Prediction of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Artificial Intelligence Techniques.

Purpose: This study aimed to develop artificial intelligence models for predicting postoperative functional outcomes in patients with rhegmatogenous retinal detachment (RRD). Methods: A retrospective review and data extraction were conducted on 184 patients diagnosed with RRD who underwent pars plana vitrectomy (PPV) and gas tamponade. The primary outcome was the best-corrected visual acuity (BCVA) at three months after the surgery. Those with a BCVA of less than 6/18 Snellen acuity were classified into a vision impairment group. A deep learning model was developed using presurgical predictors, including ultra-widefield fundus images, structural optical coherence tomography (OCT) images of the macular region, age, gender, and preoperative BCVA. A fusion method was used to capture the interaction between different modalities during model construction. Results: Among the participants, 74 (40%) still had vision impairment after the treatment. There were significant differences in age, gender, presurgical BCVA, intraocular pressure, macular detachment, and extension of retinal detachment between the vision impairment and vision non-impairment groups. The multimodal fusion model achieved a mean area under the curve (AUC) of 0.91, with a mean accuracy of 0.86, sensitivity of 0.94, and specificity of 0.80. Heatmaps revealed that the macular involvement was the most active area, as observed in both the OCT and ultra-widefield images. Conclusions: This pilot study demonstrates that artificial intelligence techniques can achieve a high AUC for predicting functional outcomes after RRD surgery, even with a small sample size. Machine learning methods identified The macular region as the most active region. Translational Relevance: Multimodal fusion models have the potential to assist clinicians in predicting postoperative visual outcomes prior to undergoing PPV.

Flow diverters treatment planning of small- and medium-sized intracranial saccular aneurysms on the internal carotid artery via constraint-based virtual deployment.

PURPOSE: The internal carotid artery (ICA) is a region with a high incidence for small- and medium-sized saccular aneurysms. However, the treatment relies heavily on the surgeon's experience to achieve optimal outcome. Although the finite element method (FEM) and computational fluid dynamics can predict the postoperative outcomes, due to the computational complexity of traditional methods, there is an urgent need for investigating the fast but versatile approaches related to numerical simulations of flow diverters (FDs) deployment coupled with the hemodynamic analysis to determine the treatment plan. METHODS: We collected the preoperative and postoperative data from 34 patients (29 females, 5 males; mean age 55.74 ± 9.98 years) who were treated with a single flow diverter for small- to medium-sized intracranial saccular aneurysms on the ICA. The constraint-based virtual deployment (CVD) method is proposed to simulate the FDs expanding outward along the vessel centerline while be constrained by the inner wall of the vessel. RESULTS: The results indicate that there were no significant differences in the reduction rates of wall shear stress and aneurysms neck velocity between the FEM and methods. However, the solution time of CVD was greatly reduced by 98%. CONCLUSION: In the typical location of small- and medium-sized saccular aneurysms, namely the ICA, our virtual FDs deployment simulation effectively balances the computational accuracy and efficiency. Combined with hemodynamics analysis, our method can accurately represent the blood flow changes within the lesion region to assist surgeons in clinical decision-making.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Colour fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfil the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus colour photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyse their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniques and strategies that could be of practical value for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will serve as an essential guideline and benchmark for future research.

Cellular energy supply for promoting vascular remodeling of small-diameter vascular grafts: a preliminary study of a new strategy for vascular graft development.

