Head-to-Head Comparison of Two AI Computer-Aided Triage Solutions for Detecting Intracranial Hemorrhage on Non-Contrast Head CT.

Glenn M Garcia, Peter Young, Lydia Dawood, Mohammed Elshikh
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Abstract

Background and purpose: This study aims to provide a comprehensive comparison of the performance and reproducibility of two commercially available artificial intelligence (AI) software computer-aided triage and notification solutions, Vendor A (Aidoc) and Vendor B (Viz.ai), for the detection of intracranial hemorrhage (ICH) on non-contrast enhanced head CT (NCHCT) scans performed within a single academic institution.

Materials and methods: The retrospective analysis was conducted on a large patient cohort from multiple healthcare settings within a single academic institution, utilizing standardized scanning protocols. Sensitivity, specificity, false positive, and false negative rates were evaluated for both vendors. Outputs assessed included AI-generated case-level classification.

Results: Among 4,081 scans, 595 were positive for ICH. Vendor A demonstrated a sensitivity of 94.4% and specificity of 97.4%, PPV of 85.9%, and NPV of 99.1%. Vendor B showed a sensitivity of 59.5% and specificity of 99.0%, PPV of 90.0%, and NPV of 92.6%. Vendor A had 20 false negatives, which primarily involved subdural and intraparenchymal hemorrhages, and 97 false positives, which appear to be related to motion artifact. Vendor B had 145 false negatives, largely comprised of subdural and subarachnoid hemorrhages, and 36 false positives, which appeared to be related to motion artifact and calcified or dense lesions. Concordantly, 18 cases were false negatives and 11 cases were false positives for both AI solutions.

Conclusions: The findings of this study provide valuable information for clinicians and healthcare institutions considering the implementation of AI software for computer aided-triage and notification in the detection of intracranial hemorrhage. The discussion encompasses the implications of the results, the importance of evaluating AI findings in context-especially in the absence of explainability tools, potential areas for improvement, and the relevance of standardized scanning protocols in ensuring the reliability of AI-based diagnostic tools in clinical practice.

Abbreviations: ICH = Intracranial Hemorrhage; NCHCT = Non-contrast Enhanced Head CT; AI = Artificial Intelligence; SDH = Subdural Hemorrhage; SAH = Subarachnoid Hemorrhage; IPH = Intraparenchymal Hemorrhage; IVH = Intraventricular Hemorrhage; PPV = Positive Predictive Value; NPV = Negative Predictive Value; CADt = Computer-Aided Triage; PACS = Picture Archiving and Communication System; FN = False Negative; FP = False Positive; CI = Confidence Interval.

两种AI计算机辅助分诊方案在非对比头部CT上检测颅内出血的头对头比较
背景和目的:本研究旨在全面比较两种商用人工智能(AI)软件计算机辅助分类和通知解决方案的性能和可重复性,供应商a (Aidoc)和供应商B (Viz.ai),用于在单个学术机构内进行的非对比增强头部CT (NCHCT)扫描中检测颅内出血(ICH)。材料和方法:采用标准化扫描协议,对来自单一学术机构的多个医疗保健机构的大型患者队列进行回顾性分析。对两家供应商的敏感性、特异性、假阳性和假阴性率进行了评估。评估的产出包括人工智能生成的病例级分类。结果:4081次扫描中,595次为脑出血阳性。供应商A的敏感性为94.4%,特异性为97.4%,PPV为85.9%,NPV为99.1%。卖方B的敏感性为59.5%,特异性为99.0%,PPV为90.0%,NPV为92.6%。供应商A有20个假阴性,主要涉及硬膜下和实质内出血,97个假阳性,似乎与运动伪影有关。供应商B有145例假阴性,主要包括硬膜下和蛛网膜下腔出血,36例假阳性,似乎与运动伪影和钙化或致密病变有关。两种人工智能解决方案均有18例假阴性,11例假阳性。结论:本研究结果为临床医生和医疗机构考虑在颅内出血检测中实施人工智能软件进行计算机辅助分诊和通知提供了有价值的信息。讨论内容包括研究结果的含义、评估人工智能发现的重要性,特别是在缺乏可解释性工具的情况下,潜在的改进领域,以及标准化扫描协议在确保临床实践中基于人工智能的诊断工具的可靠性方面的相关性。缩写:ICH =颅内出血;非对比增强头部CT;AI =人工智能;SDH =硬膜下出血;蛛网膜下腔出血;IPH =肝实质内出血;脑室内出血;PPV =阳性预测值;NPV =负预测值;计算机辅助分诊;图片存档和通信系统;FN =假阴性;FP =假阳性;CI =置信区间。
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