A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images

Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions

BACKGROUND

Cerebral aneurysms pose a significant risk to patient safety, particularly when ruptured, emphasizing the need for early detection and accurate prediction. Traditional diagnostic methods, reliant on clinician-based evaluations, face challenges in sensitivity and consistency, prompting the exploration of deep learning (DL) systems for improved performance.

METHODS

This systematic review and meta-analysis assessed the performance of DL models in detecting and predicting intracranial aneurysms compared to clinician-based evaluations. Imaging modalities included CT angiography (CTA), digital subtraction angiography (DSA), and time-of-flight MR angiography (TOF-MRA). Data on lesion-wise sensitivity, specificity, and the impact of DL assistance on clinician performance were analyzed. Subgroup analyses evaluated DL sensitivity by aneurysm size and location, and interrater agreement was measured using Fleiss' κ.

RESULTS

DL systems achieved an overall lesion-wise sensitivity of 90 % and specificity of 94 %, outperforming human diagnostics. Clinician specificity improved significantly with DL assistance, increasing from 83 % to 85 % in the patient-wise scenario and from 93 % to 95 % in the lesion-wise scenario. Similarly, clinician sensitivity also showed notable improvement with DL assistance, rising from 82 % to 96 % in the patient-wise scenario and from 82 % to 88 % in the lesion-wise scenario. Subgroup analysis showed DL sensitivity varied with aneurysm size and location, reaching 100 % for aneurysms larger than 10 mm. Additionally, DL assistance improved interrater agreement among clinicians, with Fleiss' κ increasing from 0.668 to 0.862.

CONCLUSIONS

DL models demonstrate transformative potential in managing cerebral aneurysms by enhancing diagnostic accuracy, reducing missed cases, and supporting clinical decision-making. However, further validation in diverse clinical settings and seamless integration into standard workflows are necessary to fully realize the benefits of DL-driven diagnostics.

PlaNet-S: an Automatic Semantic Segmentation Model for Placenta Using U-Net and SegNeXt

This study aimed to develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. A total of 218 pregnant women with suspected placental abnormalities who underwent magnetic resonance imaging (MRI) were enrolled, yielding 1090 annotated images for developing a deep learning model for placental segmentation. The images were standardized and divided into training and test sets. The performance of Placental Segmentation Network (PlaNet-S), which integrates U-Net and SegNeXt within an ensemble framework, was assessed using Intersection over Union (IoU) and counting connected components (CCC) against the U-Net, U-Net + + , and DS-transUNet. PlaNet-S had significantly higher IoU (0.78, SD = 0.10) than that of U-Net (0.73, SD = 0.13) (p < 0.005) and DS-transUNet (0.64, SD = 0.16) (p < 0.005), while the difference with U-Net + + (0.77, SD = 0.12) was not statistically significant. The CCC for PlaNet-S was significantly higher than that for U-Net (p < 0.005), U-Net + + (p < 0.005), and DS-transUNet (p < 0.005), matching the ground truth in 86.0%, 56.7%, 67.9%, and 20.9% of the cases, respectively. PlaNet-S achieved higher IoU than U-Net and DS-transUNet, and comparable IoU to U-Net + + . Moreover, PlaNet-S significantly outperformed all three models in CCC, indicating better agreement with the ground truth. This model addresses the challenges of time-consuming physician-assisted manual segmentation and offers the potential for diverse applications in placental imaging analyses.

Deep learning-based system for automatic identification of benign and malignant eyelid tumours

AIMS

Our aim is to develop a deep learning-based system for automatically identifying and classifying benign and malignant tumours of the eyelid to improve diagnostic accuracy and efficiency.

METHODS

The dataset includes photographs of normal eyelids, benign and malignant eyelid tumours and was randomly divided into a training and validation dataset in a ratio of 8:2. We used the training dataset to train eight convolutional neural network models to classify normal eyelids, benign and malignant eyelid tumours. These models included VGG16, ResNet50, Inception-v4, EfficientNet-V2-M and their variants. The validation dataset was used to evaluate and compare the performance of the different deep learning models.

RESULTS

All eight models achieved an average accuracy greater than 0.746 for identifying normal eyelids, benign and malignant eyelid tumours, with an average sensitivity and specificity exceeding 0.790 and 0.866, respectively. The mean area under the receiver operating characteristic curve (AUC) for the eight models was more than 0.904 in correctly identifying normal eyelids, benign and malignant eyelid tumours. The dual-path Inception-v4 network demonstrated the highest performance, with an AUC of 0.930 (95% CI 0.900 to 0.954) and an F1-score of 0.838 (95% CI 0.787 to 0.882).

CONCLUSION

The deep learning-based system shows significant potential in improving the diagnosis of eyelid tumours, providing a reliable and efficient tool for clinical practice. Future work will validate the model with more extensive and diverse datasets and integrate it into clinical workflows for real-time diagnostic support.

PMMNet: A Dual Branch Fusion Network of Point Cloud and Multi-View for Intracranial Aneurysm Classification and Segmentation

Intracranial aneurysm (IA) is a vascular disease of the brain arteries caused by pathological vascular dilation, which can result in subarachnoid hemorrhage if ruptured. Automatically classification and segmentation of intracranial aneurysms are essential for their diagnosis and treatment. However, the majority of current research is focused on two-dimensional images, ignoring the 3D spatial information that is also critical. In this work, we propose a novel dual-branch fusion network called the Point Cloud and Multi-View Medical Neural Network (PMMNet) for IA classification and segmentation. Specifically, one branch based on 3D point clouds serves the purpose of extracting spatial features, whereas the other branch based on multi-view images acquires 2D pixel features. Ultimately, the two types of features are fused for IA classification and segmentation. To extract both local and global features from 3D point clouds, Multilayer Perceptron (MLP) and the attention mechanism are used in parallel. In addition, a SPSA module is proposed for multi-view image feature learning, which extracts more exquisite channel and spatial multi-scale features from 2D images. Experiments conducted on the IntrA dataset outperform other state-of-the-art methods, demonstrating that the proposed PMMNet exhibits strong superiority on the medical 3D dataset. We also obtain competitive results on public datasets, including ModelNet40, ModelNet10, and ShapeNetPart, which further validate the robustness and generality of the PMMNet.

Development and Validation of a Sham-AI Model for Intracranial Aneurysm Detection at CT Angiography

Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was conducted from November 2022 to March 2023. A sham-AI model with near-zero sensitivity and similar specificity to a standard AI model was developed using 16 422 CT angiography examinations. Digital subtraction angiography-verified CT angiographic examinations from four hospitals were collected, half of which were processed by standard AI and the others by sham AI to generate sequence A; sequence B was generated in the reverse order. Twenty-eight radiologists from seven hospitals were randomly assigned to either sequence and then assigned to the other sequence after a washout period. The diagnostic performances of radiologists alone, radiologists with standard-AI assistance, and radiologists with sham-AI assistance were compared using sensitivity and specificity, and radiologists' susceptibility to sham AI suggestions was assessed. Results The testing dataset included 300 patients (median age, 61.0 years [IQR, 52.0-67.0]; 199 male), 50 of whom had aneurysms. Standard AI and sham AI performed as expected (sensitivity, 96.0% vs 0.0%; specificity, 82.0% vs 76.0%). The differences in sensitivity and specificity between standard AI-assisted and sham AI-assisted readings were 20.7% (95% CI: 15.8, 25.5 [superiority]) and 0.0% (95% CI: -2.0, 2.0 [noninferiority]), respectively. The difference between sham AI-assisted readings and radiologists alone was -2.6% (95% CI: -3.8, -1.4 [noninferiority]) for both sensitivity and specificity. After sham-AI suggestions, 5.3% (44 of 823) of true-positive and 1.2% (seven of 577) of false-negative results of radiologists alone were changed. Conclusion Radiologists' diagnostic performance was not compromised when aided by the proposed sham-AI model compared with their unassisted performance. Keywords: CT Angiography, Vascular, Intracranial Aneurysm, Sham AI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Mayfield and Romero in this issue.

