Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

Abedi, Vida; Razavi, Seyed Mostafa; Khan, Ayesha; Avula, Venkatesh; Tompe, Aparna; Poursoroush, Asma; Sadr, Alireza Vafaei; Li, Jiang; Zand, Ramin

doi:10.3390/jcm10235710

Cited by 7 publications

(5 citation statements)

References 146 publications

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“…Artificial intelligence (AI) is driving innovation in cardiovascular and cerebrovascular medicine. 28 However, in the field of stroke, many studies are still solely retrospective with limited sample size and with inherent biases due to modeling strategies. 28 , 29 We developed two modeling pipelines to predict stroke in ED.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning-Based Prediction of Stroke in Emergency Departments

Abedi,

Misra,

Chaudhary

et al. 2024

Ther Adv Neurol Disord

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Background: Stroke misdiagnosis, associated with poor outcomes, is estimated to occur in 9% of all stroke patients. Objectives: We hypothesized that machine learning (ML) could assist in the diagnosis of ischemic stroke in emergency departments (EDs). Design: The study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines. We performed model development and prospective temporal validation, using data from pre- and post-COVID periods; we also performed a case study on a small cohort of previously misdiagnosed stroke patients. Methods: We used structured and unstructured electronic health records (EHRs) of 56,452 patient encounters from 13 hospitals in Pennsylvania, from September 2003 to January 2021. ML pipelines, including natural language processing, were created using pre-event clinical data and provider notes in the EDs. Results: Using pre-event information, our model’s area under the receiver operating characteristics curve (AUROC) ranged from 0.88 to 0.92 with a similar range accuracy (0.87–0.90). Using provider notes, we identified five models that reached a balanced performance in terms of AUROC, sensitivity, and specificity. Model AUROC ranged from 0.93 to 0.99. Model sensitivity and specificity reached 0.90 and 0.99, respectively. Four of the top five performing models were based on the post-COVID provider notes; however, no performance difference between models tested on pre- and post-COVID was observed. Conclusion: This study leveraged pre-event and at-encounter level EHR for stroke prediction. The results indicate that available clinical information can be used for building EHR-based stroke prediction models and ED stroke alert systems.

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Section: Discussionmentioning

confidence: 99%

“… 28 However, in the field of stroke, many studies are still solely retrospective with limited sample size and with inherent biases due to modeling strategies. 28 , 29 We developed two modeling pipelines to predict stroke in ED. The first was based on structured pre-event data, readily available in EHRs.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Prediction of Stroke in Emergency Departments

Abedi,

Misra,

Chaudhary

et al. 2024

Ther Adv Neurol Disord

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“…Over the last ten years, machine learning techniques have improved CAD detection and characterization, according to this survey. Despite the obstacles of using ML in a clinical setting, the power of novel ML algorithms drives significant discoveries in CAD classification, according to the findings (22).…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data

Shoaib,

Junaid,

Husnain

et al. 2024

Front. Cardiovasc. Med.

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The 2017 World Health Organization Fact Sheet highlights that coronary artery disease is the leading cause of death globally, responsible for approximately 30% of all deaths. In this context, machine learning (ML) technology is crucial in identifying coronary artery disease, thereby saving lives. ML algorithms can potentially analyze complex patterns and correlations within medical data, enabling early detection and accurate diagnosis of CAD. By leveraging ML technology, healthcare professionals can make informed decisions and implement timely interventions, ultimately leading to improved outcomes and potentially reducing the mortality rate associated with coronary artery disease. Machine learning algorithms create non-invasive, quick, accurate, and economical diagnoses. As a result, machine learning algorithms can be employed to supplement existing approaches or as a forerunner to them. This study shows how to use the CNN classifier and RNN based on the LSTM classifier in deep learning to attain targeted “risk” CAD categorization utilizing an evolving set of 450 cytokine biomarkers that could be used as suggestive solid predictive variables for treatment. The two used classifiers are based on these “45” different cytokine prediction characteristics. The best Area Under the Receiver Operating Characteristic curve (AUROC) score achieved is (0.98) for a confidence interval (CI) of 95; the classifier RNN-LSTM used “450” cytokine biomarkers had a great (AUROC) score of 0.99 with a confidence interval of 0.95 the percentage 95, the CNN model containing cytokines received the second best AUROC score (0.92). The RNN-LSTM classifier considerably beats the CNN classifier regarding AUROC scores, as evidenced by a p-value smaller than 7.48 obtained via an independent t-test. As large-scale initiatives to achieve early, rapid, reliable, inexpensive, and accessible individual identification of CAD risk gain traction, robust machine learning algorithms can now augment older methods such as angiography. Incorporating 65 new sensitive cytokine biomarkers can increase early detection even more. Investigating the novel involvement of cytokines in CAD could lead to better risk detection, disease mechanism discovery, and new therapy options.

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“…Artificial Intelligence can also be of great value in both cardio and cerebrovascular diseases in several important fields of interest, as in disease diagnosis and patient monitoring, in preventive care by scanning through images and reports, in risk stratification for primary or secondary prevention, and in resource and workflow optimization by leveraging administrative data [ 12 ].…”

Section: Ai Support In Clinical Carementioning

confidence: 99%