Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study

Bao, Heling; Bi, Hui; Zhang, Xiaosong; Zhao, Yun; Dong, Yan; Luo, Xiao-Jun; Zhou, Deping; You, Zhixue; Wu, Yinglan; Liu, Zhaoyang; Zhang, Yuping; Liu, Juan; Fang, Liwen; Wang, Lınhong

doi:10.1016/j.ygyno.2020.07.099

Cited by 61 publications

(62 citation statements)

References 27 publications

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“…One paradigm is to scan a slide in advance and then perform whole slide image analysis; the other paradigm is to analyze the observer's FOV image from the microscope and present AI results through real‐time display technology. Most AI‐assisted diagnosis techniques have been used in the former paradigm 23‐27 . As a representative of the latter paradigm, a novel augmented microscope with AI capabilities was presented by Chen et al in 2019 28 ; it is expected to be used in a huge array of pathology applications, such as the evaluation of lymph node metastasis and mitosis counting.…”

Section: Discussionmentioning

confidence: 99%

Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study

Tang

Cai

Kong

et al. 2021

Cancer Cytopathology

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BACKGROUND Cervical cytology screening is usually laborious with a heavy workload and poor diagnostic consistency. The authors have developed an artificial intelligence (AI) microscope that can provide onsite diagnostic assistance for cervical cytology screening in real time. METHODS A total of 2167 cervical cytology slides were selected from a cohort of 10,601 cases from Shenzhen Maternity and Child Healthcare Hospital, and the training data set consisted of 42,073 abnormal cervical epithelial cells. The recognition results of an AI technique were presented in a microscope eyepiece by an augmented reality technique. Potentially abnormal cells were highlighted with binary classification results in a 10× field of view (FOV) and with multiclassification results according to the Bethesda system in 20× and 40× FOVs. In addition, 486 slides were selected for the reader study to evaluate the performance of the AI microscope. RESULTS In the reader study, which compared manual reading with AI assistance, the sensitivities for the detection of low‐grade squamous intraepithelial lesions and high‐grade squamous intraepithelial lesions were significantly improved from 0.837 to 0.923 (P < .001) and from 0.830 to 0.917 (P < .01), respectively; the κ score for atypical squamous cells of undetermined significance (ASCUS) was improved from 0.581 to 0.637; the averaged pairwise κ of consistency for multiclassification was improved from 0.649 to 0.706; the averaged pairwise κ of consistency for binary classification was improved from 0.720 to 0.798; and the averaged pairwise κ of ASCUS was improved from 0.557 to 0.639. CONCLUSIONS The results of this study show that an AI microscope can provide real‐time assistance for cervical cytology screening and improve the efficiency and accuracy of cervical cytology diagnosis.

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Section: Discussionmentioning

confidence: 99%

Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study

Tang

Cai

Kong

et al. 2021

Cancer Cytopathology

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“…AI studies have also classified the degree of CIN and carcinoma in situ. Bao et al 21 used deep learning to analyze 188 542 cervical cytology images to develop a diagnostic system that can distinguish carcinoma in situ from cervical dysplasia. The diagnostic accuracies of CIN 2 and CIN 3 were 92.6% and 96.1%, respectively.…”

Section: Application Of Ai In Cervical Cancer and Cervical Intraepithelial Neoplasiamentioning

confidence: 99%

“…The diagnostic accuracies of CIN 2 and CIN 3 were 92.6% and 96.1%, respectively. In HPV‐positive patients, this AI‐assisted diagnosis tool improved the specificity without decreasing sensitivity 21 . Shanti et al 22 established a deep learning model focusing on the morphology of single cells in the cervical cytology.…”

Section: Application Of Ai In Cervical Cancer and Cervical Intraepithelial Neoplasiamentioning

confidence: 99%

Application of artificial intelligence in gynecologic malignancies: A review

Sone

Toyohara

Taguchi

et al. 2021

J of Obstet and Gynaecol

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With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi‐omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.

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“…With the fast development of computing and Internet science, artificial intelligence (AI) has been engaged in the healthcare industry in recent years, especially in the diagnosis of cancers [ 8 – 12 ]. In the field of cervical cancer prevention, efforts have been made in the development of computing scoring systems and artificial intelligence [ 13 – 19 ]. Computing scoring systems involving artificial intelligence were proposed to improve the quality of management of women with abnormal screening results [ 13 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Computing scoring systems involving artificial intelligence were proposed to improve the quality of management of women with abnormal screening results [ 13 , 15 ]. Computational analysis was involved to improve the accuracy of cytology grading [ 16 – 19 ]. In the year 2020, Xue et al reported that a colposcopic artificial intelligence auxiliary diagnostic system (CAIADS) for grading colposcopic impressions and guiding biopsies was developed and successfully validated and concluded that CAIADS achieved high sensitivity and comparable specificity to colposcopies interpreted by colposcopists [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

The Performance of Artificial Intelligence in Cervical Colposcopy: A Retrospective Data Analysis

Zhao

Xing

et al. 2022

Journal of Oncology

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Objective. We aimed to evaluate the performance of artificial intelligence (AI) system in detecting high-grade precancerous lesions. Methods. A retrospective and diagnostic study was conducted in Chongqing Cancer Hospital. Anonymized medical records with cytology, HPV testing, colposcopy findings with images, and the histopathological results were selected. The sensitivity, specificity, and areas under the curve (AUC) in detecting CIN2+ and CIN3+ were evaluated for the AI system, the AI-assisted colposcopy, and the human colposcopists, respectively. Results. Anonymized medical records from 346 women were obtained. The images captured under colposcopy of 194 women were found positive by the AI system; 245 women were found positive either by human colposcopists or the AI system. In detecting CIN2+, the AI-assisted colposcopy significantly increased the sensitivity (96.6% vs. 88.8%, p = 0.016 ). The specificity was significantly lower for AI-assisted colposcopy (38.1%), compared with human colposcopists (59.5%, p < 0.001 ) or the AI system (57.6%, p < 0.001 ). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.741, 0.765, and 0.674, respectively. In detecting CIN3+, the sensitivities of the AI system and AI-assisted colposcopy were not significantly higher than human colposcopists (97.5% vs. 92.6%, p = 0.13 ). The specificity was significantly lower for AI-assisted colposcopy (37.4%) compared with human colposcopists (59.2%, p < 0.001 ) or compared with the AI system (56.6%, p < 0.001 ). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.759, 0.674, and 0.771, respectively. Conclusions. The AI system provided equally matched sensitivity to human colposcopists in detecting CIN2+ and CIN3+. The AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+.

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Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study

Cited by 61 publications

References 27 publications

Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study

Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study

Application of artificial intelligence in gynecologic malignancies: A review

The Performance of Artificial Intelligence in Cervical Colposcopy: A Retrospective Data Analysis

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