CT Scan Strategies for Early Stroke Diagnosis: A Mini Review for Medical Practitioners

Iskandar Zakaria


Background: Stroke is one of the leading causes of death and disability worldwide. Early detection and rapid intervention are crucial to reducing the adverse effects of stroke. In the last decade, the use of computed tomography (CT) scans has become the standard in stroke diagnosis. However, the main challenge medical practitioners face is the rapid and accurate interpretation of CT scan images for early signs of stroke. Objective: The main aim is to improve the accuracy and efficiency of stroke diagnosis early, thus enabling faster and more effective medical intervention. Methods: The research methodology involves using advanced algorithms and image analysis techniques to identify early signs of stroke on CT scan images. Results: This study reviewed a series of cases of patients with early stroke symptoms, comparing the results of manual analysis by medical practitioners with those of analysis using an improved computerized approach. This study significantly improved early stroke detection using optimized CT Scan image analysis methods. Compared to traditional methods, this approach offers higher accuracy, potentially reducing the time required for diagnosis. Conclusion: This study confirms that integrating advanced image analysis technology in medical practice can be essential in early stroke diagnosis. The implications of these findings are significant, especially in improving emergency medical response and stroke management, as well as in lowering the risk of long-term damage to patients.


: CT Scan, Early Diagnosis of Stroke, Image Analysis, Medical Technology, Neurology

Full Text:



Katan M, Luft A. Global burden of stroke. Paper presented at: Seminars in neurology, 2018.

Guberina N, Dietrich U, Radbruch A, et al. Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine. Neuroradiology 2018;60:889-901.

Mair G, Wardlaw JM. Imaging of acute stroke prior to treatment: current practice and evolving techniques. Br J Radiol 2014;87(1040):20140216.

Mokli Y, Pfaff J, Dos Santos DP, Herweh C, Nagel S. Computer-aided imaging analysis in acute ischemic stroke–background and clinical applications. Neurological Research and Practice 2019;1(1):1-13.

Herweh C, Ringleb PA, Rauch G, et al. Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients. International Journal of Stroke 2016;11(4):438-45.

Mendelow AD. Stroke: pathophysiology, diagnosis, and management: Elsevier Health Sciences; 2015.

Parmar P. Stroke: classification and diagnosis. Clinical Pharmacist 2018;10(1).

Vilela P, Rowley HA. Brain ischemia: CT and MRI techniques in acute ischemic stroke. European journal of radiology 2017;96:162-72.

Dourado Jr CM, da Silva SPP, da Nobrega RVM, et al. Deep learning IoT system for online stroke detection in skull computed tomography images. Computer Networks 2019;152:25-39.

Murray NM, Unberath M, Hager GD, Hui FK. Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review. Journal of neurointerventional surgery 2019.

Xu Y, Holanda G, Souza LFdF, et al. Deep learning-enhanced internet of medical things to analyze brain ct scans of hemorrhagic stroke patients: a new approach. IEEE Sensors Journal 2020;21(22):24941-51.

Herpich F, Rincon F. Management of acute ischemic stroke. Critical care medicine 2020;48(11):1654.

Musuka TD, Wilton SB, Traboulsi M, Hill MD. Diagnosis and management of acute ischemic stroke: speed is critical. Cmaj 2015;187(12):887-93.

Chen Q, Xia T, Zhang M, et al. Radiomics in stroke neuroimaging: techniques, applications, and challenges. Aging and disease 2021;12(1):143.

Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ digital medicine 2018;1(1):9.

Darras KE, Spouge R, Hatala R, et al. Integrated virtual and cadaveric dissection laboratories enhance first year medical students’ anatomy experience: a pilot study. BMC medical education 2019;19:1-6.

Wardlaw JM, Mair G, Von Kummer R, et al. Accuracy of automated computer-aided diagnosis for stroke imaging: a critical evaluation of current evidence. Stroke 2022;53(7):2393-403.

Broocks G, Meyer L. New Advances in Diagnostic Radiology for Ischemic Stroke. J Clin Med 2023;12(19).

Yeo M, Kok HK, Kutaiba N, et al. Artificial intelligence in clinical decision support and outcome prediction–applications in stroke. Journal of medical imaging and radiation oncology 2021;65(5):518-28.

Zeleňák K, Krajina A, Meyer L, et al. How to improve the management of acute ischemic stroke by modern technologies, artificial intelligence, and new treatment methods. Life 2021;11(6):488.

Krishnan G, Singh S, Pathania M, et al. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023;6:1227091.

Soun J, Chow D, Nagamine M, et al. Artificial intelligence and acute stroke imaging. American Journal of Neuroradiology 2021;42(1):2-11.

Yedavalli VS, Tong E, Martin D, Yeom KW, Forkert ND. Artificial intelligence in stroke imaging: Current and future perspectives. Clinical Imaging 2021;69:246-54.

Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 2019;7:e7702.

Bramlett HM, Dietrich WD. Long-Term Consequences of Traumatic Brain Injury: Current Status of Potential Mechanisms of Injury and Neurological Outcomes. J Neurotrauma 2015;32(23):1834-48.

Gilotra K, Swarna S, Mani R, Basem J, Dashti R. Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease. Front Hum Neurosci 2023;17:1254417.

Alotaibi YK, Federico F. The impact of health information technology on patient safety. Saudi Med J 2017;38(12):1173-80.

El-Bouzaidi YEI, Abdoun O. Advances in artificial intelligence for accurate and timely diagnosis of COVID-19: A comprehensive review of medical imaging analysis. Scientific African 2023;22:e01961.

Soun JE, Chow DS, Nagamine M, et al. Artificial Intelligence and Acute Stroke Imaging. AJNR Am J Neuroradiol 2021;42(1):2-11.

Santos MK, Ferreira Júnior JR, Wada DT, et al. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia brasileira 2019;52:387-96.

Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR medical education 2019;5(2):e16048.

DOI: https://doi.org/10.24815/jds.v8i2.37111

Article Metrics

Abstract view : 0 times
PDF - 0 times


  • There are currently no refbacks.


Indexed by:







All papers published in FKG  USK Press are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Social Media: