AI Medical Compendium Topic:
Neuroimaging

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Machine Learning Algorithm Validation: From Essentials to Advanced Applications and Implications for Regulatory Certification and Deployment.

Neuroimaging clinics of North America
The deployment of machine learning (ML) models in the health care domain can increase the speed and accuracy of diagnosis and improve treatment planning and patient care. Translating academic research to applications that are deployable in clinical s...

3D computational cannula fluorescence microscopy enabled by artificial neural networks.

Optics express
Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and...

Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.

The neuroradiology journal
Many brain disorders - such as Alzheimer's disease, Parkinson's disease, schizophrenia and autism - are heterogeneous, that is, they may have several subtypes. Traditionally, clinicians have identified subtypes, such as subtypes of psychosis, using c...

Binary Classification of Alzheimer's Disease Using sMRI Imaging Modality and Deep Learning.

Journal of digital imaging
Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for the development of possible future treatment option(s). St...

Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging.

Neuroinformatics
A fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass pr...

A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.

Neuroinformatics
Quantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature rep...

Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges.

Current opinion in neurology
PURPOSE OF REVIEW: Machine learning is an artificial intelligence technique that allows computers to perform a task without being explicitly programmed. Machine learning can be used to assist diagnosis and prognosis of brain disorders. Although the e...

Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals.

Unsupervised stratification in neuroimaging through deep latent embeddings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain use...