A survey of intracranial aneurysm detection and segmentation

Intracranial aneurysms (IAs) are a critical public health concern: they are asymptomatic and can lead to fatal subarachnoid hemorrhage in case of rupture. Neuroradiologists rely on advanced imaging techniques to identify aneurysms in a patient and consider the characteristics of IAs along with several other patient-related factors for rupture risk assessment and treatment decision-making. The process of diagnostic image reading is time-intensive and prone to inter- and intra-individual variations, so researchers have proposed many computer-aided diagnosis (CAD) systems for aneurysm detection and segmentation. This paper provides a comprehensive literature survey of semi-automated and automated approaches for IA detection and segmentation and proposes a taxonomy to classify the approaches. We also discuss the current issues and give some insight into the future direction of CAD systems for IA detection and segmentation.

Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit

PURPOSE

We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine clinical settings.

METHODS

Two distinct cohorts of head CT angiography (CTA) data were assembled to validate an AI model. Cohort 1, comprising gold-standard consecutive CTA cases, was used in an MRMC study involving six board-certified general radiologists. Cohort 2, representing clinical CTA cases, was used to simulate a routine clinical setting. Following these evaluations, an algorithmic audit was conducted to identify any unusual or unexpected behaviors exhibited by the model.

RESULTS

Cohort 1 consisted of 131 CTA cases, while Cohort 2 included 515 CTA cases. In the MRMC study, the AI-assisted strategy demonstrated a significant improvement in aneurysm diagnostic performance, with the area under the receiver operating characteristic curve increasing from 0.815 (95%CI: 0.754-0.875) to 0.875 (95%CI: 0.831-0.921; p = 0.008). In the AI-based first-reader study, 60.4% of the CTA cases were identified as negative by the AI, with a high negative predictive value of 0.994 (95%CI: 0.977-0.999). The algorithmic audit highlighted two issues for improvement: the accurate detection of tiny aneurysms and the effective exclusion of false-positive lesions.

CONCLUSION

This study highlights the clinical utility of a high-performance AI model in detecting IAs, significantly improving general radiologists' diagnostic performance with the potential to reduce their workload in routine clinical practice. The algorithmic audit offers insights to guide the development and validation of future AI models.

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography

PURPOSE

To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when interpreting AI-detected cerebral aneurysm findings in time-of-flight magnetic resonance angiography (TOF-MRA) studies.

MATERIAL AND METHODS

Nine radiologists with varying levels of experience evaluated twenty TOF-MRA examinations for the presence of cerebral aneurysms. Every case was evaluated with and without assistance by the AI software © mdbrain, with a washout period of at least four weeks in-between. Half of the cases included at least one false-positive AI finding. Aneurysm ratings, follow-up recommendations, and reading times were assessed using the Wilcoxon signed-rank test.

RESULTS

False-positive AI results led to significantly higher suspicion of aneurysm findings (p = 0.01). Inexperienced readers further recommended significantly more intense follow-up examinations when presented with false-positive AI findings (p = 0.005). Reading times were significantly shorter with AI assistance in inexperienced (164.1 vs 228.2 s; p < 0.001), moderately experienced (126.2 vs 156.5 s; p < 0.009), and very experienced (117.9 vs 153.5 s; p < 0.001) readers alike.

CONCLUSION

Our results demonstrate the susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. While AI systems for cerebral aneurysm detection can provide benefits, challenges in human-AI interaction need to be mitigated to ensure safe and effective adoption.

Artificial intelligence in emergency neuroradiology: Current applications and perspectives

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brain, head and neck, and spine. This narrative review aims at providing an up-to-date discussion about the state of the art of applications of artificial intelligence in emergency neuroradiology, which have substantially expanded in depth and scope in the past few years. A detailed analysis of machine learning and deep learning algorithms in several tasks related to acute ischemic stroke involving various imaging modalities, including a description of existing commercial products, is provided. The applications of artificial intelligence in acute intracranial hemorrhage and other vascular pathologies such as intracranial aneurysm and arteriovenous malformation are discussed. Other areas of emergency neuroradiology including infection, fracture, cord compression, and pediatric imaging are further discussed in turn. Based on these discussions, this article offers insight into practical considerations regarding the applications of artificial intelligence in emergency neuroradiology, calling for more development driven by clinical needs, attention to pediatric neuroimaging, and analysis of real-world performance.

The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection

Computed tomography pulmonary angiography (CTPA) is an essential diagnostic tool for identifying pulmonary embolism (PE). The integration of AI has significantly advanced CTPA-based PE detection, enhancing diagnostic accuracy and efficiency. This review investigates the growing role of AI in the diagnosis of pulmonary embolism using CTPA imaging. The review examines the capabilities of AI algorithms, particularly deep learning models, in analyzing CTPA images for PE detection. It assesses their sensitivity and specificity compared to human radiologists. AI systems, using large datasets and complex neural networks, demonstrate remarkable proficiency in identifying subtle signs of PE, aiding clinicians in timely and accurate diagnosis. In addition, AI-powered CTPA analysis shows promise in risk stratification, prognosis prediction, and treatment optimization for PE patients. Automated image interpretation and quantitative analysis facilitate rapid triage of suspected cases, enabling prompt intervention and reducing diagnostic delays. Despite these advancements, several limitations remain, including algorithm bias, interpretability issues, and the necessity for rigorous validation, which hinder widespread adoption in clinical practice. Furthermore, integrating AI into existing healthcare systems requires careful consideration of regulatory, ethical, and legal implications. In conclusion, AI-driven CTPA-based PE detection presents unprecedented opportunities to enhance diagnostic precision and efficiency. However, addressing the associated limitations is critical for safe and effective implementation in routine clinical practice. Successful utilization of AI in revolutionizing PE care necessitates close collaboration among researchers, medical professionals, and regulatory organizations.

Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging

Unruptured intracranial aneurysms are protuberances that appear in cerebral arteries, and their diagnostic evaluation can be a complex, time-consuming, and exhaustive task. In recent years, computer-aided systems have been developed to improve diagnostic processes. Although the proposed methods have already been reviewed to assess their suitability for clinical use, the segmentation methods have not been reviewed in detail, nor has there been a standardized way to compare segmentation and detection tasks. A systematic review was conducted to examine the technical and methodological factors contributing to this limitation. The analysis encompassed 49 studies conducted between 2019 and 2023 that utilized artificial intelligence methods and any medical imaging modality for the detection or segmentation of intracranial aneurysms. Most of the included studies focused exclusively on detection (57%), magnetic resonance angiography was the predominant imaging modality (47%), and the methodologies generally used 3D imaging as the input (71%). The reported sensitivities ranged from 0.68 to 0.90, specificities from 0.18 to 1.0, false positives per case from 0.18 to 13.8, and the Dice similarity coefficient from 0.53 to 0.98. Variations in aneurysm size were found to have a substantial impact on system performance. Studies were evaluated using a diagnostic accuracy study quality assessment tool, which revealed significant concerns regarding applicability. These concerns primarily stem from the poor reproducibility and inconsistent reporting of metrics. Recommendations for reporting outcomes were made to compare procedures across different types of imaging and tasks.

Enhancing Radiologists' Performance in Detecting Cerebral Aneurysms Using a Deep Learning Model: A Multicenter Study

RATIONALE AND OBJECTIVES

This study aimed to develop a deep learning (DL)-based model for detecting and diagnosing cerebral aneurysms in clinical settings, with and without human assistance.

