AIMC Topic: Machine Learning

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AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

Journal of neuroradiology = Journal de neuroradiologie
PURPOSE: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL).

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy.

Journal of chemical information and modeling
Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reli...

Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models.

The Science of the total environment
This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk...

flowSim: Near duplicate detection for flow cytometry data.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The analysis of large amounts of data is important for the development of machine learning (ML) models. flowSim is the first algorithm designed to visualize, detect and remove highly redundant information in flow cytometry (FCM) training sets to decr...

The Big Bang of Deep Learning in Ultrasound-Guided Surgery: A Review.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and...

Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology.

Biomolecules
The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are impor...

Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach.

BMC cardiovascular disorders
BACKGROUND: Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles a...

The 510(k) Third Party Review Program: Promise and Potential.

Journal of medical systems
Every year, the Food and Drug Administration (FDA) clears approximately 3,000 medical devices for marketing via the 510(k) pathway. These constitute 99% of all devices approved for human use and includes the premarket review of many devices incorpora...

Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis.

Journal of breath research
Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose diseas...

Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning.

Frontiers in endocrinology
BACKGROUND: Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic un...