where sample training is limited. The recent advances in large-scale pre-trained visual-la...

Development of a survey-based stacked ensemble predictive model for autonomy preferences in patients with periodontal disease.

Journal of dentistry
OBJECTIVES: This study aimed to develop a model to predict the autonomy preference (AP) and satisfaction after tooth extraction (STE) in patients with periodontal disease. Understanding of individual AP and STE is essential for improving patient sati...

Extended Activity Cliffs-Driven Approaches on Data Splitting for the Study of Bioactivity Machine Learning Predictions.

Molecular informatics
The presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its high data dependency, Machine Learning QSAR models will be directly influenced by the activity landscape. We propose several extended similarity...

Machine learning in image-based outcome prediction after radiotherapy: A review.

Journal of applied clinical medical physics
The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various...

Predicting Asthma Exacerbations Using Machine Learning Models.

Advances in therapy
INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis are needed for real-world decision-making in asthma. Machine learning (ML) leverages mathematical and ...

Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions.

Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society
The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and deep learning, impacts researchers, clinicians, and industry due to promising performance and clinica...

Transparent Machine Learning Model to Understand Drug Permeability through the Blood-Brain Barrier.

Journal of chemical information and modeling
The blood-brain barrier (BBB) selectively regulates the passage of chemical compounds into and out of the central nervous system (CNS). As such, understanding the permeability of drug molecules through the BBB is key to treating neurological diseases...

Toward trustable use of machine learning models of variant effects in the clinic.

American journal of human genetics
There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part by progress in applying deep learning methods to sequence data. These models have the potential to e...