MATERIALS AND METHODS

The DL model was trained using data from 3829 patients across 11 clinical centers and tested on 484 patients from three institutions. Image interpretations were conducted by 10 radiologists (four junior, six senior), the DL model alone, and a combination of radiologists with the DL model. Time spent on post-processing and reading was recorded. The analysis of the area under the curve (AUC), sensitivity, and specificity for the above-mentioned three reading modes was performed at both the lesion and patient levels.

RESULTS

Combining the DL model with radiologists reduced image interpretation time by 37.2% and post-processing time by 90.8%. With DL model assistance, the AUC increased from 0.842 to 0.881 (P = 0.008) for junior radiologists (JRs) and from 0.853 to 0.895 (P < 0.001) for senior radiologists (SRs). With DL model assistance, sensitivity significantly improved at both lesion (JR: 68.9% to 81.6%, P = 0.011; SR: 72.4% to 83.5%, P < 0.001) and patient levels (JR: 76.2% to 86.9%, P = 0.011; SR: 80.1% to 88.2%, P < 0.001). Specificity at the patient level showed improvement (JR: 82.6% to 82.7%, P = 0.005; SR: 82.6% to 86.1%, P = 0.021).

CONCLUSIONS

The DL model enhanced radiologists' diagnostic performance in detecting cerebral aneurysms, especially for JRs, and expedited the workflow.

AASeg: Artery-Aware Global-to-Local Framework for Aneurysm Segmentation in Head and Neck CTA Images

Aneurysm segmentation in computed tomography angiography (CTA) images is essential for medical intervention aimed at preventing subarachnoid hemorrhages. However, most existing studies tend to overlook the topological characteristics of arteries related to aneurysms, often resulting in suboptimal performance in aneurysm segmentation. To address this challenge, we propose an artery-aware global-to-local framework for aneurysm segmentation (AASeg) using CTA images of head and neck. This framework consists of two key components: 1) a centerline graph network (CG-Net) for aneurysm global localization, and 2) a point cloud network (PC-Net) for local aneurysm segmentation. The centerline graph is generated by extracting artery centerline structures from vessel masks obtained through a pre-trained model for head and neck vessel segmentation. This representation serves as a high-level representation of the artery structure, allowing for analysis of aneurysms along the entire arteries. It facilitates aneurysm localization via aneurysm-segment graph classification along the arteries. Then, local region of aneurysm segment can be sampled from the vessel mask according to the aneurysm-segment graph. Subsequently, aneurysm segmentation is performed on the point cloud constructed from the aneurysm segment through the PC-Net. Extensive experiments show that the proposed framework achieves state-of-the-art performance in aneurysm localization on a main dataset and an external testing dataset, with Recall of 84.1% and 80.7%, false positives per case of 1.72 and 1.69, and segmentation DSC of 66.1% and 60.2%, respectively.

Intracranial Aneurysm Segmentation with a Dual-Path Fusion Network

Intracranial aneurysms (IAs), a significant medical concern due to their prevalence and life-threatening nature, pose challenges regarding diagnosis owing to their diminutive and variable morphology. There are currently challenges surrounding automating the segmentation of IAs, which is essential for diagnostic precision. Existing deep learning methods in IAs segmentation tend to emphasize semantic features at the expense of detailed information, potentially compromising segmentation quality. Our research introduces the innovative Dual-Path Fusion Network (DPF-Net), an advanced deep learning architecture crafted to refine IAs segmentation by adeptly incorporating detailed information. DPF-Net, with its unique resolution-preserving detail branch, ensures minimal loss of detail during feature extraction, while its cross-fusion module effectively promotes the connection of semantic information and finer detail features, enhancing segmentation precision. The network also integrates a detail aggregation module for effective fusion of multi-scale detail features. A view fusion strategy is employed to address spatial disruptions in patch generation, thereby improving feature extraction efficiency. Evaluated on the CADA dataset, DPF-Net achieves a remarkable mean Dice similarity coefficient (DSC) of 0.8967, highlighting its potential in automated IAs diagnosis in clinical settings. Furthermore, DPF-Net's outstanding performance on the BraTS 2020 MRI dataset for brain tumor segmentation with a mean DSC of 0.8535 further confirms its robustness and generalizability.

Reducing the workload of medical diagnosis through artificial intelligence: A narrative review

Artificial intelligence (AI) has revolutionized medical diagnostics by enhancing efficiency, improving accuracy, and reducing variability. By alleviating the workload of medical staff, AI addresses challenges such as increasing diagnostic demands, workforce shortages, and reliance on subjective interpretation. This review examines the role of AI in reducing diagnostic workload and enhancing efficiency across medical fields from January 2019 to February 2024, identifying limitations and areas for improvement. A comprehensive PubMed search using the keywords "artificial intelligence" or "AI," "efficiency" or "workload," and "patient" or "clinical" identified 2587 articles, of which 51 were reviewed. These studies analyzed the impact of AI on radiology, pathology, and other specialties, focusing on efficiency, accuracy, and workload reduction. The final 51 articles were categorized into 4 groups based on diagnostic efficiency, where category A included studies with supporting material provided, category B consisted of those with reduced data volume, category C focused on independent AI diagnosis, and category D included studies that reported data reduction without changes in diagnostic time. In radiology and pathology, which require skilled techniques and large-scale data processing, AI improved accuracy and reduced diagnostic time by approximately 90% or more. Radiology, in particular, showed a high proportion of category C studies, as digitized data and standardized protocols facilitated independent AI diagnoses. AI has significant potential to optimize workload management, improve diagnostic efficiency, and enhance accuracy. However, challenges remain in standardizing applications and addressing ethical concerns. Integrating AI into healthcare workforce planning is essential for fostering collaboration between technology and clinicians, ultimately improving patient care.

Assessment of the stability of intracranial aneurysms using a deep learning model based on computed tomography angiography

PURPOSE

Assessment of the stability of intracranial aneurysms is important in the clinic but remains challenging. The aim of this study was to construct a deep learning model (DLM) to identify unstable aneurysms on computed tomography angiography (CTA) images.

METHODS

The clinical data of 1041 patients with 1227 aneurysms were retrospectively analyzed from August 2011 to May 2021. Patients with aneurysms were divided into unstable (ruptured, evolving and symptomatic aneurysms) and stable (fortuitous, nonevolving and asymptomatic aneurysms) groups and randomly divided into training (833 patients with 991 aneurysms) and internal validation (208 patients with 236 aneurysms) sets. One hundred and ninety-seven patients with 229 aneurysms from another hospital were included in the external validation set. Six models based on a convolutional neural network (CNN) or logistic regression were constructed on the basis of clinical, morphological and deep learning (DL) features. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated to evaluate the discriminating ability of the models.

RESULTS

The AUCs of Models A (clinical), B (morphological) and C (DL features from the CTA image) in the external validation set were 0.5706, 0.9665 and 0.8453, respectively. The AUCs of Model D (clinical and DL features), Model E (clinical and morphological features) and Model F (clinical, morphological and DL features) in the external validation set were 0.8395, 0.9597 and 0.9696, respectively.

CONCLUSIONS

The CNN-based DLM, which integrates clinical, morphological and DL features, outperforms other models in predicting IA stability. The DLM has the potential to assess IA stability and support clinical decision-making.

Deep learning for the detection of moyamoya angiopathy using T2-weighted images: a multicenter study

Background

Moyamoya angiopathy (MMA) can be potentially missed in the initial magnetic resonance (MR) examination without MR angiography (MRA). The aim of this study was to develop an optimal deep learning model based on T2-weighted imaging (T2WI) for MMA detection.

