DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy

Kelly, Brendan; Martinez, Mesha; M., Huy; Hayden, Joel; Huang, Yuhao; Yedavalli, Vivek; Ho, Chang Yueh; Keane, Pearse Andrew; Killeen, Ronan P.; Lawlor, Aonghus; Moseley, Michael E.; Yeom, Kristen W.; Lee, Edward H.

doi:10.1007/s00330-023-09478-3

Cited by 8 publications

(5 citation statements)

References 28 publications

(28 reference statements)

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“…21 It allows longitudinal analysis of imaging data, potentially enabling the AI to capture disease progression and treatment responses and even predict future outcomes. 22,23,39 Integrating temporality into AI models enhances their capacity to detect subtle, time-dependent changes in patient imaging data, which may remain undetected by conventional image analysis. 23 We aimed to build on this research because in conditions such as MS, in which the temporal evolution of lesions is a critical aspect of disease-monitoring and management, they could lead to earlier intervention and better patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

A Radiomic “Warning Sign” of Progression on Brain MRI in Individuals with MS

Kelly,

Mathur,

McGuinness

et al. 2024

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS. MATERIALS AND METHODS:This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model. RESULTS:The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications. CONCLUSIONS:Our machine learning model based on radiomics features successfully differentiated prelesions from normalappearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.

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Section: Discussionmentioning

confidence: 99%

A Radiomic “Warning Sign” of Progression on Brain MRI in Individuals with MS

Kelly,

Mathur,

McGuinness

et al. 2024

AJNR Am J Neuroradiol

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“…Management of acute stroke has been disrupted by the advent of thrombolysis 13 , thrombectomy, and more recently by artificial intelligence in diagnostic 14 and interventional radiology 15 . Despite the potential benefits of Artificial Intelligence (AI) in clinical settings, there are several barriers to its more widespread adoption 16 .…”

Section: Non Standard Abbreviationsmentioning

confidence: 99%

iSPAN: Improved prediction of outcomes post thrombectomy with Machine Learning

Kelly

Mathur

Duignan

et al. 2023

Preprint

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Background: <break>This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy.<break> <break>Methods: <break>This retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020. External validation data was obtained. The primary outcome variable was day 90 mRS 3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN and compared results.<break> <break>Results: <break>812 patients were initially included (397 female, average age 73), 62 for external validation. The best performing clinical score and ML model were SPAN and XGBoost (sensitivity specificity and accuracy 0.967, 0.290, 0.628 and 0.783, 0.693, 0.738 respectively). A significant difference was found overall and XGBoost was more accurate than SPAN (p< 0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of 2B and 3 points for 3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than XGBoost (p>0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67), respectively.<break> <break>Conclusions: iSPAN incorporates machine-derived features to achieve better predictions compared to existing scores. It is not inferior to the XGB model and is externally generalisable.

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“…[8][9][10][11][12] Previous work has attempted to predict reperfusion grading or classify the occlusion as M1 or M2 based on DSA. 13,14 An effective F I G U R E 1 Study workflow. An AP cerebral DSA frame is fed into the model, and if an LVO is present, the overall model will attempt to localize it with a bounding box.…”

Section: Introductionmentioning

confidence: 99%

“…While there are many instances of work using DL strategies to automatically identify and characterize occlusions on CTA, there has been comparably less effort in adopting similar strategies for DSA 8–12 . Previous work has attempted to predict reperfusion grading or classify the occlusion as M1 or M2 based on DSA 13,14 . An effective strategy to precisely localize vessel occlusions in DSAs for intra‐EVT assistance remains to be realized.…”

Section: Introductionmentioning

confidence: 99%

A deep learning method to identify and localize large‐vessel occlusions from cerebral digital subtraction angiography

Warman,

Warman

et al. 2024

Journal of Neuroimaging

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Background and purposeAn essential step during endovascular thrombectomy is identifying the occluded arterial vessel on a cerebral digital subtraction angiogram (DSA). We developed an algorithm that can detect and localize the position of occlusions in cerebral DSA.MethodsWe retrospectively collected cerebral DSAs from a single institution between 2018 and 2020 from 188 patients, 86 of whom suffered occlusions of the M1 and proximal M2 segments. We trained an ensemble of deep‐learning models on fewer than 60 large‐vessel occlusion (LVO)‐positive patients. We evaluated the model on an independent test set and evaluated the truth of its predicted localizations using Intersection over Union and expert review.ResultsOn an independent test set of 166 cerebral DSA frames with an LVO prevalence of 0.19, the model achieved a specificity of 0.95 (95% confidence interval [CI]: 0.90, 0.99), a precision of 0.7450 (95% CI: 0.64, 0.88), and a sensitivity of 0.76 (95% CI: 0.66, 0.91). The model correctly localized the LVO in at least one frame in 13 of the 14 LVO‐positive patients in the test set. The model achieved a precision of 0.67 (95% CI: 0.52, 0.79), recall of 0.69 (95% CI: 0.46, 0.81), and a mean average precision of 0.75 (95% CI: 0.56, 0.91).ConclusionThis work demonstrates that a deep learning strategy using a limited dataset can generate effective representations used to identify LVOs. Generating an expanded and more complete dataset of LVOs with obstructed LVOs is likely the best way to improve the model's ability to localize LVOs.

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DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy

Cited by 8 publications

References 28 publications

A Radiomic “Warning Sign” of Progression on Brain MRI in Individuals with MS

A Radiomic “Warning Sign” of Progression on Brain MRI in Individuals with MS

iSPAN: Improved prediction of outcomes post thrombectomy with Machine Learning

A deep learning method to identify and localize large‐vessel occlusions from cerebral digital subtraction angiography

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