CT-Based Quantitative Analysis for Pathological Features Associated With Postoperative Recurrence and Potential Application Upon Artificial Intelligence: A Narrative Review With a Focus on Chronic Subdural Hematomas. (24th March 2020)

• Record Type: Journal Article
• Authors: Kung, Woon-Man; Lin, Muh-Shi
• Is Part Of: Molecular imaging. Volume 19(2020)
• Publication Date: 2020-03-24
• Subjects: postoperative recurrence -- computer-assisted quantitative method -- artificial intelligence -- chronic subdural hematomas; Molecular diagnosis -- Periodicals; Diagnostic imaging -- Periodicals; Molecular biology -- Periodicals; Molecular diagnosis; Diagnostic imaging; Molecular biology; Periodicals; 616.075
• DOI: 10.1177/1536012120914773
• Languages: English
• ISSNs: 1535-3508

Abstract

Chronic subdural hematomas (CSDHs) frequently affect the elderly population. The postoperative recurrence rate of CSDHs is high, ranging from 3% to 20%. Both qualitative and quantitative analyses have been explored to investigate the mechanisms underlying postoperative recurrence. We surveyed the pathophysiology of CSDHs and analyzed the relative factors influencing postoperative recurrence. Here, we summarize various qualitative methods documented in the literature and present our unique computer-assisted quantitative method, published previously, to assess postoperative recurrence. Imaging features of CSDHs, based on qualitative analysis related to postoperative high recurrence rate, such as abundant vascularity, neomembrane formation, and patent subdural space, could be clearly observed using the proposed quantitative analysis methods in terms of mean hematoma density, brain re-expansion rate, hematoma volume, average distance of subdural space, and brain shifting. Finally, artificial intelligence (AI) device types and applications in current health care are briefly outlined. We conclude that the potential applications of AI techniques can be integrated to the proposed quantitative analysis method to accomplish speedy execution and accurate prediction for postoperative outcomes in the management of CSDHs.