Methods

This retrospective multicenter study included MMA patients, control group patients with normal MRA and patients with cerebrovascular disease except MMA from seven hospitals (site 1 to site 7). Five models, namely shallow convolutional neural network (SCNN), LeNet-5 Convolutional Neural Network (LeNet), Visual Geometry Group Network (VGG), Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet), were used for training and validation. The model training and internal validation were performed with data from sites 1-4. Data from sites 5-7 were used for independent external validation, and the optimal model was selected according to the results of accuracy. Chi-squared test was used to further verify the influence of different MR manufacturers, field strength, age at the MR examination and MRA score on the optimal model.

Results

A total of 1,038 MMA patients, 1,211 normal MRA and 271 patients with cerebrovascular disease except MMA were included. DenseNet showed the highest accuracy (0.859, 95% CI: 0.833, 0.884) in the independent external validation, which was not significantly different from that of VGG (0.834, 95% CI: 0.807, 0.861) and ResNet (0.855, 95% CI: 0.829, 0.880) but was significantly higher than that of SCNN (0.631, 95% CI: 0.595, 0.665; P<0.001) and LeNet (0.563, 95% CI: 0.527, 0.599; P=0.001). The accuracy of 1.5 T data was higher than 3.0 T data (χ2=6.559, P=0.01). The accuracy of MMA with MRA who scored more than 5 was higher than that scoring ≤5 (≤5 vs. 6-10: χ2=10.734, P=0.001; ≤5 vs. ≥11: χ2=10.369, P=0.001).

Conclusions

DenseNet based on T2WI can be used to screen MMA, outperforming SCNN, LeNet, VGG and ResNet. The MRA score of MMA affected the DenseNet accuracy.

Deep learning-assistance significantly increases the detection sensitivity of neurosurgery residents for intracranial aneurysms in subarachnoid hemorrhage

OBJECTIVE

The purpose of this study was to evaluate the effectiveness of a deep learning model (DLM) in improving the sensitivity of neurosurgery residents to detect intracranial aneurysms on CT angiography (CTA) in patients with aneurysmal subarachnoid hemorrhage (aSAH).

METHODS

In this diagnostic accuracy study, a set of 104 CTA scans of aSAH patients containing a total of 126 aneurysms were presented to three blinded neurosurgery residents (a first-year, third-year, and fifth-year resident), who individually assessed them for aneurysms. After the initial reading, the residents were given the predictions of a dedicated DLM previously established for automated detection and segmentation of intracranial aneurysms. The detection sensitivities for aneurysms of the DLM and the residents with and without the assistance of the DLM were compared.

RESULTS

The DLM had a detection sensitivity of 85.7%, while the residents showed detection sensitivities of 77.8%, 86.5%, and 87.3% without DLM assistance. After being provided with the DLM's results, the residents' individual detection sensitivities increased to 97.6%, 95.2%, and 98.4%, respectively, yielding an average increase of 13.2%. The DLM was particularly useful in detecting small aneurysms. In addition, interrater agreement among residents increased from a Fleiss κ of 0.394 without DLM assistance to 0.703 with DLM assistance.

CONCLUSIONS

The results of this pilot study suggest that deep learning models can help neurosurgeons detect aneurysms on CTA and make appropriate treatment decisions when immediate radiological consultation is not possible.

PEGylated Ultrasmall Iron Oxide Nanoparticles as MRI Contrast Agents for Vascular Imaging and Real-Time Monitoring

Accurate imaging evaluations of pre- and post-treatment of cardiovascular diseases are pivotal for effective clinical interventions and improved patient outcomes. However, current imaging methods lack real-time monitoring capabilities with a high contrast and resolution during treatments. This study introduces PEGylated ultrasmall iron oxide nanoparticles (PUSIONPs), which have undergone comprehensive safety evaluations, boasting an r1 value of 6.31 mM-1 s-1, for contrast-enhanced magnetic resonance angiography (MRA). Systematic comparisons against common clinical methods in rabbits reveal that PUSIONPs-enhanced MRA exhibited improved vascular contrast, clearer vascular boundaries, and superior vessel resolution. Moreover, owing to their nanosize, PUSIONPs demonstrate significantly prolonged blood circulation compared to small molecular contrast agents such as Magnevist and Ultravist. This extended circulation enables captivating real-time monitoring of thrombolysis treatment for up to 4 h in rabbit models postsingle contrast agent injection. Additionally, in larger animal models such as beagles and Bama minipigs, PUSIONPs-enhanced MRA also showcases superior contrast effects, boundary delineation, and microvessel visualization, underscoring their potential to transform cardiovascular imaging, particularly in real-time monitoring and high-resolution visualization during treatment processes.

Intracranial aneurysm instability prediction model based on 4D-Flow MRI and HR-MRI

This study aims to develop a reliable predictive model for assessing intracranial aneurysm (IA) instability by utilizing four-dimensional flow magnetic resonance imaging (4D-Flow MRI) and high-resolution MRI (HR-MRI). Initially, we curated a prospective dataset, dubbed the primary cohort, by aggregating patient data that was consecutively enrolled across two centers from November 2018 to November 2021. Unstable aneurysms were defined as those with symptoms, morphological change or ruptured during follow-up periods. We introduce a specialized ensemble learning framework, termed the Hybrid Model, which synergistically combines two heterogeneous base learning algorithms: 4D-Flow logistic regression (4D-Flow-LR) and Multi-crop Attention Branch Network (MicroAB-Net). The ability of the hybrid model to predict aneurysm instability was compared with baseline models: PHASES (population, hypertension, age, size, earlier rupture, and site) LR, ELAPSS (earlier subarachnoid hemorrhage, location, age, population, size, and shape) LR, aneurysm wall enhancement (AWE) LR, and Radiomics using the area under the curve (AUC) with Delong's test. Finally, the Hybrid Model was further validated in the validation cohort (patients enrolled between December 2021 to May 2022). In the primary cohort, 189 patients (144 women [76.2 ​%]; aged 58.90 years ​± ​10.32) with 213 IAs were included. In the validation cohort, 48 patients (35 women [72.9 ​%]; aged 55.0 years ​± ​10.77) with 53 IAs were included. The Hybrid Model achieved the highest performance both in the primary cohort (AUC ​= ​0.854) and the validation cohort (AUC ​= ​0.876). The Hybrid model provided a promising prediction of aneurysm instability.

Identification of immune-inflammation targets for intracranial aneurysms: a multiomics and epigenome-wide study integrating summary-data-based Mendelian randomization, single-cell-type expression analysis, and DNA methylation regulation

BACKGROUND

Dysfunction of the immune system and inflammation plays a vital role in developing intracranial aneurysms (IAs). However, the progress of genetic pathophysiology is complicated and not entirely elaborated. This study aimed to explore the genetic associations of immune-related and inflammation-related genes (IIRGs) with IAs and their subtypes using Mendelian randomization, colocalization test, and integrated multiomics functional analysis.

METHODS

The authors conducted a summary-data-based Mendelian randomization (SMR) analysis using data from several genome-wide association studies of gene expression (31 684 European individuals) and protein quantitative trait loci (35 559 Icelanders), as well as information on IAs and their subtypes from The International Stroke Genetics Consortium (IGSC) for discovery phase and the FinnGen study for replication. This analysis aimed to determine the causal relationship between IIRGs and the risk of IAs and their subtypes. Further functional analyses, including DNA methylation regulation (1980, European individuals), single-cell-type expression analysis, and protein-protein interaction, were conducted to detect the specific cell type with enriched expression and discover potential drug targets.