Rapid endothelialization is extremely essential for the success of small-diameter tissue-engineered vascular graft (TEVG) (<6 mm) transplantation. However, severe inflammation in situ often causes cellular energy decline of endothelial cells. The cellular energy supply involved in vascular graft therapy remains unclear, and whether promoting energy supply would be helpful in the regeneration of vascular grafts needs to be established. In our work, we generated an AMPK activator (5-aminoimidazole-4-carboxamide ribonucleotide, AICAR) immobilized vascular graft. AICAR-modified vascular grafts were successfully generated by the co-electrospinning technique. In vitro results indicated that AICAR could upregulate energy supply in endothelial cells and reprogram macrophages (MΦ) to assume an anti-inflammatory phenotype. Furthermore, endothelial cells (ECs) co-cultured with AICAR achieved higher survival rates, better migration, and angiogenic capacity than the controls. Concurrently, a rabbit carotid artery transplantation model was used to investigate AICAR-modified vascular grafts at different time points. The results showed that AICAR-modified vascular grafts had higher patency rates (92.9% and 85.7% at 6 and 12 weeks, respectively) than those of the untreated group (11.1% and 0%). In conclusion, AICAR strengthened the cellular energy state and attenuated the adverse effects of inflammation. AICAR-modified vascular grafts achieved better vascular remodeling. This study provides a new perspective on promoting the regeneration of small-diameter vascular grafts.

Evaluation of aneurysm rupture risk based upon flowrate-independent hemodynamic parameters: a multi-center pilot study.

BACKGROUND: Specifying generic flow boundary conditions in aneurysm hemodynamic simulations yields a great degree of uncertainty for the evaluation of aneurysm rupture risk. Herein, we proposed the use of flowrate-independent parameters in discriminating unstable aneurysms and compared their prognostic performance against that of conventional absolute parameters. METHODS: This retrospective study included 186 aneurysms collected from three international centers, with the stable aneurysms having a minimum follow-up period of 24 months. The flowrate-independent aneurysmal wall shear stress (WSS) and energy loss (EL) were defined as the coefficients of the second-order polynomials characterizing the relationships between the respective parameters and the parent-artery flows. Performance of the flowrate-independent parameters in discriminating unstable aneurysms with the logistic regression, Adaboost, and support-vector machine (SVM) methods was quantified and compared against that of the conventional parameters, in terms of sensitivity, specificity, and area under the curve (AUC). RESULTS: In discriminating unstable aneurysms, the proposed flowrate-independent EL achieved the highest sensitivity (0.833, 95% CI 0.586 to 0.964) and specificity (0.833, 95% CI 0.672 to 0.936) on the SVM, with the AUC outperforming the conventional EL by 0.133 (95% CI 0.039 to 0.226, p=0.006). Likewise, the flowrate-independent WSS outperformed the conventional WSS in terms of the AUC (difference: 0.137, 95% CI 0.033 to 0.241, p=0.010). CONCLUSION: The flowrate-independent hemodynamic parameters surpassed their conventional counterparts in predicting the stability of aneurysms, which may serve as a promising set of hemodynamic metrics to be used for the prediction of aneurysm rupture risk when physiologically real vascular boundary conditions are unavailable.

A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata.

Introduction: Skin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones. Methods: A deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation. Results: Including metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007. Conclusion: A deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.

A deep learning-based automatic system for intracranial aneurysms diagnosis on three-dimensional digital subtraction angiographic images.

BACKGROUND: Intracranial aneurysms (IAs) are a life-threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images. However, critical diagnostic information such as morphology and aneurysm location are not captured by deep learning algorithms and still require manual assessments. PURPOSE: Digital subtraction angiography (DSA) is the gold standard for aneurysm diagnosis. To facilitate the fully automatic diagnosis of aneurysms, we proposed a comprehensive system for the detection, morphology measurement, and location classification of aneurysms on three-dimensional DSA images, allowing automatic diagnosis without further human input. METHODS: The system comprised three neural networks: a network for aneurysm detection, a network for morphology measurement, and a network for aneurysm location identification. A cross-scale dual-path transformer module was proposed to effectively fuse local and global information to capture aneurysms of varying sizes. A multitask learning approach was also proposed to allow an accurate localization of aneurysm neck for morphology measurement. RESULTS: The cross-scale dual-path transformer module was shown to outperform other state-of-the-art network architectures, improving segmentation, and classification accuracy. The detection network in our system achieved an F2 score of 0.946 (recall 93%, precision 100%), better than the winning team in the Cerebral Aneurysm Detection and Analysis challenge. The measurement network achieved a relative error of less than 10% for morphology measurement, at the same level as human operators. Perfect accuracy (100%) was achieved on aneurysm location classification. CONCLUSIONS: We have demonstrated that a comprehensive system can automatically detect, measure morphology and report the aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of IAs, improving radiologists' performance and reducing their workload.

Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction.

OBJECTIVES: We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data. METHOD: Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons. RESULT: Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system. CONCLUSION: Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture. KEY POINTS: • A self-supervised learning method was proposed to mitigate the data-hungry issue of deep learning, enabling training deep neural network with a limited amount of data. • Using the proposed method, deep embeddings were extracted to represent intracranial aneurysm morphology. Prediction model based on deep embeddings was significantly better than conventional morphology model and radiomics model. • An assistive diagnosis system was developed using deep embeddings for case-based reasoning, which was shown to significantly improve neurosurgeons' performance to predict rupture.

Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study.

Background: The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons. Objectives: The recently emerged automated machine learning (AutoML) has shown promise in making ML more accessible to non-computer experts. We aimed to evaluate the feasibility of applying AutoML to develop the ML models for treatment outcome prediction. Methods: The patients with aneurysms treated by endovascular treatment were prospectively recruited from 2016 to 2020. Treatment was considered successful if angiographic complete occlusion was achieved at follow-up. A statistical prediction model was developed using multivariate logistic regression. In addition, two ML models were developed. One was developed manually and the other was developed by AutoML. Three models were compared based on their area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC). Results: The aneurysm size, stent-assisted coiling (SAC), and posterior circulation were the three significant and independent variables associated with treatment outcome. The statistical model showed an AUPRC of 0.432 and AUROC of 0.745. The conventional manually trained ML model showed an improved AUPRC of 0.545 and AUROC of 0.781. The AutoML derived ML model showed the best performance with AUPRC of 0.632 and AUROC of 0.832, significantly better than the other two models. Conclusions: This study demonstrated the feasibility of using AutoML to develop a high-quality ML model, which may outperform the statistical model and manually derived ML models. AutoML could be a useful tool that makes ML more accessible to the clinical researchers.

Flow diverter modeled as heterogeneous and anisotropic porous medium: Simulation, experimental validation and case analysis.

Simulation of flow diverter (FD) treated aneurysm can evaluate treatment efficacy and aid treatment planning. However, explicit modeling of thin wires of FD impose extremely high demand of computational resources and time, which limit its use in time-sensitive presurgical planning. One alternative approach is to model FD as homogenous porous medium, which saves time but with compromise in accuracy. We proposed a new method to model FD as heterogeneous and anisotropic porous medium whose properties were determined from local porosity. The new method was validated by comparing with PIV measurement from an in-vitro phantom. Simulation result was in good agreement with experimental measurement. Four patient cases were further analyzed to compare the new method with the homogenous porous media method. Results showed that in patient cases with curved artery, new method was preferred over the homogenous method, as the assumption of homogenous porosity led to overpredicted flow reduction effect by as much as 87.9%, which may lead to overoptimistic decision making and poor prognosis. Our new method can provide timely and accurate simulation to aid in the treatment planning of aneurysms.

A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms.

