Machine learning applications for vascular stenosis detection in computed tomography angiography: a systematic review and meta-analysis

Anwer A. M. O. A., Karacan H., Enver L., Çabuk G.

NEURAL COMPUTING AND APPLICATIONS, cilt.36, ss.1-20, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 36
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s00521-024-10199-x
  • Dergi Adı: NEURAL COMPUTING AND APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.1-20
  • Gazi Üniversitesi Adresli: Evet

Özet

In an era in which cardiovascular disease has become the main cause of death all over the world, diagnostic accuracy in identifying blood vessels has become particularly important. Vascular stenosis causes serious health risks by affecting blood flow, leading to conditions like heart attacks and strokes. Traditional diagnostic methods face challenges in terms of timeliness and accuracy. Our systematic review aims to critically assess the role of machine learning (ML) techniques in enhancing computed tomography angiography’s (CTA) diagnostic capabilities for vasoconstriction. This review followed the predetermined inclusion and exclusion criteria and focused on research articles published between January 2013 and October 2023 collected from databases such as PubMed, IEEE, Web of Science, and Scopus. Studies focus on multiphase CTA or dynamic CTA; papers do not use the ML; and papers not in English are removed. The risk of bias of included studies was evaluated using the QUADAS2 tool. The results were analyzed in tabular form using metrics such as accuracy, sensitivity, and specificity and examine variations in stenosis detection by anatomical regions. In our review, a total of 63 studies were identified as relevant. These studies included a variety of ML applications for identifying anatomical stenosis of the arteries in different anatomical areas. The findings highlighted a trend of high sensitivity and specificity in broader anatomical assessments, with nuanced variations observed in detailed segmental analysis. The review acknowledges limitations within the existing studies, including the retrospective nature of most studies and their limited scope in terms of patient diversity and center variation. Nonetheless, the implications of integrating ML in vascular stenosis detection via CTA are profound, suggesting a pivotal shift toward more accurate, efficient, and patient-centric diagnostic practices in cardiovascular care.