RESULTS

After integrating multiomics evidence from expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL), the authors found that tier 1: RELT [odds ratio (OR): 0.14, 95% CI: 0.04-0.50], TNFSF12 (OR: 1.24, 95% CI: 1.24-1.43), tier 3: ICAM5 (OR: 0.89, 95% CI: 0.82-0.96), and ERAP2 (OR: 1.07, 95% CI: 1.02-1.12) were associated with the risk of IAs; tier 3: RELT (OR: 0.11, 95% CI: 0.02-0.54), ERAP2 (OR: 1.08, 95% CI: 1.02-1.13), and TNFSF12 (OR: 1.24, 95% CI: 1.05-1.47) were associated with the risk of aneurysmal subarachnoid hemorrhage (aSAH); and tier 1: RELT (OR: 0.04, 95% CI: 0.01-0.30) was associated with the risk of unruptured intracranial aneurysms (uIAs). Further functional analyses showed that RELT was regulated by cg06382664 and cg18850434 and ICAM5 was regulated by cg04295144 in IAs; RELT was regulated by cg06382664, cg08770935, cg16533363, and cg18850434 in aSAH; and RELT was regulated by cg06382664 and cg21810604 in uIAs. In addition, the authors found that H6PD (OR: 1.13, 95% CI: 1.01-1.28), NT5M (OR: 1.91, 95% CI: 1.21-3.01), and NPTXR (OR: 1.13, 95% CI: 1.01-1.26) were associated with IAs; NT5M (OR: 2.13, 95% CI: 1.23-3.66) was associated aSAH; and AP4M1 (OR: 0.06, 95% CI: 0.01-0.42) and STX7 (OR: 3.97, 95% CI: 1.41-11.18) were related to uIAs. STX7 and TNFSF12 were mainly enriched in microglial cells, whereas H6PD, STX7 , and TNFSF12 were mainly enriched in astrocytes.

CONCLUSIONS

After integrating multiomics evidence, the authors eventually identified IIRGs: RELT, TNFSF12, ICAM5 , and ERAP2 were the novel therapy targets for IAs. These new results confirmed a vital role of immune and inflammation in the etiology of IAs, contributing to enhance our understanding of the immune and inflammatory mechanisms in the pathogenesis of IAs and revealing the complex genetic causality of IAs.

DACG: Dual Attention and Context Guidance model for radiology report generation

Medical images are an essential basis for radiologists to write radiology reports and greatly help subsequent clinical treatment. The task of generating automatic radiology reports aims to alleviate the burden of clinical doctors writing reports and has received increasing attention this year, becoming an important research hotspot. However, there are severe issues of visual and textual data bias and long text generation in the medical field. Firstly, Abnormal areas in radiological images only account for a small portion, and most radiological reports only involve descriptions of normal findings. Secondly, there are still significant challenges in generating longer and more accurate descriptive texts for radiology report generation tasks. In this paper, we propose a new Dual Attention and Context Guidance (DACG) model to alleviate visual and textual data bias and promote the generation of long texts. We use a Dual Attention Module, including a Position Attention Block and a Channel Attention Block, to extract finer position and channel features from medical images, enhancing the image feature extraction ability of the encoder. We use the Context Guidance Module to integrate contextual information into the decoder and supervise the generation of long texts. The experimental results show that our proposed model achieves state-of-the-art performance on the most commonly used IU X-ray and MIMIC-CXR datasets. Further analysis also proves that our model can improve reporting through more accurate anomaly detection and more detailed descriptions. The source code is available at https://github.com/LangWY/DACG.

Comprehensive Management of Intracranial Aneurysms Using Artificial Intelligence: An Overview

Intracranial aneurysms (IAs), an asymptomatic vascular lesion, are becoming increasingly common as imaging technology progresses. Subarachnoid hemorrhage from IAs rupture entails a substantial risk of mortality or severe disability. The early detection and prompt intervention of IAs posing a high risk of rupture are paramount for optimizing clinical management and safeguarding patients' lives. Artificial intelligence (AI), with its exceptional capabilities in image-based tasks, has garnered significant scholarly interest worldwide. Its application in the management of IAs holds promise for advancing medical research and patient care. Utilizing deep learning algorithms, AI exhibits remarkable capabilities in precisely identifying and segmenting aneurysms, significantly enhancing diagnostic sensitivity and accuracy. Furthermore, AI can meticulously analyze extensive aneurysm datasets to forecast aneurysm growth, rupture hazards, and prognostic scenarios, offering clinician's invaluable assistance in decision-making. This article comprehensively examines the latest advancements in the utilization of AI in aneurysm treatment, encompassing detection and segmentation, rupture risk assessment, prediction of therapeutic outcomes, and facilitation of microcatheter shaping. A brief discussion is held on the challenges and future paths for clinical AI deployments.

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation

BACKGROUND

Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task.

OBJECTIVE

To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs.

METHODS

This retrospective study was conducted using head CT angiography (CTA) examinations depicting IAs from two hospitals in China between 2010 and 2021. Training included cases with subarachnoid hemorrhage (SAH) and arterial stenosis, common accompanying vascular abnormalities. Testing was performed in cohorts with reference-standard digital subtraction angiography (cohort 1), with SAH (cohort 2), acquired outside the time interval of training data (cohort 3), and an external dataset (cohort 4). The algorithm's performance was evaluated using sensitivity, recall, false positives per case (FPs/case), and Dice coefficient, with manual segmentation as the reference standard.

RESULTS

The study included 3190 CTA scans with 4124 IAs. Sensitivity, recall, and FPs/case for detection of IAs were, respectively, 98.58%, 96.17%, and 2.08 in cohort 1; 95.00%, 88.8%, and 3.62 in cohort 2; 96.00%, 93.77%, and 2.60 in cohort 3; and, 96.17%, 94.05%, and 3.60 in external cohort 4. The segmentation accuracy, as measured by the Dice coefficient, was 0.78, 0.71, 0.71, and 0.66 for cohorts 1-4, respectively. VA-Unet detection recall and FPs/case and segmentation accuracy were affected by several clinical factors, including aneurysm size, bifurcation aneurysms, and the presence of arterial stenosis and SAH.

CONCLUSIONS

VA-Unet accurately detected and segmented IAs in head CTA comparably to expert interpretation. The proposed algorithm has significant potential to assist radiologists in efficiently detecting and segmenting IAs from CTA images.

A mutual inclusion mechanism for precise boundary segmentation in medical images

Introduction

Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images.

Methods

Our approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process.

Results

We evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation.

Conclusion

The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.

Advancements in opportunistic intracranial aneurysm screening: The impact of a deep learning algorithm on radiologists' analysis of T2-weighted cranial MRI

(1) Background: Unruptured Intracranial Aneurysms (UIAs) are common blood vessel malformations, occurring in up to 3 % of healthy adults. Magnetic Resonance Angiography (MRA) is frequently used for the screening of UIAs due to its high resolution in vascular anatomy. However, T2-Weighted Magnetic Resonance Imaging (T2WI) is a standard sequence utilized for the majority of outpatient head scans. By employing a sequence such as T2WI, there is a possible shift towards early detection of UIAs through opportunistic screening. Here, we assess a Deep Learning Algorithm (DLA) developed to assist radiologists in identifying and reporting UIAs on T2WI in a routine clinical setting. (2) Methods: A DLA was trained on a set of 110 patients undergoing an MR head scan with the gold standard set by two radiology experts. Eight radiologists were given a cohort of 50 cranial T2WI studies and asked for a routine report. After a 10-day washout period, they reviewed the same cases randomized and supported by the DLA predictions. We assessed changes in sensitivity, specificity, accuracy, and false positives. (3) Results: During routine reporting, the models' assistance improved the sensitivity of the eight participants by an average of 36.19 and the accuracy by 10.00 percentage points. (4) Conclusion: Our results indicate the potential benefit of deep learning to improve radiologists' detection of UIAs during routine reporting. From this, we can infer that the combination of T2WI with our DLA supports opportunistic screening, suggesting potential approaches for future research and application.

Intracranial aneurysm CTA images and 3D models dataset with clinical morphological and hemodynamic data

Intracranial aneurysm is a cerebrovascular disease associated with a high rupture risk, often resulting in death or severe disability. Recent advances in AI enable the prediction of intracranial aneurysm initiation, progression, and rupture through medical image analysis. Despite growing research interest, there is a shortage of publicly available datasets for training and validating AI models. This paper presents a comprehensive dataset comprising high-resolution CTA images of 99 patients with 105 MCA aneurysms and 44 normal healthy controls, along with their respective clinical data and 3D models of aneurysms and the parent arteries derived from the CTA images. Furthermore, recognizing the significance of blood hemodynamics on aneurysm development, this dataset also included the morphological and hemodynamic parameters obtained by computational fluid dynamics (CFD) for each patient and healthy control, which can be utilized by researchers without prior CFD experience. This dataset will facilitate hypothesis-driven or data-driven research on intracranial aneurysms, and has the potential to deepen our understanding of this disease.

Vessel-aware aneurysm detection using multi-scale deformable 3D attention

Manual detection of intracranial aneurysms (IAs) in computed tomography (CT) scans is a complex, time-consuming task even for expert clinicians, and automating the process is no less challenging. Critical difficulties associated with detecting aneurysms include their small (yet varied) size compared to scans and a high potential for false positive (FP) predictions. To address these issues, we propose a 3D, multi-scale neural architecture that detects aneurysms via a deformable attention mechanism that operates on vessel distance maps derived from vessel segmentations and 3D features extracted from the layers of a convolutional network. Likewise, we reformulate aneurysm segmentation as bounding cuboid prediction using binary cross entropy and three localization losses (location, size, IoU). Given three validation sets comprised of 152/138/38 CT scans and containing 126/101/58 aneurysms, we achieved a Sensitivity of 91.3%/97.0%/74.1% @ FP rates 0.53/0.56/0.87, with Sensitivity around 80% on small aneurysms. Manual inspection of outputs by experts showed our model only tends to miss aneurysms located in unusual locations. Code and model weights are available online.

Robust Automated Tumour Segmentation Network Using 3D Direction-Wise Convolution and Transformer

Semantic segmentation of tumours plays a crucial role in fundamental medical image analysis and has a significant impact on cancer diagnosis and treatment planning. UNet and its variants have achieved state-of-the-art results on various 2D and 3D medical image segmentation tasks involving different imaging modalities. Recently, researchers have tried to merge the multi-head self-attention mechanism, as introduced by the Transformer, into U-shaped network structures to enhance the segmentation performance. However, both suffer from limitations that make networks under-perform on voxel-level classification tasks, the Transformer is unable to encode positional information and translation equivariance, while the Convolutional Neural Network lacks global features and dynamic attention. In this work, a new architecture named TCTNet Tumour Segmentation with 3D Direction-Wise Convolution and Transformer) is introduced, which comprises an encoder utilising a hybrid Transformer-Convolutional Neural Network (CNN) structure and a decoder that incorporates 3D Direction-Wise Convolution. Experimental results show that the proposed hybrid Transformer-CNN network structure obtains better performance than other 3D segmentation networks on the Brain Tumour Segmentation 2021 (BraTS21) dataset. Two more tumour datasets from Medical Segmentation Decathlon are also utilised to test the generalisation ability of the proposed network architecture. In addition, an ablation study was conducted to verify the effectiveness of the designed decoder for the tumour segmentation tasks. The proposed method maintains a competitive segmentation performance while reducing computational effort by 10% in terms of floating-point operations.

Evaluation of the clinical application value of artificial intelligence in diagnosing head and neck aneurysms

OBJECTIVE

To evaluate the performance of a semi-automated artificial intelligence (AI) software program (CerebralDoc® system) in aneurysm detection and morphological measurement.

METHODS

In this study, 354 cases of computed tomographic angiography (CTA) were retrospectively collected in our hospital. Among them, 280 cases were diagnosed with aneurysms by either digital subtraction angiography (DSA) and CTA (DSA group, n = 102), or CTA-only (non-DSA group, n = 178). The presence or absence of aneurysms, as well as their location and related morphological features determined by AI were evaluated using DSA and radiologist findings. Besides, post-processing image quality from AI and radiologists were also rated and compared.

RESULTS

In the DSA group, AI achieved a sensitivity of 88.24% and an accuracy of 81.97%, whereas radiologists achieved a sensitivity of 95.10% and an accuracy of 84.43%, using DSA results as the gold standard. The AI in the non-DSA group achieved 81.46% sensitivity and 76.29% accuracy, as per the radiologists' findings. The comparison of position consistency results showed better performance under loose criteria than strict criteria. In terms of morphological characteristics, both the DSA and the non-DSA groups agreed well with the diagnostic results for neck width and maximum diameter, demonstrating excellent ICC reliability exceeding 0.80. The AI-generated images exhibited superior quality compared to the standard software for post-processing, while also demonstrating a significantly reduced processing time.

CONCLUSIONS

The AI-based aneurysm detection rate demonstrates a commendable performance, while the extracted morphological parameters exhibit a remarkable consistency with those assessed by radiologists, thereby showcasing significant potential for clinical application.

Prediction of carotid artery plaque area based on parallel multi-gate attention capture model

Cardiovascular disease (CVD) is a group of conditions involving the heart or blood vessels and is a leading cause of death and disability worldwide. Carotid artery plaque, as a key risk factor, is crucial for the early prevention and management of CVD. The purpose of this study is to combine clinical application and deep learning techniques to design a predictive model for the carotid artery plaque area. This model aims to identify individuals at high risk and reduce the incidence of cardiovascular disease through the implementation of relevant preventive measures. This study proposes an innovative multi-gate attention capture (MGAC) model that utilizes data such as risk factors, laboratory tests, and physical examinations to predict the area of carotid artery plaque. Experimental findings reveal the superior performance of the MGAC model, surpassing other commonly used deep learning models with the following metrics: mean absolute error of 4.17, root mean square error of 10.89, mean logarithmic squared error of 0.21, and coefficient of determination of 0.98.

Risk factors and predictive indicators of rupture in cerebral aneurysms

Cerebral aneurysms are abnormal dilations of blood vessels in the brain that have the potential to rupture, leading to subarachnoid hemorrhage and other serious complications. Early detection and prediction of aneurysm rupture are crucial for effective management and prevention of rupture-related morbidities and mortalities. This review aims to summarize the current knowledge on risk factors and predictive indicators of rupture in cerebral aneurysms. Morphological characteristics such as aneurysm size, shape, and location, as well as hemodynamic factors including blood flow patterns and wall shear stress, have been identified as important factors influencing aneurysm stability and rupture risk. In addition to these traditional factors, emerging evidence suggests that biological and genetic factors, such as inflammation, extracellular matrix remodeling, and genetic polymorphisms, may also play significant roles in aneurysm rupture. Furthermore, advancements in computational fluid dynamics and machine learning algorithms have enabled the development of novel predictive models for rupture risk assessment. However, challenges remain in accurately predicting aneurysm rupture, and further research is needed to validate these predictors and integrate them into clinical practice. By elucidating and identifying the various risk factors and predictive indicators associated with aneurysm rupture, we can enhance personalized risk assessment and optimize treatment strategies for patients with cerebral aneurysms.

Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images

PURPOSE

The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist's accuracy in identifying aneurysms and reduces image analysis time.

