AIMC Topic: Machine Learning

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Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

World neurosurgery
BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional ap...

Enhanced breast mass mammography classification approach based on pre-processing and hybridization of transfer learning models.

Journal of cancer research and clinical oncology
BACKGROUND AND OBJECTIVE: The second most prevalent cause of death among women is now breast cancer, surpassing heart disease. Mammography images must accurately identify breast masses to diagnose early breast cancer, which can significantly increase...

Everything has its price: Foundations of cost-sensitive machine learning and its application in psychology.

Psychological methods
Psychology has seen an increase in the use of machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclass...

Utility of machine learning for identifying stapes fixation on ultra-high-resolution CT.

Japanese journal of radiology
PURPOSE: Imaging diagnosis of stapes fixation (SF) is challenging owing to a lack of definite evidence. We developed a comprehensive machine learning (ML) model to identify SF on ultra-high-resolution CT.

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.

Current rheumatology reports
PURPOSE OF REVIEW: Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and pat...

Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification.

Chemical communications (Cambridge, England)
Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and ...

MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems.

PloS one
When judging the quality of a computational system for a pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in the multi-label paradi...

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield ...

Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population.

Biochemical and biophysical research communications
Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification ...