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.

慢性硬膜下血肿（CSDH）经常影响老年人口。CSDHs的术后复发率很高，范围从3％到20％。对定性和定量分析都进行了探讨，以探讨术后复发的机制。我们调查了CSDHs的病理生理，并分析了影响术后复发的相关因素。在这里，我们总结了文献中记载的各种定性方法，并介绍了我们先前发表的独特的计算机辅助定量方法，以评估术后复发情况。基于与术后高复发率相关的定性分析，如丰富的血管，新膜形成和硬膜下硬膜间隙，CSDH的影像学特征，使用提出的定量分析方法可以清楚地观察到平均血肿密度，脑再扩张率，血肿量，硬膜下间隙的平均距离和脑移位。最后，简要概述了当前医疗保健中的人工智能（AI）设备类型和应用。我们得出的结论是，可以将AI技术的潜在应用集成到所提出的定量分析方法中，以实现对CSDHs术后结果的快速执行和准确预测。