Evidence map›Paper›PMID 38937664›Full record

ArticleBMC cancer2024

Clinical evaluation of an artificial intelligence-assisted cytological system among screening strategies for a cervical cancer high-risk population.

Wen Yang, Xiangshu Jin, Liying Huang, Shufang Jiang, Jia Xu, Yurong Fu, Yaoyao Song, Xueyan Wang, Xueqing Wang, Zhiming Yang and 1 more

Abstract read
In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Wen Yang *Department of Obstetrics and Gynecology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Xiangshu Jin *Department of Obstetrics and Gynecology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Liying Huang *Tianjin Central Hospital of Gynecology Obstetrics, Tianjin, China.
Shufang JiangDepartment of Obstetrics and Gynecology, the First Medical Center of Chinese PLA General Hospital, Beijing, China.
Jia XuDepartment of Obstetrics and Gynecology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Yurong FuDepartment of Obstetrics and Gynecology, the First Medical Center of Chinese PLA General Hospital, Beijing, China.
Yaoyao SongiDeepWise Artificial Intelligence Robot Technology (Beijing) Co., LTD, 12 Shangdi Xinxin Road, Beijing, China.
Xueyan WangiDeepWise Artificial Intelligence Robot Technology (Beijing) Co., LTD, 12 Shangdi Xinxin Road, Beijing, China.
Xueqing WangiDeepWise Artificial Intelligence Robot Technology (Beijing) Co., LTD, 12 Shangdi Xinxin Road, Beijing, China.
Zhiming YangiDeepWise Artificial Intelligence Robot Technology (Beijing) Co., LTD, 12 Shangdi Xinxin Road, Beijing, China. yzm@ideepwise.ai.
Yuanguang MengDepartment of Obstetrics and Gynecology, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, China. meng6512@vip.sina.com.

Funding

the Chinese PLA military family planning project 19JSZ15
6 · The paper itself

Abstract

backgroundPrimary cervical cancer screening and treating precancerous lesions are effective ways to prevent cervical cancer. However, the coverage rates of human papillomavirus (HPV) vaccines and routine screening are low in most developing countries and even some developed countries. This study aimed to explore the benefit of an artificial intelligence-assisted cytology (AI) system in a screening program for a cervical cancer high-risk population in China.

methodsA total of 1231 liquid-based cytology (LBC) slides from women who underwent colposcopy at the Chinese PLA General Hospital from 2018 to 2020 were collected. All women had received a histological diagnosis based on the results of colposcopy and biopsy. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), false-positive rate (FPR), false-negative rate (FNR), overall accuracy (OA), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and Youden index (YI) of the AI, LBC, HPV, LBC + HPV, AI + LBC, AI + HPV and HPV Seq LBC screening strategies at low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) thresholds were calculated to assess their effectiveness. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic values of the different screening strategies.

resultsThe Se and Sp of the primary AI-alone strategy at the LSIL and HSIL thresholds were superior to those of the LBC + HPV cotesting strategy. Among the screening strategies, the YIs of the AI strategy at the LSIL + threshold and HSIL + threshold were the highest. At the HSIL + threshold, the AI strategy achieved the best result, with an AUC value of 0.621 (95% CI, 0.587-0.654), whereas HPV testing achieved the worst result, with an AUC value of 0.521 (95% CI, 0.484-0.559). Similarly, at the LSIL + threshold, the LBC-based strategy achieved the best result, with an AUC of 0.637 (95% CI, 0.606-0.668), whereas HPV testing achieved the worst result, with an AUC of 0.524 (95% CI, 0.491-0.557). Moreover, the AUCs of the AI and LBC strategies at this threshold were similar (0.631 and 0.637, respectively).

conclusionsThese results confirmed that AI-only screening was the most authoritative method for diagnosing HSILs and LSILs, improving the accuracy of colposcopy diagnosis, and was more beneficial for patients than traditional LBC + HPV cotesting.

Indexed as

Artificial IntelligenceEarly Detection of CancerPapillomavirus InfectionsUterine Cervical NeoplasmsAdultChinaColposcopyCytodiagnosisFemaleHumansMiddle AgedROC CurveSensitivity and SpecificityUterine Cervical DysplasiaYoung AdultArtificial intelligence-assisted cytology (AI)Cervical cancer screeningHigh-grade squamous intraepithelial lesion (HSIL)Human papillomavirus (HPV)Liquid-based cytology (LBC)Low-grade squamous intraepithelial lesion (LSIL)

Identifiers

PMID38937664
PMCPMC11212367

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.