Roshan Mona P, Al-Shaikhli Seema A, Linfante Italo, Antony Thompson T, Clarke Jamie E, Noman Raihan, Lamy Chrisnel, Britton Sean, Belnap Starlie C, Abrams Kevin, Sidani Charif
Radiology, Florida International University, Herbert Wertheim College of Medicine, Miami, USA.
Miami Neuroscience Institute, Baptist Health South Florida, Miami, USA.
Cureus. 2024 Aug 8;16(8):e66449. doi: 10.7759/cureus.66449. eCollection 2024 Aug.
Introduction Artificial intelligence (AI) alerts the radiologist to the presence of intracranial hemorrhage (ICH) as fast as 1-2 minutes from scan completion, leading to faster diagnosis and treatment. We wanted to validate a new AI application called Viz.ai ICH to improve the diagnosis of suspected ICH. Methods We performed a retrospective analysis of 4,203 consecutive non-contrast brain computed tomography (CT) reports in a single institution between September 1, 2021, and January 31, 2022. The reports were made by neuroradiologists who reviewed each case for the presence of ICH. Reports and identified cases with positive findings for ICH were reviewed. Positive cases were categorized based on subtype, timing, and size/volume. Viz.ai ICH output was reviewed for positive cases. This AI model was validated by assessing its performance with Viz.ai ICH as the index test compared to the neuroradiologists' interpretation as the gold standard. Results According to neuroradiologists, 9.2% of non-contrast brain CT reports were positive for ICH. The sensitivity of Viz.ai ICH was 85%, specificity was 98%, positive predictive value was 81%, and negative predictive value was 99%. Subgroup analysis was performed based on intraparenchymal, subarachnoid, subdural, and intraventricular subtypes. Sensitivities were 94%, 79%, 83%, and 44%, respectively. Further stratification revealed sensitivity improves with higher acuity and volume/size across subtypes. Conclusion Our analysis indicates that AI can accurately detect ICH's presence, particularly for large-volume/large-size ICH. The paper introduces a novel AI model for detecting ICH. This advancement contributes to the field by revolutionizing ICH detection and improving patient outcomes.
引言 人工智能（AI）能够在扫描完成后1至2分钟内就提醒放射科医生存在颅内出血（ICH）情况，从而实现更快的诊断和治疗。我们希望验证一种名为Viz.ai ICH的新型人工智能应用，以改善对疑似颅内出血的诊断。方法 我们对2021年9月1日至2022年1月31日期间一家机构连续的4203份非增强脑计算机断层扫描（CT）报告进行了回顾性分析。这些报告由神经放射科医生撰写，他们对每个病例进行颅内出血检查。对报告以及确定为颅内出血阳性结果的病例进行了复查。阳性病例根据亚型、时间和大小/体积进行分类。对颅内出血阳性病例的Viz.ai ICH输出结果进行了复查。将该人工智能模型以Viz.ai ICH作为指标测试、与神经放射科医生的解读作为金标准进行比较，评估其性能，从而对该模型进行验证。结果 根据神经放射科医生的判断，9.2%的非增强脑CT报告颅内出血呈阳性。Viz.ai ICH的敏感性为85%，特异性为98%，阳性预测值为81%，阴性预测值为99%。基于脑实质内、蛛网膜下腔、硬膜下和脑室内亚型进行了亚组分析。敏感性分别为94%、79%、83%和44%。进一步分层显示，各亚型中随着严重程度和体积/大小增加，敏感性提高。结论 我们的分析表明，人工智能能够准确检测颅内出血的存在，特别是对于大体积/大尺寸的颅内出血。本文介绍了一种用于检测颅内出血的新型人工智能模型。这一进展通过革新颅内出血检测并改善患者预后，为该领域做出了贡献。
