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

Wen, Zhongjian; Wang, Yiren; Zhong, Yuxin; Hu, Yiheng; Yang, Cheng; Peng, Yan; Zhan, Xiang; Zhou, Ping; Zeng, Zhen

doi:10.3389/fneur.2024.1391382

Cited by 4 publications

(3 citation statements)

References 56 publications

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“…Federated learning offers an innovative solution for data privacy protection and utilization problems ( 63 ). This distributed machine-learning method initializes a global model through a central server and distributes it to all participating devices.…”

Section: Current Challenges and Future Prospectsmentioning

confidence: 99%

Artificial intelligence in ischemic stroke images: current applications and future directions

Liu,

Wen,

Wang

et al. 2024

Front. Neurol.

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This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

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Section: Current Challenges and Future Prospectsmentioning

confidence: 99%

Artificial intelligence in ischemic stroke images: current applications and future directions

Liu,

Wen,

Wang

et al. 2024

Front. Neurol.

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“…It involves random sampling with replacement from the training dataset to create multiple subsets for model validation, reducing the variance of validation results and ensuring more reliable evaluations compared to proportional splits, especially with small sample sizes ( 9 , 10 ). SHAP (SHapley Additive exPlanations), based on cooperative game theory, offers clear explanations for feature contribution values, bridging the gap between complex algorithms and clinical application, ensuring transparency and traceability in model-based decision-making, which is crucial for the scientific validity and credibility of medical decisions ( 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study

Wen,

Wang,

Chen

et al. 2024

Front. Oncol.

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ObjectiveThis study aims to construct a predictive model based on machine learning algorithms to assess the risk of prolonged hospital stays post-surgery for colorectal cancer patients and to analyze preoperative and postoperative factors associated with extended hospitalization.MethodsWe prospectively collected clinical data from 83 colorectal cancer patients. The study included 40 variables (comprising 39 predictor variables and 1 target variable). Important variables were identified through variable selection via the Lasso regression algorithm, and predictive models were constructed using ten machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Light Gradient Boosting Machine, KNN, and Extreme Gradient Boosting, Categorical Boosting, Artificial Neural Network and Deep Forest. The model performance was evaluated using Bootstrap ROC curves and calibration curves, with the optimal model selected and further interpreted using the SHAP explainability algorithm.ResultsTen significantly correlated important variables were identified through Lasso regression, validated by 1000 Bootstrap resamplings, and represented through Bootstrap ROC curves. The Logistic Regression model achieved the highest AUC (AUC=0.99, 95% CI=0.97–0.99). The explainable machine learning algorithm revealed that the distance walked on the third day post-surgery was the most important variable for the LR model.ConclusionThis study successfully constructed a model predicting postoperative hospital stay duration using patients’ clinical data. This model promises to provide healthcare professionals with a more precise prediction tool in clinical practice, offering a basis for personalized nursing interventions, thereby improving patient prognosis and quality of life and enhancing the efficiency of medical resource utilization.

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“…It involves random sampling with replacement from the training dataset to create multiple subsets for model validation, reducing the variance of validation results and ensuring more reliable evaluations compared to proportional splits, especially with small sample sizes (9,10). SHAP (SHapley Additive exPlanations), based on cooperative game theory, offers clear explanations for feature contribution values, bridging the gap between complex algorithms and clinical application, ensuring transparency and traceability in model-based decision-making, which is crucial for the scientific validity and credibility of medical decisions (11,12).…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 4 publications

References 56 publications

Artificial intelligence in ischemic stroke images: current applications and future directions

Artificial intelligence in ischemic stroke images: current applications and future directions

Construction of a predictive model for postoperative hospitalization time in colorectal cancer patients based on interpretable machine learning algorithm: a prospective preliminary study

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