Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging

Cao, Chen; Liu, Zhiyang; Li, Guohua; Song, Junqiang; Xia, Shuang

doi:10.21037/qims-21-324

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…All patients were divided into SPs and ASPs groups. The detailed criteria for the diagnosis of SPs are: patients with regions of ADC <620 × 10 −6 mm 2 /s (CST-ADC) ( 17 ) or Tmax >6 s mismatch volume (penumbra volume–infarct volume) of 15 ml or more ( 18 ). The rest of the carotid plaque is ASPs.…”

Section: Methodsmentioning

confidence: 99%

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Gui

Cao

Zhang

et al. 2023

Front. Cardiovasc. Med.

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ObjectiveIn this study, we aimed to investigate the classification of symptomatic plaques by evaluating the models generated via two different approaches, a radiomics-based machine learning (ML) approach, and an end-to-end learning approach which utilized deep learning (DL) techniques with several representative model frameworks.MethodsWe collected high-resolution magnetic resonance imaging (HRMRI) data from 104 patients with carotid artery stenosis, who were diagnosed with either symptomatic plaques (SPs) or asymptomatic plaques (ASPs), in two medical centers. 74 patients were diagnosed with SPs and 30 patients were ASPs. Sampling Perfection with Application-optimized Contrasts (SPACE) by using different flip angle Evolutions was used for MRI imaging. Repeated stratified five-fold cross-validation was used to evaluate the accuracy and receiver operating characteristic (ROC) of the trained classifier. The two proposed approaches were investigated to train the models separately. The difference in the model performance of the two proposed methods was quantitatively evaluated to find a better model to differentiate between SPs and ASPs.Results3D-SE-Densenet-121 model showed the best performance among all prediction models (AUC, accuracy, precision, sensitivity, and F1-score of 0.9300, 0.9308, 0.9008, 0.8588, and 0.8614, respectively), which were 0.0689, 0.1119, 0.1043, 0.0805, and 0.1089 higher than the best radiomics-based ML model (MLP). Decision curve analysis showed that the 3D-SE-Densenet-121 model delivered more net benefit than the best radiomics-based ML model (MLP) with a wider threshold probability.ConclusionThe DL models were able to accurately differentiate between symptomatic and asymptomatic carotid plaques with limited data, which outperformed radiomics-based ML models in identifying symptomatic plaques.

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Section: Methodsmentioning

confidence: 99%

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Gui

Cao

Zhang

et al. 2023

Front. Cardiovasc. Med.

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“…In T2WI, fatty tissues appear dark, and fluid or water-containing tissues appear bright [52]. The gradient-echo T2* (GRE T2*wi) sequence is a common additional MRI sequence that gives a correct assessment of hemorrhagic alteration [56,57]. Susceptibility-weighted imaging (SWI) sequences are akin to the GRE T2*wi sequence, but has better sensitivity to detect hemorrhages.…”

Section: Mri In Ischemic Strokementioning

confidence: 99%

Neuroimaging Modalities Used for Ischemic Stroke Diagnosis and Monitoring

Nukovic,

Opancina,

Ciceri

et al. 2023

Medicina

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Strokes are one of the global leading causes of physical or mental impairment and fatality, classified into hemorrhagic and ischemic strokes. Ischemic strokes happen when a thrombus blocks or plugs an artery and interrupts or reduces blood supply to the brain tissue. Deciding on the imaging modality which will be used for stroke detection depends on the expertise and availability of staff and the infrastructure of hospitals. Magnetic resonance imaging provides valuable information, and its sensitivity for smaller infarcts is greater, while computed tomography is more extensively used, since it can promptly exclude acute cerebral hemorrhages and is more favorable speed-wise. The aim of this article was to give information about the neuroimaging modalities used for the diagnosis and monitoring of ischemic strokes. We reviewed the available literature and presented the use of computed tomography, CT angiography, CT perfusion, magnetic resonance imaging, MR angiography and MR perfusion for the detection of ischemic strokes and their monitoring in different phases of stroke development.

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“…6,7 Deep learning (DL) is a branch of machine learning that has made great advances in diagnosing stroke by capturing morphological structures and texture features from images. 8,9 Thus, we hypothesize that DL can provide information on imaging features which were overlooked and improve the diagnostic ability of routine brain MRI. To the best our knowledge, no studies have been conducted to apply DL algorithm of routine brain MRI in the diagnosis of CVT.…”

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confidence: 99%

Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging

Zhang

et al. 2023

Stroke

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Background: Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep learning (DL) algorithm for detecting CVT using routine brain magnetic resonance imaging. Methods: Routine brain magnetic resonance imaging, including T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images of patients suspected of CVT from April 2014 through December 2019 who were enrolled from a CVT registry, were collected. The images were divided into 2 data sets: a development set and a test set. Different DL algorithms were constructed in the development set using 5-fold cross-validation. Four radiologists with various levels of expertise independently read the images and performed diagnosis within the test set. The diagnostic performance on per-patient and per-segment diagnosis levels of the DL algorithms and radiologist’s assessment were evaluated and compared. Results: A total of 392 patients, including 294 patients with CVT (37±14 years, 151 women) and 98 patients without CVT (42±15 years, 65 women), were enrolled. Of these, 100 patients (50 CVT and 50 non-CVT) were randomly assigned to the test set, and the other 292 patients comprised the development set. In the test set, the optimal DL algorithm (multisequence multitask deep learning algorithm) achieved an area under the curve of 0.96, with a sensitivity of 96% (48/50) and a specificity of 88% (44/50) on per-patient diagnosis level, as well as a sensitivity of 88% (129/146) and a specificity of 80% (521/654) on per-segment diagnosis level. Compared with 4 radiologists, multisequence multitask deep learning algorithm showed higher sensitivity both on per-patient (all P <0.05) and per-segment diagnosis levels (all P <0.001). Conclusions: The CVT-detected DL algorithm herein improved diagnostic performance of routine brain magnetic resonance imaging, with high sensitivity and specificity, which provides a promising approach for detecting CVT.

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Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging

Cited by 4 publications

References 18 publications

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques

Neuroimaging Modalities Used for Ischemic Stroke Diagnosis and Monitoring

Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging

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