METHODS

TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software.

RESULTS

In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction).

CONCLUSIONS

Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter.

Progress and trends in neurological disorders research based on deep learning

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

TLF: Triple learning framework for intracranial aneurysms segmentation from unreliable labeled CTA scans

Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation. Nevertheless, training large-scale DNNs demands substantial quantities of high-quality labeled data, making the annotation of numerous brain CTA scans a challenging endeavor. To address these challenges and effectively develop a robust IAs segmentation model from a large amount of unlabeled training data, we propose a triple learning framework (TLF). The framework primarily consists of three learning paradigms: pseudo-supervised learning, contrastive learning, and confident learning. This paper introduces an enhanced mean teacher model and voxel-selective strategy to conduct pseudo-supervised learning on unreliable labeled training data. Concurrently, we construct the positive and negative training pairs within the high-level semantic feature space to improve the overall learning efficiency of the TLF through contrastive learning. In addition, a multi-scale confident learning is proposed to correct unreliable labels, which enables the acquisition of broader local structural information instead of relying on individual voxels. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built database of hundreds of cases of brain CTA scans with IAs. Experimental results demonstrate that our method can effectively learn a robust CTA scan-based IAs segmentation model using unreliable labeled data, outperforming state-of-the-art methods in terms of segmentation accuracy. Codes are released at https://github.com/XueShuangqian/TLF.

Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study

Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneurysm segmentation and detection on CT angiography (CTA) images and to compare its performance with radiology reports. Materials and Methods Consecutive head or head and neck CTA images of suspected unruptured cerebral aneurysms were gathered retrospectively from eight hospitals between February 2018 and October 2021 for model development. An external test set with reference standard digital subtraction angiography (DSA) scans was obtained retrospectively from one of the eight hospitals between February 2022 and February 2023. Radiologists (reference standard) assessed aneurysm segmentation, while model performance was evaluated using the Dice similarity coefficient (DSC). The model's aneurysm detection performance was assessed by sensitivity and comparing areas under the receiver operating characteristic curves (AUCs) between the model and radiology reports in the DSA data set with use of the DeLong test. Results Images from 6060 patients (mean age, 56 years ± 12 [SD]; 3375 [55.7%] female) were included for model development (training: 4342; validation: 1086; and internal test set: 632). Another 118 patients (mean age, 59 years ± 14; 79 [66.9%] female) were included in an external test set to evaluate performance based on DSA. The model achieved a DSC of 0.87 for aneurysm segmentation performance in the internal test set. Using DSA, the model achieved 85.7% (108 of 126 aneurysms [95% CI: 78.1, 90.1]) sensitivity in detecting aneurysms on per-vessel analysis, with no evidence of a difference versus radiology reports (AUC, 0.93 [95% CI: 0.90, 0.95] vs 0.91 [95% CI: 0.87, 0.94]; P = .67). Model processing time from reconstruction to detection was 1.76 minutes ± 0.32 per scan. Conclusion The proposed DL model could accurately segment and detect cerebral aneurysms at CTA with no evidence of a significant difference in diagnostic performance compared with radiology reports. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Payabvash in this issue.

Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation

BACKGROUND

The retinal vasculature, a crucial component of the human body, mirrors various illnesses such as cardiovascular disease, glaucoma, and retinopathy. Accurate segmentation of retinal vessels in funduscopic images is essential for diagnosing and understanding these conditions. However, existing segmentation models often struggle with images from different sources, making accurate segmentation in crossing-source fundus images challenging.

METHODS

To address the crossing-source segmentation issues, this paper proposes a novel Multi-level Adversarial Learning and Pseudo-label Denoising-based Self-training Framework (MLAL&PDSF). Expanding on our previously proposed Multiscale Context Gating with Breakpoint and Spatial Dual Attention Network (MCG&BSA-Net), MLAL&PDSF introduces a multi-level adversarial network that operates at both the feature and image layers to align distributions between the target and source domains. Additionally, it employs a distance comparison technique to refine pseudo-labels generated during the self-training process. By comparing the distance between the pseudo-labels and the network predictions, the framework identifies and corrects inaccuracies, thus enhancing the accuracy of the fine vessel segmentation.

RESULTS

We have conducted extensive validation and comparative experiments on the CHASEDB1, STARE, and HRF datasets to evaluate the efficacy of the MLAL&PDSF. The evaluation metrics included the area under the operating characteristic curve (AUC), sensitivity (SE), specificity (SP), accuracy (ACC), and balanced F-score (F1). The performance results from unsupervised domain adaptive segmentation are remarkable: for DRIVE to CHASEDB1, results are AUC: 0.9806, SE: 0.7400, SP: 0.9737, ACC: 0.9874, and F1: 0.8851; for DRIVE to STARE, results are AUC: 0.9827, SE: 0.7944, SP: 0.9651, ACC: 0.9826, and F1: 0.8326.

CONCLUSION

These results demonstrate the effectiveness and robustness of MLAL&PDSF in achieving accurate segmentation results from crossing-domain retinal vessel datasets. The framework lays a solid foundation for further advancements in cross-domain segmentation and enhances the diagnosis and understanding of related diseases.

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study

PURPOSE

To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA).

METHOD

This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability.

RESULTS

510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06.

CONCLUSIONS

The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.

Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review

BACKGROUND

The detection and management of intracranial aneurysms (IAs) are vital to prevent life-threatening complications like subarachnoid hemorrhage (SAH). Artificial Intelligence (AI) can analyze medical images, like CTA or MRA, spotting nuances possibly overlooked by humans. Early detection facilitates timely interventions and improved outcomes. Moreover, AI algorithms offer quantitative data on aneurysm attributes, aiding in long-term monitoring and assessing rupture risks.

METHODS

We screened four databases (PubMed, Web of Science, IEEE and Scopus) for studies using artificial intelligence algorithms to identify IA. Based on algorithmic methodologies, we categorized them into classification, segmentation, detection and combined, and then their merits and shortcomings are compared. Subsequently, we elucidate potential challenges that contemporary algorithms might encounter within real-world clinical diagnostic contexts. Then we outline prospective research trajectories and underscore key concerns in this evolving field.

RESULTS

Forty-seven studies of IA recognition based on AI were included based on search and screening criteria. The retrospective results represent that current studies can identify IA in different modal images and predict their risk of rupture and blockage. In clinical diagnosis, AI can effectively improve the diagnostic accuracy of IA and reduce missed detection and false positives.

CONCLUSIONS

The AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion. In addition, analyzing aneurysm rupture and blockage before and after surgery can help doctors plan treatment and reduce the uncertainties in the treatment process.

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL Study)-a protocol for a multicenter, double-blinded randomized controlled trial

BACKGROUND

This multicenter, double-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneurysm detection in CT angiography (CTA) and its influence on patients' short-term and long-term outcomes.

METHODS

Study design: Prospective, multicenter, double-blinded RCT.

SETTINGS

The model was designed for the automatic detection of intracranial aneurysms from original CTA images.

PARTICIPANTS

Adult inpatients and outpatients who are scheduled for head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA interpreted by radiologists with the assistance of the True-AI-integrated intracranial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA interpreted by radiologists with the assistance of the Sham-AI-integrated intracranial aneurysm diagnosis strategy (Sham-AI arm).

RANDOMIZATION

Block randomization, stratified by center, gender, and age group.

PRIMARY OUTCOMES

Coprimary outcomes of superiority in patient-level sensitivity and noninferiority in specificity for the True-AI arm to the Sham-AI arm in intracranial aneurysms.

SECONDARY OUTCOMES

Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis.

BLINDING

Study participants and participating radiologists will be blinded to the intervention.

