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. 2020 Mar 4;2(2):e190026.
doi: 10.1148/ryai.2020190026. eCollection 2020 Mar.

Deep Learning in Neuroradiology: A Systematic Review of Current Algorithms and Approaches for the New Wave of Imaging Technology

Anthony D Yao  1 , Derrick L Cheng  1 , Ian Pan  1 , Felipe Kitamura  1
Affiliations

Deep Learning in Neuroradiology: A Systematic Review of Current Algorithms and Approaches for the New Wave of Imaging Technology

Anthony D Yao et al. Radiol Artif Intell. .

Abstract

Purpose: To systematically review and synthesize the current literature and to develop a compendium of technical characteristics of existing deep learning applications in neuroradiology.

Materials and methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review was conducted through September 1, 2019, using PubMed, Cochrane, and Web of Science databases. A total of 155 articles discussing deep learning applications in neuroimaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics.

Results: A total of 155 studies were identified and divided into: MRI (n = 115), functional MRI (n = 19), CT (n = 9), PET (n = 18), and US (n = 1). Seven were multimodal. MRI applications were described in 74%, and 76 (49%) were tasked with image segmentation. Of the 155 articles identified in this study, 65 (42%) were tested on institutional data; only 16 were validated against publicly available data. In addition, 53 studies (34%) used a combined dataset of less than 100, and 124 (80%) used a combined dataset of less than 1000.

Conclusion: Although deep learning has demonstrated potential for each of these modalities, this review highlights several needs in the field of deep learning research including use of internal datasets without external validation, unavailability of implementation methods, inconsistent assessment metrics, and lack of clinical validation. However, the rapid growth of deep learning in neuroradiology holds promise and, as strides are made to improve standardization, generalizability, and reproducibility, it may soon play a role in clinical diagnosis and treatment of neurologic disorders.Supplemental material is available for this article.© RSNA, 2020.

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Conflict of interest statement

Disclosures of Conflicts of Interest: A.D.Y. disclosed no relevant relationships. D.L.C. disclosed no relevant relationships. I.P. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: consultant for MD.ai (relationship unrelated to present work and no funding was received from this entity for this study. Other relationships: disclosed no relevant relationships. F.K. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: employed by DASA (author’s primary employer) and Universidade Federal de São Paulo (author’s secondary employer). Other relationships: disclosed no relevant relationships.

Figures

Figure 1:
Figure 1:
Classification of artificial intelligence architectures.
Figure 2:
Figure 2:
Overview of supervised versus unsupervised learning.
Figure 3:
Figure 3:
PRISMA Guidelines for Systematic Review Flow Diagram. A = Records screened were based on brief review of the publication title or abstract; excluded papers included those that were not in English, were published before the year 2000, and did not describe deep learning research. B = Only peer-reviewed articles that evaluated deep learning modalities in neuroradiology were included. Excluded publications reviewed the technological field, provided qualitative assessment of deep learning, provided low-quality quantitative analysis per GRADE Working Group Criteria, or did not apply deep learning to solve an imaging task. Please refer to the Methods for a more detailed description. C = Secondary literature search was conducted through September 1, 2019, using identical search criteria and methodology with updated findings. GRADE = Grading of Recommendations Assessment, Development and Evaluation, PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
See this image and copyright information in PMC

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