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脑出血研究中血肿和血肿周围水肿体积的定量:人工智能验证(QUANTUM)研究中的设计考虑因素。

Quantification of hematoma and perihematomal edema volumes in intracerebral hemorrhage study: Design considerations in an artificial intelligence validation (QUANTUM) study.

机构信息

Department of Neurological Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA.

Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.

出版信息

Clin Trials. 2022 Oct;19(5):534-544. doi: 10.1177/17407745221105886. Epub 2022 Jul 2.

Abstract

BACKGROUND

Hematoma and perihematomal edema volumes are important radiographic markers in spontaneous intracerebral hemorrhage. Accurate, reliable, and efficient quantification of these volumes will be paramount to their utility as measures of treatment effect in future clinical studies. Both manual and semi-automated quantification methods of hematoma and perihematomal edema volumetry are time-consuming and susceptible to inter-rater variability. Efforts are now underway to develop a fully automated algorithm that can replace them. A (QUANTUM) study to establish inter-quantification method measurement equivalency, which deviates from the traditional use of measures of agreement and a comparison hypothesis testing paradigm to indirectly infer quantification method measurement equivalence, is described in this article. The Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage study aims to determine whether a fully automated quantification method and a semi-automated quantification method for quantification of hematoma and perihematomal edema volumes are equivalent to the hematoma and perihematomal edema volumes of the manual quantification method.

METHODS/DESIGN: Hematoma and perihematomal edema volumes of supratentorial intracerebral hemorrhage on 252 computed tomography scans will be prospectively quantified in random order by six raters using the fully automated, semi-automated, and manual quantification methods. Primary outcome measures for hematoma and perihematomal edema volumes will be quantified via computed tomography scan on admission (<24 h from symptom onset) and on day 3 (72 ± 12 h from symptom onset), respectively. Equivalence hypothesis testing will be conducted to determine if the hematoma and perihematomal edema volume measurements of the fully automated and semi-automated quantification methods are within 7.5% of the hematoma and perihematomal edema volume measurements of the manual quantification reference method.

DISCUSSION

By allowing direct equivalence hypothesis testing, the Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage study offers advantages over radiology validation studies which utilize measures of agreement to indirectly infer measurement equivalence and studies which mistakenly try to infer measurement equivalence based on the failure of a comparison two-sided null hypothesis test to reach the significance level for rejection. The equivalence hypothesis testing paradigm applied to artificial intelligence application validation is relatively uncharted and warrants further investigation. The challenges encountered in the design of this study may influence future studies seeking to translate artificial intelligence medical technology into clinical practice.

摘要

背景

血肿和血肿周围水肿体积是自发性脑出血的重要影像学标志物。准确、可靠、高效地量化这些体积对于它们在未来临床研究中作为治疗效果的衡量标准至关重要。血肿和血肿周围水肿体积的手动和半自动定量方法既耗时又容易受到观察者间变异性的影响。目前正在努力开发一种可以替代它们的全自动算法。本文介绍了一项(QUANTUM)研究,该研究旨在建立定量方法之间的测量等效性,该研究偏离了传统使用一致性度量和比较假设检验范式来间接推断定量方法测量等效性的方法,以建立定量方法之间的测量等效性。本文描述了一项(QUANTUM)研究,该研究旨在建立定量方法之间的测量等效性,该研究偏离了传统使用一致性度量和比较假设检验范式来间接推断定量方法测量等效性的方法。QUANTUM 研究旨在确定全自动定量方法和半自动定量方法是否与血肿和血肿周围水肿体积的手动定量方法等效。

方法/设计:将前瞻性地对 252 例 CT 扫描的幕上脑出血的血肿和血肿周围水肿体积进行定量,由六名评估者随机使用全自动、半自动和手动定量方法进行定量。血肿和血肿周围水肿体积的主要测量指标将分别通过入院时(发病后<24 小时)和入院后第 3 天(发病后 72±12 小时)的 CT 扫描进行量化。将进行等效性假设检验,以确定全自动和半自动定量方法的血肿和血肿周围水肿体积测量值是否在手动定量参考方法的血肿和血肿周围水肿体积测量值的 7.5%范围内。

讨论

通过允许直接等效性假设检验,QUANTUM 研究在脑出血血肿和血肿周围水肿体积定量方面具有优于放射学验证研究的优势,放射学验证研究使用一致性度量来间接推断测量等效性,以及那些错误地试图根据比较双侧零假设检验未能达到拒绝水平来推断测量等效性的研究。应用于人工智能应用验证的等效性假设检验范式相对未知,值得进一步研究。本研究设计中遇到的挑战可能会影响未来将人工智能医疗技术转化为临床实践的研究。

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