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CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
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WHO Launches Global Push to Eliminate Cervical Cancer.世界卫生组织发起全球消除宫颈癌行动。
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A demonstration of automated visual evaluation of cervical images taken with a smartphone camera.使用智能手机摄像头拍摄的宫颈图像的自动化视觉评估演示。
Int J Cancer. 2020 Nov 1;147(9):2416-2423. doi: 10.1002/ijc.33029. Epub 2020 May 19.
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Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.在基层医疗诊所中用于检测糖尿病视网膜病变的基于人工智能的自主诊断系统的关键试验。
NPJ Digit Med. 2018 Aug 28;1:39. doi: 10.1038/s41746-018-0040-6. eCollection 2018.
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Prospective cohort study examining cervical cancer screening methods in HIV-positive and HIV-negative Cambodian Women: a comparison of human papilloma virus testing, visualization with acetic acid and digital colposcopy.前瞻性队列研究检查 HIV 阳性和 HIV 阴性柬埔寨妇女的宫颈癌筛查方法:人乳头瘤病毒检测、醋酸可视化和数字阴道镜检查的比较。
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Cervical cancer.宫颈癌。
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An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.深度学习在宫颈癌筛查中对宫颈图像进行自动评估的观察性研究。
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增强视觉评估[目视检查]对宫颈癌筛查阳性女性进行分诊的诊断效能

Diagnostic Efficacy of Enhanced Visual Assessment [Visual Check] for Triaging Cervical Cancer Screen Positive Women.

作者信息

Shamsunder Saritha, Mishra Archana, Kumar Anita, Beriwal Rajni, Ahluwalia Charanjeet, Das Sujata

机构信息

Department of Obstetrics and Gynaecology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.

Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.

出版信息

J Midlife Health. 2024 Apr-Jun;15(2):69-74. doi: 10.4103/jmh.jmh_204_23. Epub 2024 Jul 5.

DOI:10.4103/jmh.jmh_204_23
PMID:39145263
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11321519/
Abstract

INTRODUCTION

Colposcopy is important for triaging any abnormal cervical screening test. Scarcity of trained colposcopists and colposcopy centers is a big hurdle to screening programs in low- and middle-income countries.

OBJECTIVES OF THE STUDY

The objective was to assess the performance of the artificial intelligence incorporated into the mobile optical device technologies (ODT) Enhanced Visual Assessment (EVA visual check) against physician colposcopic diagnosis and the gold standard of histopathology.

MATERIALS AND METHODS

It was a cross-sectional observational study conducted on women referred to a colposcopy clinic following an abnormal screening test. Colposcopic examination was performed by colposcopists using the MobileODT EVA system. Physician's impression and Visual Check analysis were compared with the final histopathological analysis or cytology. Cases with normal cytology and normal colposcopy did not undergo biopsy, and these were considered normal.

RESULTS

A total of 2050 women were screened, and 147 screen-positive women were recruited in the study. EVA Visual Check had a sensitivity of 86.8% (75-95), specificity of 28.7% (20-39), positive predictive value (PPV) of 40.7% (32-50), negative predictive value (NPV) of 79.4% (62-91), and diagnostic accuracy of 49.7% (41-58) for diagnosing cervical intraepithelial neoplasia (CIN) 1+ lesions. EVA Visual Check has a sensitivity of 89.3% (72-98), specificity of 26.1% (18-35), PPV of 22.1% (15-31), NPV of 91.2% (76-98), and diagnostic accuracy of 38.1% (30-46) for CIN 2+ lesions.

CONCLUSION

MobileODT EVA colposcope with AI has sensitivity comparable to physician's diagnosis, whereas specificity, PPV, and NPV were less than that of physician's diagnosis. It could prove valuable for triage of screen-positive women for further management.

摘要

引言

阴道镜检查对于对任何异常宫颈筛查测试进行分类很重要。训练有素的阴道镜检查医师和阴道镜检查中心的稀缺是低收入和中等收入国家筛查计划的一大障碍。

研究目的

目的是评估整合到移动光学设备技术(ODT)增强视觉评估(EVA视觉检查)中的人工智能相对于医师阴道镜诊断和组织病理学金标准的性能。

材料和方法

这是一项横断面观察性研究,研究对象为筛查测试异常后转诊至阴道镜诊所的女性。阴道镜检查由阴道镜检查医师使用MobileODT EVA系统进行。将医师的诊断印象和视觉检查分析与最终的组织病理学分析或细胞学检查进行比较。细胞学和阴道镜检查均正常的病例未进行活检,这些病例被视为正常。

结果

共筛查了2050名女性,其中147名筛查呈阳性的女性被纳入研究。EVA视觉检查对诊断宫颈上皮内瘤变(CIN)1+病变的敏感性为86.8%(75-95),特异性为28.7%(20-39),阳性预测值(PPV)为40.7%(32-50),阴性预测值(NPV)为79.4%(62-91),诊断准确性为49.7%(41-58)。EVA视觉检查对CIN 2+病变的敏感性为89.3%(72-98),特异性为26.1%(18-35),PPV为22.1%(15-31),NPV为91.2%(76-98),诊断准确性为38.1%(30-46)。

结论

带有人工智能的MobileODT EVA阴道镜的敏感性与医师诊断相当,而特异性、PPV和NPV低于医师诊断。它对于对筛查呈阳性的女性进行进一步管理的分类可能具有重要价值。