Guo Lin, Xia Li, Zheng Qiuting, Zheng Bin, Jaeger Stefan, Giger Maryellen L, Fuhrman Jordan, Li Hui, Lure Fleming Y M, Li Hongjun, Li Li
Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.
Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China.
J Xray Sci Technol. 2024;32(6):1465-1480. doi: 10.3233/XST-240051.
Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming.
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.
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.
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).
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.
从异质性胸部X光（CXR）图像中准确检测各种肺部异常并撰写放射学报告通常既困难又耗时。
通过多读者多病例（MRMC）观察者性能研究，评估一种新型人工智能（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超过36000张CXR图像，并将其用作实验组和对照组。在对照组中，采用双读方法，即两名放射科医生解读CXR以生成最终报告，而在实验组中，一名放射科医生基于人工智能生成的报告生成最终报告。
与双读相比，使用人工智能单读的诊断准确性和敏感性分别显著提高了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有可能作为第一读者生成初步诊断报告，放射科医生只需进行审核并进行小的修改（如有需要）即可做出最终决定。研究还表明，使用人工智能单读比双读能实现更高的诊断准确性和效率。
