Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Abstract

Background It is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images’ lack of spatial information. Objective The aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection. Methods We proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks. Results A total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively. Conclusions According to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.

Artificial Intelligence Innovations in Cerebrovascular Neurosurgery: A Systematic Review of Cutting-edge Applications

“…There are 20 studies which deals with application of Arti cial Intelligence and machine learning models in modalities like detection, outcome prediction and risk prediction associated with Acute Ischemic Stroke [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], Table 3.…”

“…Results show that the proposed DSU-Net outperforms the baseline U-Net and numerous state-of-the-art models for clinical practice and offers a fresh strategy for HMCAS automatic segmentation. JunHua Liao et al compared the performance in aneurysm detection of the developed ML model with that of 3 existing frameworks, ndings suggest the performance of the frameworks can be enhanced by adding more geographical and temporal information [21]. As a result, when compared to the other frameworks, the bi-input+RetinaNet+C-LSTM framework performed the best.…”

“…Machine learning models have been developed and shown promising outcomes in classifying stenosis, diagnosing moyamoya disease, brain arteriovenous malformations, cerebrovascular thrombosis [53], [55], [57], [58]. Various neural networks can aid in accurate and quick diagnosis & segmentation of cerebral aneurysms, a task which can be labour intensive if done manually [21], [31]. Its use has also been explored in the treatment and detection of periprocedural complications of ICA [38], [46], Fig.…”

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

“…According to their research, the best accuracy was achieved by the Unit-2 architecture. Moradi et al [40] also applied UNet and feature pyramid network (FPN) [41] on the CAMUS [29] dataset for LV segmentation. FPN was applied for feature extraction and used a pyramid concept with improved accuracy and speed.…”

A Deep Learning Based on Automatic Cerebral Aneurysm Detection in Brain Computed Tomography Angiography Scan Images

“…Convolutional Neural Network (CNN) based architecture was applied to detect intracranial aneurysms in Digital Subtraction Angiography (DSA) images, achieving 93.5% accuracy [12]. Posterior communicating artery aneurysms were detected in 2D DSA images with 91% accuracy by deep learning diagnosis system [13]. Similarly, 92.82% sensitivity was achieved by using U-Net structure to detect intracranial aneurysms in DSA images [14].…”