AIMC Topic: Machine Learning

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Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology.

Journal of the American College of Radiology : JACR
Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common i...

Identification and evaluation of cognitive deficits in schizophrenia using "Machine learning".

Psychiatria Danubina
BACKGROUND: Schizophrenia can be interpreted as a pathology involving the neocortex whose cognitive dysfunctions represent a central and persistent characteristic of the disease, as well as one of the more important symptoms in relation to the impair...

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account.

Current protocols in chemical biology
The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in pr...

Radiomics: Data Are Also Images.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated onc...

Artificial Intelligence in Nuclear Medicine.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Despite the great media attention for artificial intelligence (AI), for many health care professionals the term and the functioning of AI remain a "black box," leading to exaggerated expectations on the one hand and unfounded fears on the other. In t...

Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and me...

The promises and perils of automated facial action coding in studying children's emotions.

Developmental psychology
Computer vision algorithms have made tremendous advances in recent years. We now have algorithms that can detect and recognize objects, faces, and even facial actions in still images and video sequences. This is wonderful news for researchers that ne...

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.

Korean journal of radiology
OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup.

[VOTE versus ACLTE: comparison of two snoring noise classifications using machine learning methods].

HNO
BACKGROUND: Acoustic snoring sound analysis is a noninvasive method for diagnosis of the mechanical mechanisms causing snoring that can be performed during natural sleep. The objective of this work is development and evaluation of classification sche...

Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning.

Drug safety
INTRODUCTION: Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The...