Evidence map›Paper›PMID 39425221›Full record

ArticleEuropean journal of medical research2024

Artificial intelligence applied to development of predictive stability model for intracranial aneurysms.

Junmin Tao, Wei Wei, Meiying Song, Mengdie Hu, Heng Zhao, Shen Li, Hui Shi, Luzhu Jia, Chun Zhang, Xinyue Dong and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Junmin Tao *Department of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China.
Wei Wei *Cardiovascular and Cerebrovascular Research Institute, The Central Hospital of Dalian University of Technology, Dalian, Liaoning Province, China.
Meiying SongDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China.
Mengdie HuDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China.
Heng ZhaoDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China.
Shen LiDepartment of Endocrinology Laboratory, The Central Hospital of Dalian University of Technology, Dalian, Liaoning Province, China.
Hui ShiHealth Management Center, The Central Hospital of Dalian University of Technology, Dalian, Liaoning Province, China.
Luzhu JiaDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China.
Chun ZhangDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China.
Xinyue DongDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China.
Xin ChenDepartment of Epidemiology, School of Public Health, Dalian Medical University, No. 9, West Section of Lvshun South Road, Lvshunkou District, Dalian, Liaoning Province, China. chenx@dmu.edu.cn.

Funding

Dalian Science and Technology Innovation Fund 2023JJ13SN045Scientific Research Fund of Liaoning Provincial Education Department LJKMZ20221270
6 · The paper itself

Abstract

backgroundWe aimed to develop multiple machine learning models to predict the risk of early intracranial aneurysms (IAs) rupture, evaluate and compare the performance of predictive models.

methodsInformation related to patients diagnosed with IA by CT angiography and clinicians in Central hospital of Dalian University of Technology from January 2010 to June 2022 was collected, including clinical characteristics, blood indicators and IA morphological parameters. IA with rupture or maximum growth ≥ 0.5 mm within 1 month of first diagnosis was considered unstable. The relevant factors affecting IA stability were screened and predictive models were developed based on the above three levels, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Sensitivity, specificity, accuracy and area under curve (AUC) value were used to evaluate the predictive models.

resultsA total of 989 IA patients were included in the study, including 561 stable patients and 428 unstable patients. For RF models, the training set showed that sensitivity, specificity, accuracy and the AUC values were 72.8-83.7%, 76.9-86.9%, 75.1-84.1% and 0.748 (0.719-0.778)-0.839 (0.814-0.864), respectively; after test set validation, the results were 71.9-78.8%, 75.0-84.0%, 73.6-81.1% and 0.734 (0.688-0.781)-0.809 (0.768-0.850), respectively. For SVM models, the training set were 66.0-80.2%, 76.5-85.5%, 71.7-82.3%, 0.712 (0.682-0.743)-0.913 (0.884-0.924), respectively; the test set were 44.2-78.3%, 63.4-84.4%, 57.9-80.9%, 0.699 (0.651-0.747)-0.806 (0.765-0.848), respectively. For ANN models, the training set were 66.8-83.0%, 75.3-82.3%, 71.6-82.1%, 0.783 (0.757-0.808)-0.897 (0.879-0.914); the test set were 63.1-76.3%, 65.5-84.0%, 64.4-80.6%, 0.680 (0.593-0.694)-0.860 (0.821-0.899). The results of variable importance showed that age, white blood cell count (WBC) and uric acid (UA) played an important role in predicting the stability of IA.

conclusionsThe predictive stability models of IA based on three artificial intelligence methods shows good clinical application. Age, WBC and UA played an important role in predicting the IA stability, and were potentially important predictors.

Indexed as

Artificial IntelligenceIntracranial AneurysmAdultAgedAneurysm, RupturedFemaleHumansMachine LearningMaleMiddle AgedNeural Networks, ComputerSupport Vector MachineArtificial intelligenceIntracranial aneurysmsMachine learningRisk prediction

Identifiers

PMID39425221
PMCPMC11490007

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.