Artificial intelligence applied in acute ischemic stroke: from child to elderly

Pacchiano, Francesco; Tortora, Mario; Criscuolo, Sabrina; Jaber, Katya; Acierno, Pasquale; De Simone, Marta; Tortora, Fabio; Briganti, Francesco; Caranci, Ferdinando

doi:10.1007/s11547-023-01735-1

Cited by 6 publications

(1 citation statement)

References 67 publications

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“…A vast array of studies has employed machine learning and statistical methods for AIS prediction. However, most of these studies focus on the prognosis of AIS, while research specifically aimed at predicting the incidence of AIS is less common [37][38][39] . Studies focusing on AIS incidence risk frequently address AIS as a uniform condition or may introduce a single stratifying factor, such as hypertension or diabetes, to forecast AIS occurrences 11,12 .…”

Section: Discussionmentioning

confidence: 99%

Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack

Shu,

Zheng,

Luo

et al. 2024

Sci Rep

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Elderly hypertensive patients diagnosed with transient ischemic attack (TIA) are at a heightened risk for developing acute ischemic stroke (AIS). This underscores the critical need for effective risk prediction and identification of predictive factors. In our study, we utilized patient data from peripheral blood tests and clinical profiles within hospital information systems. These patients were followed for a three-year period to document incident AIS. Our cohort of 11,056 individuals was randomly divided into training, validation, and testing sets in a 5:2:3 ratio. We developed an XGBoost model, developed using selected indicators, provides an effective and non-invasive method for predicting the risk of AIS in elderly hypertensive patients diagnosed with TIA. Impressively, this model achieved a balanced accuracy of 0.9022, a recall of 0.8688, and a PR-AUC of 0.9315. Notably, our model effectively encapsulates essential data variations involving mixed nonlinear interactions, providing competitive performance against more complex models that incorporate a wider range of variables. Further, we conducted an in-depth analysis of the importance and sensitivity of each selected indicator and their interactions. This research equips clinicians with the necessary tools for more precise identification of high-risk individuals, thereby paving the way for more effective stroke prevention and management strategies.

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Section: Discussionmentioning

confidence: 99%

Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack

Shu,

Zheng,

Luo

et al. 2024

Sci Rep

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The most efficient machine learning algorithms in stroke prediction: A systematic review

Asadi,

Rahimi,

Daeechini

et al. 2024

Health Science Reports

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AbstracBackground and AimsStroke is one of the most common causes of death worldwide, leading to numerous complications and significantly diminishing the quality of life for those affected. The purpose of this study is to systematically review published papers on stroke prediction using machine learning algorithms and introduce the most efficient machine learning algorithms and compare their performance. The papers have published in period from 2019 to August 2023.MethodsThe authors conducted a systematic search in PubMed, Scopus, Web of Science, and IEEE using the keywords “Artificial Intelligence,” “Predictive Modeling,” “Machine Learning,” “Stroke,” and “Cerebrovascular Accident” from 2019 to August 2023.ResultsTwenty articles were included based on the inclusion criteria. The Random Forest (RF) algorithm was introduced as the best and most efficient stroke ML algorithm in 25% of the articles (n = 5). In addition, in other articles, Support Vector Machines (SVM), Stacking and XGBOOST, DSGD, COX& GBT, ANN, NB, and RXLM algorithms were introduced as the best and most efficient ML algorithms in stroke prediction.ConclusionThis research has shown a rapid increase in using ML algorithms to predict stroke, with significant improvements in model accuracy in recent years. However, no model has reached 100% accuracy or is entirely error‐free. Variations in algorithm efficiency and accuracy stem from differences in sample sizes, datasets, and data types. Further studies should focus on consistent datasets, sample sizes, and data types for more reliable outcomes.

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Artificial intelligence in fracture detection on radiographs: a literature review

Lo Mastro,

Grassi,

Berritto

et al. 2024

Jpn J Radiol

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Artificial intelligence applied in acute ischemic stroke: from child to elderly

Cited by 6 publications

References 67 publications

Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack

Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack

The most efficient machine learning algorithms in stroke prediction: A systematic review

Artificial intelligence in fracture detection on radiographs: a literature review

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