The new era of artificial intelligence in neuroradiology: current research and promising tools / A nova era da inteligência artificial em neurorradiologia: pesquisa atual e ferramentas promissoras

Abstract Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

Resumo A radiologia tem uma série de características que a torna uma disciplina médica especialmente adequada à adoção precoce da inteligência artificial (IA), incluindo um fluxo de trabalho digital bem estabelecido, protocolos padronizados para armazenamento de imagens e inúmeras atividades interpretativas bem definidas. Tal adequação é corroborada pelos mais de 200 produtos radiológicos comerciais baseados em IA recentemente aprovados pelo Food and Drug Administration (FDA) para auxiliar os radiologistas em uma série de tarefas restritas de análise de imagens, como quantificação, triagem de fluxo de trabalho e aprimoramento da qualidade das imagens. Entretanto, para o aumento da eficácia e eficiência da IA, além de uma utilização clínica bem-sucedida dos produtos que utilizam essa tecnologia, os radiologistas devem estar atualizados com as aplicações em suas áreas específicas de atuação. Assim, neste artigo, pesquisamos na literatura existente aplicações baseadas em IA em neurorradiologia, mais especificamente em condições como doenças vasculares, epilepsia, condições desmielinizantes e neurodegenerativas. Também abordamos os principais algoritmos por trás de tais aplicações, discutimos alguns dos desafios na generalização no uso desses modelos e introduzimos as soluções comercialmente disponíveis mais relevantes adotadas na prática clínica. Se cautelosamente desenvolvidos, os algoritmos de IA têm o potencial de melhorar radicalmente a radiologia, aperfeiçoando a análise de imagens, aumentando o valor das técnicas de imagem quantitativas e mitigando erros de diagnóstico.

Quantification of the Thoracic Aorta and Detection of Aneurysm at CT: Development and Validation of a Fully Automatic Methodology.

Purpose: To develop and validate a deep learning-based system that predicts the largest ascending and descending aortic diameters at chest CT through automatic thoracic aortic segmentation and identifies aneurysms in each segment. Materials and Methods: In this retrospective study conducted from July 2019 to February 2021, a U-Net and a postprocessing algorithm for thoracic aortic segmentation and measurement were developed by using a dataset (dataset A) that included 315 CT studies split into training, hyperparameter-tuning, and testing sets. The U-Net and postprocessing algorithm were associated with a Digital Imaging and Communications in Medicine series filter and visualization interface and were further validated by using a dataset (dataset B) that included 1400 routine CT studies. In dataset B, system-predicted measurements were compared with annotations made by two independent readers as well as radiology reports to evaluate system performance. Results: In dataset B, the mean absolute error between the automatic and reader-measured diameters was equal to or less than 0.27 cm for both the ascending aorta and the descending aorta. The intraclass correlation coefficients (ICCs) were greater than 0.80 for the ascending aorta and equal to or greater than 0.70 for the descending aorta, and the ICCs between readers were 0.91 (95% CI: 0.90, 0.92) and 0.82 (95% CI: 0.80, 0.84), respectively. Aneurysm detection accuracy was 88% (95% CI: 86, 90) and 81% (95% CI: 79, 83) compared with reader 1 and 90% (95% CI: 88, 91) and 82% (95% CI: 80, 84) compared with reader 2 for the ascending aorta and descending aorta, respectively. Conclusion: Thoracic aortic aneurysms were accurately predicted at CT by using deep learning.Keywords: Aorta, Convolutional Neural Network, Machine Learning, CT, Thorax, AneurysmsSupplemental material is available for this article.© RSNA, 2022.

Longitudinal whole-brain analysis of multi-subject diffusion data in diffuse axonal injury.