SAMPLE SIZE

Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intracranial aneurysms for patients undergoing head CTA in the hospital is approximately 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided α = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm.

DISCUSSION

The study will determine the precise impact of the AI system on the detection performance for intracranial aneurysms in a double-blinded design and following the real-world effects on patients' short-term and long-term outcomes.

TRIAL REGISTRATION

This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT06118840 . Registered 11 November 2023.

The new era of artificial intelligence in neuroradiology: current research and promising tools

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. However, they lack the ability to harness the image's intrinsic position and channel features. Research employing Dual Attention mechanisms of position and channel have not been specifically optimized for the high-detail demands of medical images. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block (DA-Block) into the traditional U-shaped architecture. Also, DA-TransUNet tailored for the high-detail requirements of medical images, optimizes the intermittent channels of Dual Attention (DA) and employs DA in each skip-connection to effectively filter out irrelevant information. This integration significantly enhances the model's capability to extract features, thereby improving the performance of medical image segmentation. DA-TransUNet is validated in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across 5 datasets. In summary, DA-TransUNet has made significant strides in medical image segmentation, offering new insights into existing techniques. It strengthens model performance from the perspective of image features, thereby advancing the development of high-precision automated medical image diagnosis. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study

BACKGROUND

Artificial intelligence (AI) models in real-world implementation are scarce. Our study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm detection, assess how it helps clinicians improve diagnostic performance, and validate its application in real-world clinical implementation.

METHODS

We developed a deep-learning model using 16 546 head and neck CTA examination images from 14 517 patients at eight Chinese hospitals. Using an adapted, stepwise implementation and evaluation, 120 certified clinicians from 15 geographically different hospitals were recruited. Initially, the AI model was externally validated with images of 900 digital subtraction angiography-verified CTA cases (examinations) and compared with the performance of 24 clinicians who each viewed 300 of these cases (stage 1). Next, as a further external validation a multi-reader multi-case study enrolled 48 clinicians to individually review 298 digital subtraction angiography-verified CTA cases (stage 2). The clinicians reviewed each CTA examination twice (ie, with and without the AI model), separated by a 4-week washout period. Then, a randomised open-label comparison study enrolled 48 clinicians to assess the acceptance and performance of this AI model (stage 3). Finally, the model was prospectively deployed and validated in 1562 real-world clinical CTA cases.

FINDINGS

The AI model in the internal dataset achieved a patient-level diagnostic sensitivity of 0·957 (95% CI 0·939-0·971) and a higher patient-level diagnostic sensitivity than clinicians (0·943 [0·921-0·961] vs 0·658 [0·644-0·672]; p<0·0001) in the external dataset. In the multi-reader multi-case study, the AI-assisted strategy improved clinicians' diagnostic performance both on a per-patient basis (the area under the receiver operating characteristic curves [AUCs]; 0·795 [0·761-0·830] without AI vs 0·878 [0·850-0·906] with AI; p<0·0001) and a per-aneurysm basis (the area under the weighted alternative free-response receiver operating characteristic curves; 0·765 [0·732-0·799] vs 0·865 [0·839-0·891]; p<0·0001). Reading time decreased with the aid of the AI model (87·5 s vs 82·7 s, p<0·0001). In the randomised open-label comparison study, clinicians in the AI-assisted group had a high acceptance of the AI model (92·6% adoption rate), and a higher AUC when compared with the control group (0·858 [95% CI 0·850-0·866] vs 0·789 [0·780-0·799]; p<0·0001). In the prospective study, the AI model had a 0·51% (8/1570) error rate due to poor-quality CTA images and recognition failure. The model had a high negative predictive value of 0·998 (0·994-1·000) and significantly improved the diagnostic performance of clinicians; AUC improved from 0·787 (95% CI 0·766-0·808) to 0·909 (0·894-0·923; p<0·0001) and patient-level sensitivity improved from 0·590 (0·511-0·666) to 0·825 (0·759-0·880; p<0·0001).

INTERPRETATION

This AI model demonstrated strong clinical potential for intracranial aneurysm detection with improved clinician diagnostic performance, high acceptance, and practical implementation in real-world clinical cases.

FUNDING

National Natural Science Foundation of China.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces "NIMEQ-SACNet," a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet's parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model's ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet's pre-eminence over prevailing algorithms and classification frameworks.

Metallic artifacts-free spectral computed tomography angiography based on renal clearable bismuth chelate

Computed tomography angiography (CTA) is one of the most important diagnosis techniques for various vascular diseases in clinic. However, metallic artifacts caused by metal implants and calcified plaques in more and more patients severely hinder its wide applications. Herein, we propose an improved metallic artifacts-free spectral CTA technique based on renal clearable bismuth chelate (Bi-DTPA dimeglumine) for the first time. Bi-DTPA dimeglumine owns the merits of ultra-simple synthetic process, approximately 100% of yield, large-scale production capability, good biocompatibility, and favorable renal clearable ability. More importantly, Bi-DTPA dimeglumine shows superior contrast-enhanced effect in CTA compared with clinical iohexol at a wide range of X-ray energies especially in higher X-ray energy. In rabbits' model with metallic transplants, Bi-DTPA dimeglumine assisted-spectral CTA can not only effectively mitigate metallic artifacts by reducing beam hardening effect under high X-ray energy, but also enables accurate delineation of vascular structure. Our proposed strategy opens a revolutionary way to solve the bottleneck problem of metallic artifacts in CTA examinations.

Deep learning-based cerebral aneurysm segmentation and morphological analysis with three-dimensional rotational angiography

BACKGROUND

The morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device selection in endovascular treatment, but manual evaluation by human raters only has moderate interrater/intrarater reliability.

METHODS

We collected data for 889 cerebral angiograms from consecutive patients with suspected cerebral aneurysms at our institution from January 2017 to October 2021. The automatic morphological analysis model was developed on the derivation cohort dataset consisting of 388 scans with 437 aneurysms, and the performance of the model was tested on the validation cohort dataset consisting of 96 scans with 124 aneurysms. Five clinically important parameters were automatically calculated by the model: aneurysm volume, maximum aneurysm size, neck size, aneurysm height, and aspect ratio.

RESULTS

On the validation cohort dataset the average aneurysm size was 7.9±4.6 mm. The proposed model displayed high segmentation accuracy with a mean Dice similarity index of 0.87 (median 0.93). All the morphological parameters were significantly correlated with the reference standard (all P<0.0001; Pearson correlation analysis). The difference in the maximum aneurysm size between the model prediction and reference standard was 0.5±0.7 mm (mean±SD). The difference in neck size between the model prediction and reference standard was 0.8±1.7 mm (mean±SD).

CONCLUSION

The automatic aneurysm analysis model based on angiography data exhibited high accuracy for evaluating the morphological characteristics of cerebral aneurysms.

Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations

Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.

Deep Learning: A Primer for Neurosurgeons

This chapter explores the transformative impact of deep learning (DL) on neurosurgery, elucidating its pivotal role in enhancing diagnostic performance, surgical planning, execution, and postoperative assessment. It delves into various deep learning architectures, including convolutional and recurrent neural networks, and their applications in analyzing neuroimaging data for brain tumors, spinal cord injuries, and other neurological conditions. The integration of DL in neurosurgical robotics and the potential for fully autonomous surgical procedures are discussed, highlighting advancements in surgical precision and patient outcomes. The chapter also examines the challenges of data privacy, quality, and interpretability that accompany the implementation of DL in neurosurgery. The potential for DL to revolutionize neurosurgical practices through improved diagnostics, patient-specific surgical planning, and the advent of intelligent surgical robots is underscored, promising a future where technology and healthcare converge to offer unprecedented solutions in neurosurgery.