journal
https://read.qxmd.com/read/34862559/machine-learning-in-neuro-oncology-epilepsy-alzheimer-s-disease-and-schizophrenia
#1
REVIEW
Mason English, Chitra Kumar, Bonnie Legg Ditterline, Doniel Drazin, Nicholas Dietz
Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862558/radiomic-features-associated-with-extent-of-resection-in-glioma-surgery
#2
REVIEW
Giovanni Muscas, Simone Orlandini, Eleonora Becattini, Francesca Battista, Victor E Staartjes, Carlo Serra, Alessandro Della Puppa
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 classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862557/clinical-prediction-modeling-in-intramedullary-spinal-tumor-surgery
#3
JOURNAL ARTICLE
Elie Massaad, Yoon Ha, Ganesh M Shankar, John H Shin
Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insight about the pathophysiology and prognosis of neurological conditions. This chapter provides a guide for clinicians on relevant aspects of machine learning and reviews selected application of these methods in intramedullary spinal cord tumors. The potential areas of application of machine learning extend far beyond the analyses of clinical data to include several areas of artificial intelligence, such as genomics and computer vision...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862556/machine-learning-and-intracranial-aneurysms-from-detection-to-outcome-prediction
#4
JOURNAL ARTICLE
Vittorio Stumpo, Victor E Staartjes, Giuseppe Esposito, Carlo Serra, Luca Regli, Alessandro Olivi, Carmelo Lucio Sturiale
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 statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862555/artificial-intelligence-in-adult-spinal-deformity
#5
JOURNAL ARTICLE
Pramod N Kamalapathy, Aditya V Karhade, Daniel Tobert, Joseph H Schwab
Artificial Intelligence is gaining traction in medicine for its ease of use and advancements in technology. This study evaluates the current literature on the use of artificial intelligence in adult spinal deformity.
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862554/at-the-pulse-of-time-machine-vision-in-retinal-videos
#6
JOURNAL ARTICLE
Timothy Hamann, Maximilian Wiest, Anton Mislevics, Andrey Bondarenko, Sandrine Zweifel
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 documented qualitatively by clinicians during biomicroscopy. Nowadays numerous imaging devices recording the fundus exist. Hence, video data for objectification of SVP is readily available. Still, these clinical datasets are afflicted with various quality issues and artifacts. In this machine vision based study, we explore methods to overcome challenges in identifying SVP in fundus videos of varying quality and provide a detailed protocol thereof...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862553/machine-learning-in-pituitary-surgery
#7
REVIEW
Vittorio Stumpo, Victor E Staartjes, Luca Regli, Carlo Serra
Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia)...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862552/natural-language-processing-applications-in-the-clinical-neurosciences-a-machine-learning-augmented-systematic-review
#8
JOURNAL ARTICLE
Quinlan D Buchlak, Nazanin Esmaili, Christine Bennett, Farrokh Farrokhi
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 predict clinical outcomes. However, applications to the clinical neurosciences appear to be limited. NLP has matured with the implementation of deep transformer models (e.g., XLNet, BERT, T5, and RoBERTa) and transfer learning. The objectives of this study were to (1) systematically review NLP applications in the clinical neurosciences, and (2) explore NLP analysis to facilitate literature synthesis, providing clear examples to demonstrate the potential capabilities of these technologies for a clinical audience...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862551/big-data-in-the-clinical-neurosciences
#9
JOURNAL ARTICLE
G Damian Brusko, Gregory Basil, Michael Y Wang
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 refinement of big data and predictive modeling using machine learning within neurosurgery. Initial development of national databases arose from surgeons aiming to improve outcome predictions for patients with traumatic brain injury in the 1960s. In the following decades, other surgical specialties began building databases that left a lasting impact on the current national neurosurgical databases, particularly in spine surgery...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862550/predictive-analytics-in-clinical-practice-advantages-and-disadvantages
#10
JOURNAL ARTICLE
Hendrik-Jan Mijderwijk, Hans-Jakob Steiger
Predictive analytics are increasingly reported by clinicians. These tools aim to improve patient outcomes in terms of quality, safety, and efficiency. However, deploying predictive analytics in clinical practice remains challenging today. We highlight several advantages and disadvantages of the application of predictive analytics in clinical practice. To flourish and reach its potential, predictive analytics need data that is of adequate quantity and quality, ideally tailored to clinical scenarios in equipoise regarding optimal management...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862549/the-artificial-intelligence-doctor-considerations-for-the-clinical-implementation-of-ethical-ai
#11
JOURNAL ARTICLE
Julius M Kernbach, Karlijn Hakvoort, Jonas Ort, Hans Clusmann, Georg Neuloh, Daniel Delev
