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Leveraging Machine Learning Algorithms to Forecast Delayed Cerebral Ischemia Following Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis of 5,115 Participants Publisher Pubmed



Mohammadzadeh I1, 2 ; Niroomand B1 ; Eini P3 ; Khaledian H4 ; Choubineh T5 ; Luzzi S6, 7
Authors
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Authors Affiliations
  1. 1. Department of Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  2. 2. Neuroscience Lab, Department of Cell Biology and Anatomical Sciences, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  3. 3. Toxicological Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  4. 4. Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Computer (Computer Engineering), North Tehran Branch, Islamic Azad University, Tehran, Iran
  6. 6. Department of Medicine, Surgery, and Pharmacy University of Sassari, SD, Sassari, Italy
  7. 7. Department of Neurosurgery AOU Sassari, Azienda Ospedaliera Universitaria, Ospedale Civile SS. Annunziata, SD, Sassari, Italy

Source: Neurosurgical Review Published:2025


Abstract

It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systematic review and meta-analysis evaluate the efficacy of machine learning (ML) in predicting DCI, aiming to integrate complex clinical data to enhance diagnostic accuracy. We searched PubMed, Scopus, Web of science, and Embase databases without restrictions until June 2024, applying PRISMA guidelines. Out of 1498 studies screened, 10 met our eligibility criteria involving ML approaches in patients with confirmed aSAH. The studies employed various ML algorithms and reported differential ML metrics outcomes. Meta-analysis was performed on eight studies, which resulted in a pooled sensitivity of 0.79 [95% CI: 0.63–0.89], specificity of 0.78[95% CI: 0.68–0.85], positive DLR of 3.54 [95% CI: 2.22–5.64] and the negative DLR of 0.28 [95% CI: 0.15–0.52], diagnostic odds ratio of 12.82 [95% CI: 4.66–35.28], the diagnostic score of 2.55 [95% CI: 1.54–3.56], and the area under the curve (AUC) of 0.85. These findings show significant diagnostic accuracy and demonstrate the potential of ML algorithms to significantly improve the predictability of DCI, implying that ML could impart a significant role on improving clinical decision making. However, variability in methodological approaches across studies shows a need for standardization to realize the full benefits of ML in clinical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.