OBJECTIVES: Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture. METHODS: One hundred twenty-two aneurysms were included in the study (93 unruptured). Morphological and radiomics features were extracted for each case. Statistical analysis was performed to identify significant features which were incorporated into prediction models constructed with a machine learning algorithm. To investigate the usefulness of radiomics features, three models were constructed and compared. The baseline model A was constructed with morphological features, while model B was constructed with addition of radiomics shape features and model C with more radiomics features. Multivariate analysis was performed for the ten most important variables in model C to identify independent risk factors. A simplified model based on independent risk factors was constructed for clinical use. RESULTS: Five morphological features and 89 radiomics features were significantly associated with rupture. Model A, model B, and model C achieved the area under the receiver operating characteristic curve of 0.767, 0.807, and 0.879, respectively. Model C was significantly better than model A and model B (p < 0.001). Multivariate analysis identified two radiomics features which were used to construct the simplified model showing an AUROC of 0.876. CONCLUSIONS: Radiomics signatures were different between ruptured and unruptured aneurysms. The use of radiomics features, especially texture features, may significantly improve rupture prediction performance. KEY POINTS: • Significant radiomics differences exist between ruptured and unruptured intracranial aneurysms. • Radiomics shape features can significantly improve rupture prediction performance over conventional morphology-based prediction model. The inclusion of histogram and texture radiomics features can further improve the performance. • A simplified model with two variables achieved a similar level of performance as the more complex ones. Our prediction model can serve as a promising tool for the risk management of intracranial aneurysms.

Elevated Lipid Infiltration Is Associated With Cerebral Aneurysm Rupture.

Background: Intracranial aneurysm wall degradation can be associated with lipid infiltration. However, the relationship between lipid infiltration and aneurysm rupture has not been explored quantitatively. To investigate the correlation between lipid infiltration and aneurysm rupture, we utilized patient-specific simulation of low-density lipoprotein (LDL) transport to analyze lipid infiltration in the cerebral aneurysm wall. Methods: Sixty-two aneurysms were analyzed. Patient blood pressure, plasma LDL concentration, and three-dimensional angiographic images were obtained to simulate LDL transport in aneurysms. Morphological, hemodynamic, and lipid accumulation parameters were compared between ruptures and unruptured groups. Multivariate logistic regression was also performed to determine parameters that are independently associated with rupture. Results: Size ratio, wall shear stress, low shear area, relative residence time, area-averaged LDL infiltration rate, and maximum LDL infiltration rate were significant parameters in univariate analysis (P < 0.05). Multivariate analysis revealed that only average LDL infiltration remained as a significant variable (P < 0.05). The prediction model derived showed good performance for rupture prediction (AUC, 0.885; 95% CI, 0.794-0.976). Conclusions: Ruptured aneurysms showed significantly higher LDL infiltration compared to unruptured ones. Our results suggested that lipid infiltration may promote aneurysm rupture. Lipid infiltration characteristics should be considered when assessing aneurysm rupture risk.

Deep learning for automated cerebral aneurysm detection on computed tomography images.

PURPOSE: Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medical centers and different machines to develop and evaluate an automatic detection model. METHODS: In this study, we have introduced a deep learning model, the faster RCNN model, in order to develop a tool for automatic detection of aneurysms from medical images. The inputs of the model were 2D nearby projection (NP) images from 3D CTA, which were made by the NP method proposed in this study. This method made aneurysms clearly visible on images and improved the model's performance. The study included 311 patients with 352 aneurysms, selected from three hospitals, and 208 and 103 of these patients, respectively, were randomly selected to train and test the models. RESULTS: The sensitivity of the trained model was 91.8%. For aneurysm sizes larger than 3 mm, the sensitivity of successful aneurysm detection was 96.7%. We achieved state-of-the-art sensitivity for > 3 mm aneurysms. The sensitivities also indicated that there was no significant difference among aneurysms at different locations in the body. Computing time for the detection process was less than 25 s per case. CONCLUSIONS: We successfully developed a deep learning model that can automatically detect aneurysms. The model performed well for aneurysms of different sizes or in different locations. This finding indicates that the deep learning model has the potential to vastly improve clinician performance by providing automated aneurysm detection.

Rupture Risk Assessment for Cerebral Aneurysm Using Interpretable Machine Learning on Multidimensional Data.

Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment. Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model. Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838-0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729-0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713-0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model. Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.

Morphological and Hemodynamic Differences Between Aneurysmal Middle Cerebral Artery Bifurcation and Contralateral Nonaneurysmal Anatomy.

BACKGROUND: The morphological and hemodynamic features differ between middle cerebral artery (MCA) bifurcations with and without aneurysms. OBJECTIVE: To investigate the morphological and hemodynamic differences between aneurysmal MCA bifurcation and contralateral nonaneurysmal anatomy. METHODS: Computed tomography angiography of 36 patients with unilateral small saccular MCA bifurcation aneurysms was evaluated. The parent-daughter angles (φ1 for larger branch and φ2 for smaller branch), bifurcation angle (φ = φ1 + φ2), inclination angle (γ angle), and their relationships with the MCA bifurcation locations were analyzed. Computational fluid dynamics simulation was performed in 6 cases to explore the hemodynamics influenced by the bifurcation morphology. RESULTS: The φ angle was significantly higher in aneurysmal than contralateral nonaneurysmal bifurcations (160.8° ± 31.0° vs 99.0° ± 19.2°, respectively; P = .000); the φ1, φ2, and γ angles were also higher. However, by regression analysis combined with MCA bifurcation locations, only the φ angle might be associated with the aneurysm presence (odds ratio = 1.120, 95% confidence interval = 1.059-1.185) and a φ angle cut-off of 124.8° was established. Computational fluid dynamics simulation demonstrated that flow resistance of the wider aneurysmal MCA bifurcation was significantly higher than that on the contralateral side. CONCLUSION: A larger φ angle was more prevalent in aneurysmal than nonaneurysmal MCA bifurcations, and the higher flow resistance caused by the larger φ angle might be a potential hemodynamic factor associated with MCA aneurysm presence.

A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms.

Flow diverters, the specially designed low porosity stents, have been used to redirect blood flow from entering aneurysm, which induces flow stasis in aneurysm and promote thrombosis for repairing aneurysm. However, it is not clear how thrombus develops following flow-diversion treatment. Our objective was to develop a computation model for the prediction of stasis-induced thrombosis following flow-diversion treatment in cerebral aneurysms. We proposed a hypothesis to initiate coagulation following flow-diversion treatment. An experimental model was used by ligating rat's right common carotid artery (RCCA) to create flow-stasis environment. Thrombus formed in RCCA as a result of flow stasis. The fibrin distributions in different sections along the axial length of RCCA were measured. The fibrin distribution predicted by our computational model displayed a trend of increase from the proximal neck to the distal tip, consistent with the experimental results on rats. The model was applied on a saccular aneurysm treated with flow diverter to investigate thrombus development following flow diversion. Thrombus was predicted to form inside the sac, and the aneurysm was occluded with only a small remnant neck remained. Our model can serve as a tool to evaluate flow-diversion treatment outcome and optimize the design of flow diverters.

Hemodynamic modeling of leukocyte and erythrocyte transport and interactions in intracranial aneurysms by a multiphase approach.

Hemodynamics has been recognized as an important factor in the development, growth, and rupture of cerebral aneurysms, and investigated by computational fluid dynamics techniques using a single phase approach. However, flow-dependent cell transport and interactions are usually ignored in single phase models, in which blood is usually treated as a single phase Newtonian fluid. For getting better insight into the underlying pathology of intracranial aneurysm, cell transport and interactions should be covered in hemodynamic studies. In the present study, a multiphase hemodynamic model incorporating cell transport and interactions was developed, in which blood was modeled as multiphase fluid having a continuous phase (plasma) and two particulate phases (erythrocytes and leukocytes). The model showed good agreement with experimental data and observations in the literature, and was applied to four patient-specific aneurysms in a pulsatile manner. Leukocyte accumulations were predicted at locations with flow disturbance and low wall shear stress. The concentrations of leukocyte at accumulation sites were found to exceed 200 to 500% of normal physiological level on three unstable aneurysms, including two ruptured aneurysms and a growing aneurysm where accumulation was observed near a daughter sac and a secondary aneurysm. This suggested that aneurysms with complex secondary flow patterns could be prone to leukocyte accumulation on the wall. As this is the first study to characterize cell transport and interactions in aneurysm hemodynamics, our model can serve as a foundation for future intracranial aneurysm models.