Jiyeon Park, Chae Young Lim, So Yeon Won, Han Kyu Na, Phil Hyu Lee, Sun-Young Baek, Yun Hwa Roh, Minjung Seong, Yongsik Sim, Eung Yeop Kim, Sung Tae Kim, Beomseok Sohn
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Participants were imaged using 3T MRI, and AI-generated assessments for N1 abnormalities were obtained using an AI model (version 1.0.1.0; Heuron Corporation, Seoul, Korea), which utilized YOLOX-based object detection and SparseInst segmentation models. Four radiologists (two experienced neuroradiologists and two less experienced residents) evaluated N1 abnormalities with and without AI in a crossover study design. Diagnostic performance metrics, inter-reader agreements, and reader responses to AI-generated assessments were evaluated.</p><p><strong>Results: </strong>Use of AI significantly improved diagnostic performance compared with interpretation without it across three readers, with significant increases in specificity (0.86 vs. 0.94, <i>P</i> = 0.004; 0.91 vs. 0.97, <i>P</i> = 0.024; and 0.90 vs. 0.97, <i>P</i> = 0.012). Inter-reader agreement also improved with AI, as Fleiss's kappa increased from 0.73 (95% confidence interval [CI]: 0.61-0.84) to 0.87 (95% CI: 0.76-0.99). The net reclassification index (NRI) demonstrated significant improvement in three of the four readers. When grouped by experience level, less experienced readers showed greater improvement (NRI = 12.8%, 95% CI: 0.067-0.190) than experienced readers (NRI = 0.8%, 95% CI: -0.037-0.051). In the less experienced group, reader-AI disagreement was significantly higher in the PD group than in the normal group (8.1% vs. 3.8%, <i>P</i> = 0.029).</p><p><strong>Conclusion: </strong>DL-based AI enhances the diagnostic performance in detecting N1 abnormalities on SMwI, particularly benefiting less experienced radiologists. 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引用次数: 0
摘要
目的：评价基于深度学习（DL）的人工智能（AI）软件对不同经验水平放射科医师在敏感性地图加权成像（SMwI）上检测黑素体1 （N1）异常诊断效果的影响。材料和方法：本回顾性诊断病例对照研究分析了59例帕金森病患者（PD）和80名健康参与者的139次SMwI扫描。使用3T MRI对参与者进行成像，并使用AI模型(版本1.0.1.0；启发式公司，首尔，韩国)，利用基于yolox的目标检测和SparseInst分割模型。四名放射科医生（两名经验丰富的神经放射科医生和两名经验不足的住院医生）在交叉研究设计中评估了有无人工智能的N1异常。对诊断性能指标、读者间协议和读者对人工智能生成的评估的反应进行了评估。结果：与不使用人工智能的解读相比，使用人工智能显著提高了三个解读器的诊断性能，特异性显著提高(0.86 vs. 0.94, P = 0.004；0.91 vs. 0.97, P = 0.024；0.90 vs 0.97, P = 0.012)。AI也改善了读者间的一致性，因为Fleiss kappa从0.73（95%置信区间[CI]: 0.61-0.84）增加到0.87 （95% CI: 0.76-0.99）。净重分类指数（NRI）显示，4名读者中有3名有显著改善。当按经验水平分组时，经验不足的读者比经验丰富的读者表现出更大的改善（NRI = 12.8%, 95% CI: 0.067-0.190） （NRI = 0.8%, 95% CI: -0.037-0.051）。在经验不足组中，PD组的读者- ai不一致显著高于正常组（8.1%比3.8%,P = 0.029）。结论：基于dl的人工智能提高了SMwI N1异常的诊断性能，特别是对经验不足的放射科医生有利。这些发现强调了改善PD诊断工作流程的潜力。
Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging.
Objective: To evaluate the effect of deep learning (DL)-based artificial intelligence (AI) software on the diagnostic performance of radiologists with different experience levels in detecting nigrosome 1 (N1) abnormalities on susceptibility map-weighted imaging (SMwI).
Materials and methods: This retrospective diagnostic case-control study analyzed 139 SMwI scans of 59 patients with Parkinson's disease (PD) and 80 healthy participants. Participants were imaged using 3T MRI, and AI-generated assessments for N1 abnormalities were obtained using an AI model (version 1.0.1.0; Heuron Corporation, Seoul, Korea), which utilized YOLOX-based object detection and SparseInst segmentation models. Four radiologists (two experienced neuroradiologists and two less experienced residents) evaluated N1 abnormalities with and without AI in a crossover study design. Diagnostic performance metrics, inter-reader agreements, and reader responses to AI-generated assessments were evaluated.
Results: Use of AI significantly improved diagnostic performance compared with interpretation without it across three readers, with significant increases in specificity (0.86 vs. 0.94, P = 0.004; 0.91 vs. 0.97, P = 0.024; and 0.90 vs. 0.97, P = 0.012). Inter-reader agreement also improved with AI, as Fleiss's kappa increased from 0.73 (95% confidence interval [CI]: 0.61-0.84) to 0.87 (95% CI: 0.76-0.99). The net reclassification index (NRI) demonstrated significant improvement in three of the four readers. When grouped by experience level, less experienced readers showed greater improvement (NRI = 12.8%, 95% CI: 0.067-0.190) than experienced readers (NRI = 0.8%, 95% CI: -0.037-0.051). In the less experienced group, reader-AI disagreement was significantly higher in the PD group than in the normal group (8.1% vs. 3.8%, P = 0.029).
Conclusion: DL-based AI enhances the diagnostic performance in detecting N1 abnormalities on SMwI, particularly benefiting less experienced radiologists. These findings underscore the potential for improving diagnostic workflows for PD.
期刊介绍：
The inaugural issue of the Korean J Radiol came out in March 2000. Our journal aims to produce and propagate knowledge on radiologic imaging and related sciences.
A unique feature of the articles published in the Journal will be their reflection of global trends in radiology combined with an East-Asian perspective. Geographic differences in disease prevalence will be reflected in the contents of papers, and this will serve to enrich our body of knowledge.
World''s outstanding radiologists from many countries are serving as editorial board of our journal.
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