The applications of artificial intelligence (AI) and machine learning (ML) in modern medicine are growing exponentially, and new developments are fast-paced. However, the lack of trust and appropriate legislation hinder its clinical implementation. Recently, there is a clear increase of directives and considerations on Ethical AI. However, most literature broadly deals with ethical tensions on a meta-level without offering hands-on advice in practice. In this article, we non-exhaustively cover basic practical guidelines regarding AI-specific ethical aspects, including transparency and explicability, equity and mitigation of biases, and lastly, liability...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862548/machine-learning-and-ethics
#12
JOURNAL ARTICLE
Tiit Mathiesen, Marike Broekman
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 and memory capacity, this will give rise to many of these new dilemmas.Technology has little if any ethical status but is inevitably interwoven with human activity and thus may serve to allow qualitative and quantitative disruption of human performance and interaction. We argue that personal integrity, justice of resource allocation and accountability of moral agency comprise three themes that characterize ethical dilemmas that arise with development and application of AI...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862547/a-brief-history-of-machine-learning-in-neurosurgery
#13
JOURNAL ARTICLE
Andrew T Schilling, Pavan P Shah, James Feghali, Adrian E Jimenez, Tej D Azad
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 as researchers began developing artificial neural networks to analyze structured datasets and supervised tasks. By the turn of the millennium, machine learning had evolved beyond proof-of-concept; algorithms had success detecting tumors in unstructured clinical imaging, and unsupervised learning showed promise for tumor segmentation...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862546/overview-of-algorithms-for-natural-language-processing-and-time-series-analyses
#14
JOURNAL ARTICLE
James Feghali, Adrian E Jimenez, Andrew T Schilling, Tej D Azad
A host of machine learning algorithms have been used to perform several different tasks in NLP and TSA. Prior to implementing these algorithms, some degree of data preprocessing is required. Deep learning approaches utilizing multilayer perceptrons, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) represent commonly used techniques. In supervised learning applications, all these models map inputs into a predicted output and then model the discrepancy between predicted values and the real output according to a loss function...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862545/foundations-of-time-series-analysis
#15
JOURNAL ARTICLE
Jonas Ort, Karlijn Hakvoort, Georg Neuloh, Hans Clusmann, Daniel Delev, Julius M Kernbach
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 pursuit of forecasting future events from historical data. TS are chronological sequences of observations, and TS data are therefore prevalent in many aspects of clinical medicine and academic neuroscience. With the rise of highly complex and nonlinear datasets, machine learning (ML) methods have become increasingly popular for prediction or pattern detection and within neurosciences, including neurosurgery...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862544/natural-language-processing-practical-applications-in-medicine-and-investigation-of-contextual-autocomplete
#16
JOURNAL ARTICLE
Leah Voytovich, Clayton Greenberg
Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are growing considerably every day, this chapter will focus on one particularly relevant application for healthcare professionals-reducing the burden of clinical documentation. More specifically, the chapter will discuss two studies (Gopinath et al., Fast, structured clinical documentation via contextual autocomplete...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862543/tackling-the-complexity-of-lesion-symptoms-mapping-how-to-bridge-the-gap-between-data-scientists-and-clinicians
#17
JOURNAL ARTICLE
Emmanuel Mandonnet, Bertrand Thirion
Accurate and predictive lesion-symptoms mapping 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 lesion-symptom mapping technique, emphasizing the need of developing multivariate methods. While the organization of large datasets and their analysis with machine learning (ML) approaches represents an opportunity to increase prediction accuracy, the complexity and dimensionality of the problem remain a major obstacle...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862542/foundations-of-multiparametric-brain-tumour-imaging-characterisation-using-machine-learning
#18
JOURNAL ARTICLE
Anne Jian, Kevin Jang, Carlo Russo, Sidong Liu, Antonio Di Ieva
The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862541/foundations-of-lesion-detection-using-machine-learning-in-clinical-neuroimaging
#19
JOURNAL ARTICLE
Manoj Mannil, Nicolin Hainc, Risto Grkovski, Sebastian Winklhofer
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 and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process...
2022: Acta Neurochirurgica. Supplement
https://read.qxmd.com/read/34862540/applying-convolutional-neural-networks-to-neuroimaging-classification-tasks-a-practical-guide-in-python
#20
JOURNAL ARTICLE
Moumin A K Mohamed, Alexander Alamri, Brandon Smith, Christopher Uff
In this chapter, we describe the process of obtaining medical imaging data and its storage protocol. The authors also explain in a step-by-step approach how to extract and prepare the medical imaging data for machine learning algorithms. And finally, the process of building and assessing a convolutional neural network for medical imaging data is illustrated.
2022: Acta Neurochirurgica. Supplement
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