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Shah, S.P.; Heiss, J.D. Applications of Artificial Intelligence in Stroke and Epilepsy. Encyclopedia. Available online: https://encyclopedia.pub/entry/56079 (accessed on 14 April 2024).
Shah SP, Heiss JD. Applications of Artificial Intelligence in Stroke and Epilepsy. Encyclopedia. Available at: https://encyclopedia.pub/entry/56079. Accessed April 14, 2024.
Shah, Smit P., John D. Heiss. "Applications of Artificial Intelligence in Stroke and Epilepsy" Encyclopedia, https://encyclopedia.pub/entry/56079 (accessed April 14, 2024).
Shah, S.P., & Heiss, J.D. (2024, March 11). Applications of Artificial Intelligence in Stroke and Epilepsy. In Encyclopedia. https://encyclopedia.pub/entry/56079
Shah, Smit P. and John D. Heiss. "Applications of Artificial Intelligence in Stroke and Epilepsy." Encyclopedia. Web. 11 March, 2024.
Applications of Artificial Intelligence in Stroke and Epilepsy
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Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties—Stroke and Epilepsy in particular—clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence [AI] can predict the expected neurological impairment from an AIS [Acute Ischemic Stroke], the possibility of ICH [IntraCranial Hemorrhage] expansion, and the clinical outcomes of comatose patients.

artificial intelligence stroke epilepsy decision-making neurology

1. Introduction

In the coming years, the complexity of data used in Neurology’s clinical and research aspects will proliferate. Electronic medical records hold vast amounts of information. Major health systems rely on data-heavy technology to analyze clinical and genomic information. Computer analysis of digital medical data could aid the neurologist in making diagnoses, detecting disease patterns, and detecting health vulnerabilities. With its sophisticated machine learning algorithms, AI offers efficient and practical tools to clinicians to better interpret, access, and understand clinical information and narrow differential diagnoses in simple and complex cases [1][2]. AI has demonstrated great clinical utility in the management of Migraines as demonstrated by Torrente A. et al. [3]. Due to a high incidence of Stroke and Epilepsy in United States, which have been leading causes of morbidity and mortality, it would like to focus, exhibit, and discuss potential applications of AI in these two fields specifically by presenting the literature review and innovations so far, which can serve as great clinical adjuncts for clinicians which, in turn, can help deliver excellent patient care. Artificial intelligence could aid the neurology subspecialties of stroke and epilepsy by increasing the speed and consistency of analysis of clinical imaging studies and other data and clinical decision-making. Artificial intelligence can use evidence-based medicine practices to assure that the most modern and accepted medicine is being delivered. Artificial intelligence systems draw on extensive data sets of clinical information and are less prone than humans to have recency, recall, and other biases that can lead to inaccurate conclusions or ranking of the likelihood of the various diagnoses in a differential diagnosis. AI can help usher the era of personalized medicine into routine neurology clinical practice.

2. Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy

A growing body of literature suggests that artificial intelligence is becoming an invaluable tool for stroke and epilepsy clinicians. Studies report AI applications complementing traditional neurological care and improving diagnostic accuracy and clinical outcomes. As discussed above, early AI applications in the 2000s used clustering to analyze MRI sequences for regional brain perfusion properties. AI applications are standard care tools at the major level in CSCs [Comprehensive Stroke Centers] for analyzing CT perfusion studies and detecting large vessel occlusion [LVO]. The field of Stroke Neurology has improved its care systems by perfecting diagnostics and hastening stroke care. For example, AI tools can help minimize transfer time and improve outcomes by shortening the time to treatment with thrombolytics or mechanical thrombectomy. CT perfusion studies hold data critical to evaluating the cerebral vascular physiology after a stroke. A fundamental measure is rCBF [relative Cerebral Blood Flow], the flow rate through the vasculature in the brain region of interest [ROI]. Other measures include rCBV [relative Cerebral Blood Volume], the volume of blood within the ROI vasculature; MTT [Mean Transit Time], the average time for arterial-to-venous blood transit through infarcted tissue; and TTP [Time-To-Peak] the time interval between first appearance to peak enhancement of contrast-containing blood in the arterial vessels [4]. These CT perfusion imaging factors help assess the Mismatch Ratio and the infarct Core. Clinical decisions on the likelihood of improvement with mechanical thrombectomy consider these measures and the Modified Ranking Score [mRS]. AI assures clinical decisions are evidence-based, consistent with diagnostic and treatment guidelines, and give proper weight to relevant diagnostic and prognostic factors.
Acute decision-making in AIS uses AI for rapid and reliable analysis of perfusion and vessel imaging. AI has vessel-imaging applications beyond the AIS setting. For example, in the setting of intracranial atheromatous disease or multiple vascular risk factors, AI can help predict cognitive impairment and other patient outcomes in a patient. Physicians can explore the nonemergent role of AI in vessel imaging by using Deep Convoluted Neural Networks and Generative Adversarial Networks to generate automated perfusion maps that stratify a patient’s AIS risk.
Convoluted Deep Neural Networks have been used extensively to predict the prognosis of ICH patients. In addition, AI software can detect ICH and chronic cerebral microbleeds, ascertain ICH volume, and predict the rate of ICH expansion. AI can aid in emergency room intake neuroimaging of patients with suspected ICH. AI methods give clinicians precise volumetric and quantitative analysis of ICH’s intraparenchymal and intraventricular components, guiding treatment that may lower the morbidity and mortality of ICH in these patients. Additionally, AI analysis of serial imaging in an ICU-level setting may guide physician prognostication of ICH expansion or stability and patient outcome. Some AI studies estimate the functional outcomes of ICH patients. A physician knowing the outcome AI predicts and the relevant prognostic clinical information not considered by the AI can give patients’ families an evidence-based view of the expected ICH outcome that aids decision-making.
In Epilepsy, AI can detect ictal and interictal patterns in routine and long-term EEGs. AI-based EEG analysis can be applied to adult and pediatric epilepsy patients. AI programs may provide clinicians with information about which AED regimen would lead to better seizure control for patients with known epilepsy syndromes or genetic mutations predisposing patients to epilepsy. Also, using AI, the risk of epileptogenicity of focal MRI lesions can be predicted by routine or 1 h EEGs. This information can guide the decision for advanced neuroimaging for epilepsy patients who are epilepsy surgery candidates. This would be key in the current era given the significant evolution of surgical application in treatment refractory epilepsy patients and severely morbid conditions leading to epilepsy including Tuberous Sclerosis and Rasmussen’s Encephalitis.
Artificial intelligence’s continued adoption in neurology depends on clinicians and researchers continuing to test and improve AI prediction models. The quality improvement models used in industry can be used to continually improve AI by reducing diagnostic and other experience-based prediction errors. As new AI methods and protocols evolve, medical experts should iteratively compare expected and actual results to judge their validity, accuracy, and clinical value. Designing an AI algorithm is a plan, or hypothesis, that the algorithm will be of clinical value. However, testing an AI algorithm allows iterative scientific hypothesis testing and revision until the hypothesis fits the data. After the final version of the algorithm fits the practice data set, the algorithm is tested with new data to assess its accuracy and error rate. After that, the algorithm is revised as necessary using quality improvement methods. The quality improvement steps are [1] Plan, [5] Do, [6] Check, and [7] Act- PDCA cycle [8].
A sole human clinician can only see a tiny fraction of the patients covered by an extensive healthcare system and knows his patient outcomes, those reported by his colleagues, and those reported in the clinical literature. AI can potentially draw upon data from the entire healthcare system to derive diagnostic and prognostic information that can fill gaps in a neurologist’s experience or serve as reminders before decision-making. AI can retrospectively mine data for suspected and unsuspected factors leading to an AIS or ICH that could inform future medical treatment of at-risk individuals in a neurologist’s and primary care physician’s practice.
The PDCA quality improvement cycle rigorously reviews the predicted and actual outcomes of AI-based methods, leading to their progressive updating and improvement. The AI models from practice data sets are tested with new clinical information and revised appropriately. Testing of mature AI models with new data assesses their clinical value and error rate. AI models can be revised and re-tested iteratively until their accuracy is clinically valuable. Many organizations and companies adopted the Deming PDCA cycle to improve their systems and functional outcomes. Implementing the PDCA concept can ensure AI-based protocols have continued quality improvement, regular checks to assess their outcomes, and are developed into clinically valuable and reliable products.

References

  1. Auger, S.D.; Jacobs, B.M.; Dobson, R.; Marshall, C.R.; Noyce, A.J. Big data, machine learning and artificial intelligence: A neurologist’s guide. Pract. Neurol. 2020, 21, 4–11.
  2. Singh, A.; Velagala, V.R.; Kumar, T.; Dutta, R.R.; Sontakke, T. The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative Review. Cureus 2023, 15, e42460.
  3. Torrente, A.; Maccora, S.; Prinzi, F.; Alonge, P.; Pilati, L.; Lupica, A.; Di Stefano, V.; Camarda, C.; Vitabile, S.; Brighina, F. The Clinical Relevance of Artificial Intelligence in Migraine. Brain Sci. 2024, 14, 85.
  4. Khandelwal, N. CT perfusion in acute stroke. Indian J. Radiol. Imaging 2008, 18, 281–286.
  5. Vinny, P.; Vishnu, V.; Srivastava, M.P. Artificial Intelligence shaping the future of neurology practice. Med. J. Armed. Forces India 2021, 77, 276–282.
  6. Koza, J.R.; Bennett, F.H.; Andre, D.; Keane, M.A. Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. In Artificial Intelligence in Design ’96; Gero, J.S., Sudweeks, F., Eds.; Springer: Dordrecht, The Netherlands, 1996; pp. 151–170. ISBN 978-94-010-6610-5.
  7. Mohri, M.; Rostamizadeh, A.; Talwalkar, A. Foundations of Machine Learning; The MIT Press: Cambridge, MA, USA, 2012; ISBN 9780262018258.
  8. QA for Machine Learning Models With the PDCA Cycle. Available online: https://dzone.com/articles/qa-for-machine-learning-models-with-the-pdca-cycle (accessed on 1 February 2024).
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