BACKGROUND: Diffuse axonal injury occurs with high acceleration and deceleration forces in traumatic brain injury (TBI). This lesion leads to disarrangement of the neuronal network, which can result in some degree of deficiency. The Extended Glasgow Outcome Scale (GOS-E) is the primary outcome instrument for the evaluation of TBI victims. Diffusion tensor imaging (DTI) assesses white matter (WM) microstructure based on the displacement distribution of water molecules. OBJECTIVE: To investigate WM microstructure within the first year after TBI using DTI, the patient's clinical outcomes, and associations. METHODS: We scanned 20 moderate and severe TBI victims at 2 months and 1 year after the event. Imaging processing was done with the FMRIB software library; we used the tract-based spatial statistics software yielding fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) for statistical analyses. We computed the average difference between the two measures across subjects and performed a one-sample t-test and threshold-free cluster enhancement, using a corrected p-value < 0.05. Clinical outcomes were evaluated with the GOS-E. We tested for associations between outcome measures and significant mean FA clusters. RESULTS: Significant clusters of altered FA were identified anatomically using the JHU WM atlas. We found increasing spotted areas of FA with time in the right brain hemisphere and left cerebellum. Extensive regions of increased MD, RD, and AD were observed. Patients presented an excellent overall recovery. CONCLUSIONS: There were no associations between FA and outcome scores, but we cannot exclude the existence of a small to moderate association.

Longitudinal assessment of magnetization transfer ratio, brain volume, and cognitive functions in diffuse axonal injury.

BACKGROUND: Diffuse axonal injury (DAI) is a frequent mechanism of traumatic brain injury (TBI) that triggers a sequence of parenchymal changes that progresses from focal axonal shear injuries up to inflammatory response and delayed axonal disconnection. OBJECTIVE: The main purpose of this study is to evaluate changes in the axonal/myelinic content and the brain volume up to 12 months after TBI and to correlate these changes with neuropsychological results. METHODS: Patients with DAI (n = 25) were scanned at three time points after trauma (2, 6, and 12 months), and the total brain volume (TBV), gray matter volume, and white matter volume (WMV) were calculated in each time point. The magnetization transfer ratio (MTR) for the total brain (TB MTR), gray matter (GM MTR), and white matter (WM MTR) was also quantified. In addition, Hopkins verbal learning test (HVLT), Trail Making Test (TMT), and Rey-Osterrieth Complex Figure test were performed at 6 and 12 months after the trauma. RESULTS: There was a significant reduction in the mean TBV, WMV, TB MTR, GM MTR, and WM MTR between time points 1 and 3 (p < .05). There was also a significant difference in HVLT-immediate, TMT-A, and TMT-B scores between time points 2 and 3. The MTR decline correlated more with the cognitive dysfunction than the volume reduction. CONCLUSION: A progressive axonal/myelinic rarefaction and volume loss were characterized, especially in the white matter (WM) up to 1 year after the trauma. Despite that, specific neuropsychological tests revealed that patients' episodic verbal memory, attention, and executive function improved during the study. The current findings may be valuable in developing long-term TBI rehabilitation management programs.

DTI-derived parameters differ between moderate and severe traumatic brain injury and its association with psychiatric scores.

BACKGROUND AND AIM: Diffusion tensor imaging (DTI) parameters in the corpus callosum have been suggested to be a biomarker for prognostic outcomes in individuals with diffuse axonal injury (DAI). However, differences between the DTI parameters on moderate and severe trauma in DAI over time are still unclear. A secondary goal was to study the association between the changes in the DTI parameters, anxiety, and depressive scores in DAI over time. METHODS: Twenty subjects were recruited from a neurological outpatient clinic and evaluated at 2, 6, and 12 months after the brain injury and compared to matched age and sex healthy controls regarding the DTI parameters in the corpus callosum. State-Trace Anxiety Inventory and Beck Depression Inventory were used to assess psychiatric outcomes in the TBI group over time. RESULTS: Differences were observed in the fractional anisotropy and mean diffusivity of the genu, body, and splenium of the corpus callosum between DAI and controls (p < 0.02). Differences in both parameters in the genu of the corpus callosum were also detected between patients with moderate and severe DAI (p < 0.05). There was an increase in the mean diffusivity values and the fractional anisotropy decrease in the DAI group over time (p < 0.02). There was no significant correlation between changes in the fractional anisotropy and mean diffusivity across the study and psychiatric outcomes in DAI. CONCLUSION: DTI parameters, specifically the mean diffusivity in the corpus callosum, may provide reliable characterization and quantification of differences determined by the brain injury severity. No correlation was observed with DAI parameters and the psychiatric outcome scores.