1,074 Result(s)
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Medicine & Public Health
Neurology
Neuroradiology
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Chapter
Conference Paper
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Chapter and Conference Paper
Machine Learning and Ethics
When new technology is introduced into healthcare, novel ethical dilemmas arise in the human-machine interface. As artificial intelligence (AI), machine learning (ML) and big data can exhaust human oversight a...
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Chapter and Conference Paper
Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review
Natural language processing (NLP), a domain of artificial intelligence (AI) that models human language, has been used in medicine to automate diagnostics, detect adverse events, support decision making and pre...
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Chapter and Conference Paper
At the Pulse of Time: Machine Vision in Retinal Videos
Spontaneous venous pulsations (SVP) are a common finding in healthy people. The absence of SVP is associated with rapid progression in glaucoma and increased intracranial pressure. Traditionally, SVP has been ...
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Chapter and Conference Paper
Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction
Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional st...
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Chapter and Conference Paper
Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus
The democratization of machine learning (ML) through availability of open-source learning libraries, the availability of datasets in the “big data” era, increasing computing power even on mobile devices, and o...
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Chapter and Conference Paper
Radiomic Features Associated with Extent of Resection in Glioma Surgery
Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like...
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Chapter and Conference Paper
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part II—Generalization and Overfitting
We review the concept of overfitting, which is a well-known concern within the machine learning community, but less established in the clinical community. Overfitted models may lead to inadequate conclusions that...
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Chapter and Conference Paper
Introduction to Deep Learning in Clinical Neuroscience
The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the b...
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Chapter and Conference Paper
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV—A Practical Approach to Binary Classification Problems
We illustrate the steps required to train and validate a simple, machine learning-based clinical prediction model for any binary outcome, such as, for example, the occurrence of a complication, in the statisti...
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Chapter and Conference Paper
Deployment of Clinical Prediction Models: A Practical Guide to Nomograms and Online Calculators
The use of predictive models within neurosurgery is increasing and many models described in published journal articles are made available to readers in formats such as nomograms and online calculators. The pre...
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Chapter and Conference Paper
Foundations of Feature Selection in Clinical Prediction Modeling
Selecting a set of features to include in a clinical prediction model is not always a simple task. The goals of creating parsimonious models with low complexity while, at the same time, upholding predictive pe...
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Chapter and Conference Paper
Is My Clinical Prediction Model Clinically Useful? A Primer on Decision Curve Analysis
Decision curve analysis is an increasingly popular method to assess the impact of a prediction model on medical decision making. The analysis provides a graphical summary. A basic understanding of a decision c...
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Chapter and Conference Paper
A Discussion of Machine Learning Approaches for Clinical Prediction Modeling
While machine learning has occupied a niche in clinical medicine for decades, continued method development and increased accessibility of medical data have led to broad diversification of approaches. These ran...
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Chapter and Conference Paper
Machine Learning Algorithms in Neuroimaging: An Overview
Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) i...
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Chapter and Conference Paper
Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging
This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology an...
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Chapter and Conference Paper
Foundations of Brain Image Segmentation: Pearls and Pitfalls in Segmenting Intracranial Blood on Computed Tomography Images
Not only the time-dependent varying of signal intensity (i.e. haematoma evolution) characteristics of the intracranial blood in computed tomography images, but also the fluctuating image quality, the distortio...
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Chapter and Conference Paper
Tackling the Complexity of Lesion-Symptoms Map**: How to Bridge the Gap Between Data Scientists and Clinicians?
Accurate and predictive lesion-symptoms map** is a major goal in the field of clinical neurosciences. Recent studies have called for a reappraisal of the results given by the standard univariate voxel-based ...
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Chapter and Conference Paper
Foundations of Time Series Analysis
For almost a century, classical statistical methods including exponential smoothing and autoregression integrated moving averages (ARIMA) have been predominant in the analysis of time series (TS) and in the pu...
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Chapter and Conference Paper
A Brief History of Machine Learning in Neurosurgery
The history of machine learning in neurosurgery spans three decades and continues to develop at a rapid pace. The earliest applications of machine learning within neurosurgery were first published in the 1990s...
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Chapter and Conference Paper
Big Data in the Clinical Neurosciences
The clinical neurosciences have historically been at the forefront of innovation, often incorporating the newest research methods into practice. This chapter will explore the adoption, implementation, and refi...
