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

Abstract

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.

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

“…The most used MRI-sequence was FLAIR, T2, T1, and DWI. Two studies utilised functional MRI (fMRI) sequences for assessment [ 42 , 51 ] and one used time-of-flight [ 34 ]. The comparators used in the studies were heterogeneous.…”

“…None of the included studies reported to follow the MI-CLAIM checklist, although 17 studies were published in the years after the release of the MI-CLAIM paper from 2020 [ 21 ]. Only two studies [ 32 , 34 ] claimed to follow a reporting standard which was the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guideline [ 65 ] and one of those studies [ 32 ] additionally followed the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) [ 66 ]. The total percentage of reported items was 72%.…”

“…To reduce heterogeneity among the low-risk-bias studies, Yang et al [34], Dørum et al [42], and Uchiyama et al [46] were excluded from the meta-analyses since these studies did not use DWI sequence to detect acute ischaemic stroke lesions. Wu et al [35] were excluded due to insufficient reporting.…”

The novel imaging methods in diagnosis and assessment of cerebrovascular diseases: an overview

“…The integration of artificial intelligence algorithms with imaging technologies has the potential to significantly improve the diagnosis and assessment of cerebrovascular diseases ( 126 ). AI algorithms can analyze large amounts of imaging data, identify subtle abnormalities, and assist in the interpretation of images ( 127 ).…”

Fabry–Perot Interferometric Fiber-Optic Sensor for Rapid and Accurate Thrombus Detection

“…Various techniques have been employed to monitor coagulation, including acoustic methods [ 13 , 14 ], Raman scattering spectroscopy [ 14 , 15 ], colorimetric analysis [ 16 ], deep learning algorithms [ 17 ], and near-infrared fluorescence imaging [ 18 ]. However, fiber-optic sensors are a particularly effective approach as they allow quick and real-time monitoring of thrombin [ 19 , 20 , 21 ].…”