Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging

Bridge, Christopher P.; Bizzo, Bernardo C.; Hillis, James; Chin, John K.; Comeau, Donnella; Gauriau, Romane; Macruz, Fabíola Bezerra de Carvalho; Pawar, Jayashri; Noro, Flavia T C; Sharaf, Elshaimaa; Takahashi, Marcelo Straus; Wright, Bradley M.; Kalafut, John F.; Andriole, Katherine P.; Pomerantz, Stuart R.; Pedemonte, Stefano; González, Ramón

doi:10.1038/s41598-022-06021-0

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…The DAGMNet model resulted better performance than UNet, with higher Dice index scores of 0.74 and higher precision of 0.76. Bridge et al [ 149 ] used a deep learning model trained on 6657 DWI sequences could segment the infarcts with Dice coefficient 0.776 [ 149 ]. Chang et al [ 150 ] designed a customized deep learning approach, a hybrid 3D/2D based CNN network for hemorrhagic evaluation in CT images, and quantified the hemorrhagic lesions on NCCT images with Dice score of high accuracy 0.93.…”

Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Subudhi

Dash²,

Mohapatra

et al. 2022

Diagnostics

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Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70–90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.

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Section: Resultsmentioning

confidence: 99%

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Subudhi

Dash²,

Mohapatra

et al. 2022

Diagnostics

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“…Previous DL-based approach on ischemic stroke used mostly brain MR image 4,6,[26][27][28][29][30][31][32][33][34][35] , and some used CT perfusion [36][37][38] , CT angiography 39 , and ASPECTS calculation software 40 . Among the eight studies using NCCT 24,[40][41][42][43][44][45][46] , seven studies used deep learning to detect ischemic lesions but did not mention when the NCCT was taken after stroke onset and whether infratentorial and supratentorial ischemic lesions were included for analysis 24,[40][41][42][43][44]46 .…”

Section: Discussionmentioning

confidence: 99%

Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach

Sahoo

Mohapatra

et al. 2022

Sci Rep

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Early ischemic lesion on non-contrast computed tomogram (NCCT) in acute stroke can be subtle and need confirmation with magnetic resonance (MR) image for treatment decision-making. We retrospectively included the NCCT slices of 129 normal subjects and 546 ischemic stroke patients (onset < 12 h) with corresponding MR slices as reference standard from a prospective registry of Chang Gung Research Databank. In model selection, NCCT slices were preprocessed and fed into five different pre-trained convolutional neural network (CNN) models including Visual Geometry Group 16 (VGG16), Residual Networks 50, Inception-ResNet-v2, Inception-v3, and Inception-v4. In model derivation, the customized-VGG16 model could achieve an accuracy of 0.83, sensitivity 0.85, F-score 0.80, specificity 0.82, and AP 0.82 after using a tenfold cross-validation method, outperforming the pre-trained VGG16 model. In model evaluation, the customized-VGG16 model could correctly identify 53 in 58 subjects (91.37%) including 29 ischemic stroke patients and 24 normal subjects and reached the sensitivity of 86.95% in identifying ischemic NCCT slices (200/230), irrespective of supratentorial or infratentorial lesions. The customized-VGG16 CNN model can successfully identify the presence of early ischemic lesions on NCCT slices using the concept of automatic feature learning. Further study will be proceeded to detect the location of ischemic lesion.

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“…While CT images have been used mainly for hemorrhage identification, LVO detection, and automated ASPECT calculation, MRI images have been used primarily for automatic core volume estimation on DWI images, evaluation of penumbra, and to predict the final ischemic stroke lesions from initial MRI, stroke symptom onset and hemorrhagic transformation. [34][35][36][37][38][39][40][41] AI has also been used to improve image quality and speed acquisition -since time delays associated with brain scanning is a real constrainfor stroke risk prediction, for analysis of cerebral edema, and to evaluate treatment effect. [42][43][44] Except for stroke, to the best of our knowledge, a small number of essays discuss the computer-assisted analysis of other ischemic CVDs such AS and MMD, and for these abnormalities, models tend not to use medical pictures as input.…”

Section: Ischemic Cerebrovascular Diseasesmentioning

confidence: 99%

The new era of artificial intelligence in neuroradiology: current research and promising tools

Carvalho Macruz,

Dias,

Andrade

et al. 2024

Arq Neuropsiquiatr

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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.

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Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging

Cited by 8 publications

References 38 publications

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach

The new era of artificial intelligence in neuroradiology: current research and promising tools

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