Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study.

RATIONALE AND OBJECTIVES: Intravenous thrombolysis decision-making and obtaining of consent would be assisted by an individualized risk-benefit ratio. Deep learning (DL) models may be able to assist with this patient selection. MATERIALS AND METHODS: Clinical data regarding consecutive patients who received intravenous thrombolysis across two tertiary hospitals over a 7-year period were extracted from existing databases. The noncontrast computed tomography brain scans for these patients were then retrieved with hospital picture archiving and communication systems. Using a combination of convolutional neural networks (CNN) and artificial neural networks (ANN) several models were developed to predict either improvement in the National Institutes of Health Stroke Scale of ≥4 points at 24 hours ("NIHSS24"), or modified Rankin Scale 0-1 at 90 days ("mRS90"). The developed CNN and ANN were then applied to a test set. The THRIVE, HIAT, and SPAN-100 scores were also calculated for the patients in the test set and used to predict NIHSS24 and mRS90. RESULTS: Data from 204 individuals were included in the project. The best performing DL model for prediction of mRS90 was a combination CNN + ANN based on clinical data and computed tomography brain (accuracy = 0.74, F1 score = 0.69). The best performing model for NIHSS24 prediction was also the combination CNN + ANN (accuracy = 0.71, F1 score = 0.74). CONCLUSION: DL models may aid in the prediction of functional thrombolysis outcomes. Further investigation with larger datasets and additional imaging sequences is indicated.

Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.

The identification of high-grade glioma (HGG) progression may pose a diagnostic dilemma due to similar appearances of treatment-related changes (TRC) (e.g. pseudoprogression or radionecrosis). Deep learning (DL) may be able to assist with this task. MRI scans from consecutive patients with histologically confirmed HGG (grade 3 or 4) were reviewed. Scans for which recurrence or TRC was queried were followed up to determine whether the cases indicated recurrence/progression or TRC. Identified cases were randomly split into training and testing sets (80%/20%). Following development on the training set, classification experiments using convolutional neural networks (CNN) were then conducted using models based on each of diffusion weighted imaging (DWI - isotropic diffusion map), apparent diffusion coefficient (ADC), FLAIR and post-contrast T1 sequences. The sequence that achieved the highest accuracy on the test set was then used to develop DL models in which multiple sequences were combined. MRI scans from 55 patients were included in the study (70.1% progression/recurrence). 54.5% of the randomly allocated test set had progression/recurrence. Based upon DWI sequences the CNN achieved an accuracy of 0.73 (F1 score = 0.67). The model based on the DWI+FLAIR sequences in combination achieved an accuracy of 0.82 (F1 score = 0.86). The results of this study support similar studies that have shown that machine learning, in particular DL, may be useful in distinguishing progression/recurrence from TRC. Further studies examining the accuracy of DL models, including magnetic resonance perfusion (MRP) and magnetic resonance spectroscopy (MRS), with larger sample sizes may be beneficial.

Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations.

Background and Purpose- Triaging of referrals to transient ischemic attack (TIA) clinics is aided by risk stratification. Deep learning-based natural language processing, a type of machine learning, may be able to assist with the prediction of cerebrovascular cause of TIA-like presentations from free-text information. Methods- Consecutive TIA clinic notes were retrieved from existing databases. Texts associated with cerebrovascular and noncerebrovascular diagnoses were preprocessed before classification experiments, using a variety of classifier models, based on only the free-text description of the history of presenting complaint. The primary outcome was area under the curve (AUC) of the receiver operator curve. The model with the greatest AUC was then used in classification experiments in which it was provided with additional clinical information. Results- Of the classifier models trialed on the history of presenting complaint, the convolutional neural network achieved the greatest predictive capability (AUC±SD; 81.9±2.0). The effects of additional clinical information on AUC were variable. The greatest AUC was achieved when the convolutional neural network was provided with the history of presenting complaint and magnetic resonance imaging report (88.3±3.6). Conclusions- Deep learning-based natural language processing, in particular convolutional neural networks, based on medical free-text, may prove effective in prediction of the cause of TIA-like presentations. Future research investigating the role of the application of deep learning-based natural language processing to the automated triaging of clinic referrals in TIA, and potentially other specialty areas, is indicated.