人工智能能否生成诊断报告,供放射医师审批 CXR 图像?多阅读器和多病例观察者性能研究。

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Lin Guo, Li Xia, Qiuting Zheng, Bin Zheng, Stefan Jaeger, Maryellen L Giger, Jordan Fuhrman, Hui Li, Fleming Y M Lure, Hongjun Li, Li Li
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引用次数: 0

摘要

背景:从异质胸部X光(CXR)图像中准确检测出各种肺部异常并撰写放射学报告通常既困难又耗时:目的:通过一项多阅读器和多病例(MRMC)观察者绩效研究,了解新型人工智能(AI)系统(MOM-ClaSeg)在提高放射科医生检测异质性肺部异常的准确性和效率方面的效用:在 4 个月内从 12 家医院回顾性收集了 36,000 多张 CXR 图像,分别作为实验组和对照组。对照组采用双读法,由两名放射科医生对 CXR 进行解读,生成最终报告;实验组由一名放射科医生根据人工智能生成的报告生成最终报告:与双人阅片相比,使用人工智能进行单人阅片的诊断准确率和灵敏度分别显著提高了 1.49% 和 10.95%(P < 0.001),而特异性差异较小(0.22%),且无统计学意义(P = 0.255)。此外,实验组的平均图像阅读和诊断时间减少了 54.70%(P < 0.001):这项 MRMC 研究表明,MOM-ClaSeg 有可能作为第一阅片人生成初步诊断报告,放射科医生只需审阅并稍作修改(如有必要),即可做出最终决定。研究还表明,与双人阅片相比,人工智能单人阅片可实现更高的诊断准确性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can AI generate diagnostic reports for radiologist approval on CXR images? A multi-reader and multi-case observer performance study.

Background: Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming.

Objective: To access the utility of a novel artificial intelligence (AI) system (MOM-ClaSeg) in enhancing the accuracy and efficiency of radiologists in detecting heterogenous lung abnormalities through a multi-reader and multi-case (MRMC) observer performance study.

Methods: Over 36,000 CXR images were retrospectively collected from 12 hospitals over 4 months and used as the experiment group and the control group. In the control group, a double reading method is used in which two radiologists interpret CXR to generate a final report, while in the experiment group, one radiologist generates the final reports based on AI-generated reports.

Results: Compared with double reading, the diagnostic accuracy and sensitivity of single reading with AI increases significantly by 1.49% and 10.95%, respectively (P <  0.001), while the difference in specificity is small (0.22%) and without statistical significance (P = 0.255). Additionally, the average image reading and diagnostic time in the experimental group is reduced by 54.70% (P <  0.001).

Conclusion: This MRMC study demonstrates that MOM-ClaSeg can potentially serve as the first reader to generate the initial diagnostic reports, with a radiologist only reviewing and making minor modifications (if needed) to arrive at the final decision. It also shows that single reading with AI can achieve a higher diagnostic accuracy and efficiency than double reading.

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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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