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人工智能薄膜阅读系统联合液基细胞学检查用于宫颈癌筛查的有效性分析

Analysis of effectiveness in an artificial intelligent film reading system combined with liquid based cytology examination for cervical cancer screening.

作者信息

Liu Dawei, Chu Jingxue

机构信息

State-Owned Assets Management Office, The Fifth People's Hospital of Jinan Jinan 250000, Shandong, China.

Medical Experimental Diagnosis Center, Central Hospital Affiliated to Shandong First Medical University Jinan 250000, Shandong, China.

出版信息

Am J Transl Res. 2024 Sep 15;16(9):4979-4987. doi: 10.62347/EVXV1402. eCollection 2024.

Abstract

OBJECTIVE

To explore the effectiveness of combining an artificial intelligence (AI) film reading system with a cervical liquid-based ThinPrep cytology test (TCT) in cervical cancer screening.

METHODS

A total of 1200 adult women who underwent cervical cancer screening in the Gynecology Department of The Fifth People's Hospital of Jinan from July 2022 to June 2023 were included in the study. All participants underwent TCT followed by both manual and AI examination. The AI examination was performed using an AI film reading system that employed advanced machine learning algorithms and image processing techniques to analyze digital TCT slides. Pathological tissue biopsy was performed on all cases with abnormalities, and the results were used as the gold standard to analyze the effectiveness of the different screening methods.

RESULTS

TCT screening results revealed that the average time for manual film reading was shorter than that for the AI film reading system (P<0.001). The AI film reading system significantly detected more lesions than the manual film reading method (P<0.001). The overall compliance rate between AI imaging and manual imaging interpretation was 79.75%, with a corresponding Kappa value of 0.588, indicating moderate agreement between the two methods. The accuracy of the AI screening system for low-grade lesions and inflammation was 87.47%, compared to 79.41% for manual screening (P=0.018). For high-grade cancer lesions, the accuracy rates were 82.54% for AI and 75.90% for manual screening (P=0.241). The AI screening system had a sensitivity of 67.53% (104/154) for detecting high-grade lesions and cancers, higher than the 40.91% (63/154) sensitivity of manual screening. However, the specificity of the AI screening system was 94.07% (349/371), while manual screening had a specificity of 94.61% (351/371). The Youden index for AI screening system was 0.616, significantly higher than the 0.355 for manual screening.

CONCLUSION

In TCT screening, the AI screening system outperforms manual screening. The combination of the AI film reading system and TCT may hold significant value in cervical cancer screening, as well as in the early diagnosis and treatment of the disease.

摘要

目的

探讨将人工智能(AI)阅片系统与宫颈液基薄层细胞学检测(TCT)相结合用于宫颈癌筛查的有效性。

方法

纳入2022年7月至2023年6月在济南市第五人民医院妇科接受宫颈癌筛查的1200名成年女性。所有参与者均接受TCT检查,随后进行人工和AI检查。AI检查使用的AI阅片系统采用先进的机器学习算法和图像处理技术来分析数字TCT玻片。对所有异常病例进行病理组织活检,结果用作金标准以分析不同筛查方法的有效性。

结果

TCT筛查结果显示,人工阅片的平均时间短于AI阅片系统(P<0.001)。AI阅片系统检测出的病变明显多于人工阅片方法(P<0.001)。AI成像与人工成像判读之间的总体符合率为79.75%,相应的Kappa值为0.588,表明两种方法之间具有中度一致性。AI筛查系统对低级别病变和炎症的准确率为87.47%,而人工筛查的准确率为79.41%(P=0.018)。对于高级别癌症病变,AI的准确率为82.54%,人工筛查的准确率为75.90%(P=0.241)。AI筛查系统检测高级别病变和癌症的灵敏度为67.53%(104/154),高于人工筛查的40.91%(63/154)灵敏度。然而,AI筛查系统的特异度为94.07%(349/371),而人工筛查的特异度为94.61%(351/371)。AI筛查系统的约登指数为0.616,显著高于人工筛查的0.355。

结论

在TCT筛查中,AI筛查系统优于人工筛查。AI阅片系统与TCT相结合在宫颈癌筛查以及该疾病的早期诊断和治疗中可能具有重要价值。

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