Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier

Kaka, Hussam; Zhang, Euan; Khan, N

doi:10.1177/0846537120954293

Cited by 46 publications

(21 citation statements)

References 60 publications

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“…Discrimination between different types of brain tumors is problematic at imaging. Accurate diagnosis is crucial for planning of treatment to improve patient's outcome, helpful in the grading of tumors and response after therapy [1][2][3][4][5][6][7]. Brain tumor biopsy is considered the gold standard for diagnosis.…”

Section: Brain Tumorsmentioning

confidence: 99%

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

et al. 2021

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This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient’s prognoses.

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Section: Brain Tumorsmentioning

confidence: 99%

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

et al. 2021

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“…It utilizes artificial neural networks with multiple “hidden” layers to solve complex problems [ 7 ]. These “hidden layers” enable the machine to continually learn and incorporate newly acquired knowledge to improve its performance [ 7 , 8 ]. Deep Learning can be unsupervised, semi-supervised, or supervised.…”

Section: Terminologymentioning

confidence: 99%

Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know

Wagner

Namdar²,

Biswas

et al. 2021

Neuroradiology

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Purpose Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology. Methods When designing AI-based research in neuroradiology and appreciating the literature, it is important to understand the fundamental principles of AI. Training, validation, and test datasets must be defined and set apart as priorities. External validation and testing datasets are preferable, when feasible. The specific type of learning process (supervised vs. unsupervised) and the machine learning model also require definition. Deep learning (DL) is an AI-based approach that is modelled on the structure of neurons of the brain; convolutional neural networks (CNN) are a commonly used example in neuroradiology. Results Radiomics is a frequently used approach in which a multitude of imaging features are extracted from a region of interest and subsequently reduced and selected to convey diagnostic or prognostic information. Deep radiomics uses CNNs to directly extract features and obviate the need for predefined features. Conclusion Common limitations and pitfalls in AI-based research in neuroradiology are limited sample sizes (“small-n-large-p problem”), selection bias, as well as overfitting and underfitting.

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“…In the field of MS, ML approaches have often focused on automatic examination of MRI images to classify disease at the time of onset or to predict evolution of clinically isolated forms, following the flourishing stream of image analysis. Recent reviews have summarized the state of the art [26,27] and huge efforts to identify the MRI determinants of progression are ongoing (for instance, the collaborative network awards offered by the International Progressive MS Alliance). However, although studies on automatic analysis of brain MRI scans in MS date back to 1998 [28], the approach still remains outside clinical practice.…”

Section: Machine Learning and Multiple Sclerosismentioning

confidence: 99%

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Seccia

Romano

Salvetti

et al. 2021

Life

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The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

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Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier

Cited by 46 publications

References 60 publications

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

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