Department of Radiology and Imaging Sciences, Emory University, 101 Woodruff Circle, Room 1203, Atlanta, GA, 30322, USA.
Division of Cardiology, Department of Medicine, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
J Nucl Cardiol. 2020 Oct;27(5):1652-1664. doi: 10.1007/s12350-018-1432-3. Epub 2018 Sep 12.
To describe and validate an artificial intelligence (AI)-driven structured reporting system by direct comparison of automatically generated reports to results from actual clinical reports generated by nuclear cardiology experts.
Quantitative parameters extracted from myocardial perfusion imaging (MPI) studies are used by our AI reporting system to generate automatically a guideline-compliant structured report (sR).
A new nonparametric approach generates distribution functions of rest and stress, perfusion, and thickening, for each of 17 left ventricle segments that are then transformed to certainty factors (CFs) that a segment is hypoperfused, ischemic. These CFs are then input to our set of heuristic rules used to reach diagnostic findings and impressions propagated into a sR referred as an AI-driven structured report (AIsR). The diagnostic accuracy of the AIsR for detecting coronary artery disease (CAD) and ischemia was tested in 1,000 patients who had undergone rest/stress SPECT MPI.
At the high-specificity (SP) level, in a subset of 100 patients, there were no statistical differences in the agreements between the AIsr, and nine experts' impressions of CAD (P = .33) or ischemia (P = .37). This high-SP level also yielded the highest accuracy across global and regional results in the 1,000 patients. These accuracies were statistically significantly better than the other two levels [sensitivity (SN)/SP tradeoff, high SN] across all comparisons.
This AI reporting system automatically generates a structured natural language report with a diagnostic performance comparable to those of experts.
通过将自动生成的报告与核医学专家实际生成的临床报告结果进行直接比较，描述和验证一种人工智能（AI）驱动的结构化报告系统。
我们的 AI 报告系统使用从心肌灌注成像（MPI）研究中提取的定量参数，自动生成符合指南的结构化报告（sR）。
一种新的非参数方法为 17 个左心室节段生成静息和应激、灌注和增厚的分布函数，然后将这些分布函数转换为某个节段灌注不足、缺血的确定性因素（CF）。这些 CF 然后输入到我们的启发式规则集，用于得出诊断发现并将印象传播到 sR 中，称为 AI 驱动的结构化报告（AIsR）。在 1000 名接受静息/应激 SPECT MPI 的患者中，测试了 AIsR 检测冠状动脉疾病（CAD）和缺血的诊断准确性。
在高特异性（SP）水平，在 100 名患者的亚组中，AIsR 与九位专家对 CAD（P =.33）或缺血（P =.37）的印象之间没有统计学差异。在 1000 名患者的整体和区域结果中，这种高 SP 水平也产生了最高的准确性。与其他两个水平[灵敏度（SN）/SP 权衡，高 SN]相比，这些准确性在所有比较中均具有统计学意义。
该 AI 报告系统可自动生成具有与专家相当的诊断性能的结构化自然语言报告。
