AIMC Topic: Machine Learning

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Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients wi...

Practical Applications of Artificial Intelligence in Spine Imaging: A Review.

Radiologic clinics of North America
Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, whic...

Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.

International journal of molecular sciences
Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by r...

Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Integrated environmental assessment and management
Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem se...

Should All Pancreatic Cystic Lesions with Worrisome or High-Risk Features Be Resected? A Clinical and Radiological Machine Learning Model May Help to Answer.

Academic radiology
RATIONALE AND OBJECTIVES: According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) mo...

Using expert-reviewed CSAM to train CNNs and its anthropological analysis.

Journal of forensic and legal medicine
Machine learning methods for the identification of child sexual abuse materials (CSAM) have been previously studied, however, they have serious limitations. Firstly, the training sets used to train the appropriate machine learning algorithms were not...

Artificial intelligence in endocrinology: a comprehensive review.

Journal of endocrinological investigation
BACKGROUND AND AIM: Artificial intelligence (AI) has emerged as a promising technology in the field of endocrinology, offering significant potential to revolutionize the diagnosis, treatment, and management of endocrine disorders. This comprehensive ...