Dataset Information

The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women.

ABSTRACT: BACKGROUND:Adequate cytology is limited by insufficient cytologists in a large-scale cervical cancer screening. We aimed to develop an artificial intelligence (AI)-assisted cytology system in cervical cancer screening program. METHODS:We conducted a perspective cohort study within a population-based cervical cancer screening program for 0.7 million women, using a validated AI-assisted cytology system. For comparison, cytologists examined all slides classified by AI as abnormal and a randomly selected 10% of normal slides. Each woman with slides classified as abnormal by either AI-assisted or manual reading was diagnosed by colposcopy and biopsy. The outcomes were histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+). RESULTS:Finally, we recruited 703 103 women, of whom 98 549 were independently screened by AI and manual reading. The overall agreement rate between AI and manual reading was 94.7% (95% confidential interval [CI], 94.5%-94.8%), and kappa was 0.92 (0.91-0.92). The detection rates of CIN2+ increased with the severity of cytology abnormality performed by both AI and manual reading (Ptrend < 0.001). General estimated equations showed that detection of CIN2+ among women with ASC-H or HSIL by AI were significantly higher than corresponding groups classified by cytologists (for ASC-H: odds ratio [OR] = 1.22, 95%CI 1.11-1.34, P < .001; for HSIL: OR = 1.41, 1.28-1.55, P < .001). AI-assisted cytology was 5.8% (3.0%-8.6%) more sensitive for detection of CIN2+ than manual reading with a slight reduction in specificity. CONCLUSIONS:AI-assisted cytology system could exclude most of normal cytology, and improve sensitivity with clinically equivalent specificity for detection of CIN2+ compared with manual cytology reading. Overall, the results support AI-based cytology system for the primary cervical cancer screening in large-scale population.

SUBMITTER: Bao H

PROVIDER: S-EPMC7520355 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women.

Bao Heling H Sun Xiaorong X Zhang Yi Y Pang Baochuan B Li Hua H Zhou Liang L Wu Fengpin F Cao Dehua D Wang Jian J Turic Bojana B Wang Linhong L

Cancer medicine 20200722 18

<h4>Background</h4>Adequate cytology is limited by insufficient cytologists in a large-scale cervical cancer screening. We aimed to develop an artificial intelligence (AI)-assisted cytology system in cervical cancer screening program.<h4>Methods</h4>We conducted a perspective cohort study within a population-based cervical cancer screening program for 0.7 million women, using a validated AI-assisted cytology system. For comparison, cytologists examined all slides classified by AI as abnormal and ...[more]

PMID: 32697872

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Project description:The rising global incidence of cervical cancer is estimated to have affected more than 600,000 women, and nearly 350,000 women are predicted to have died from the disease in 2020 alone. Novel advances in cancer prevention, screening, diagnosis and treatment have all but reduced the burden of cervical cancer in developed nations. Unfortunately, cervical cancer is still the number one gynecological cancer globally. A limiting factor in managing cervical cancer globally is access to healthcare systems and trained medical personnel. Any methodology or procedure that may simplify or assist cervical cancer screening is desirable. Herein, we assess the use of artificial intelligence (AI)-assisted colposcopy in a tertiary hospital cervical diagnostic pathology unit. The study group consisted of 48 women (mean age 34) who were referred to the clinic for a routine colposcopy by their gynecologist. Cervical images were taken by an EVA-Visualcheck TM colposcope and run through an AI algorithm that gave real-time binary results of the cervical images as being either normal or abnormal. The primary endpoint of the study assessed the AI algorithm's ability to correctly identify histopathology results of CIN2+ as being abnormal. A secondary endpoint was a comparison between the AI algorithm and the clinical assessment results. Overall, we saw lower sensitivity of AI (66.7%; 12/18) compared with the clinical assessment (100%; 18/18), and histopathology results as the gold standard. The positive predictive value (PPV) was comparable between AI (42.9%; 12/28) and the clinical assessment (41.8%; 18/43). The specificity, however, was higher in the AI algorithm (46.7%; 14/30) compared to the clinical assessment (16.7%; 5/30). Comparing the congruence between the AI algorithm and histopathology results showed agreement 54.2% of the time and disagreement 45.8% of the time. A trained colposcopist was in agreement 47.9% and disagreement 52.1% of the time. Assessing these results, there is currently no added benefit of using the AI algorithm as a tool of speeding up diagnosis. However, given the steady improvements in the AI field, we believe that AI-assisted colposcopy may be of use in the future.

Project description:ImportanceCervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning system (DLS) may provide needed cervical cancer screening to resource-limited areas.ObjectiveTo determine whether artificial intelligence-supported digital microscopy diagnostics can be implemented in a resource-limited setting and used for analysis of Papanicolaou tests.Design, setting, and participantsIn this diagnostic study, cervical smears from 740 HIV-positive women aged between 18 and 64 years were collected between September 1, 2018, and September 30, 2019. The smears were digitized with a portable slide scanner, uploaded to a cloud server using mobile networks, and used to train and validate a DLS for the detection of atypical cervical cells. This single-center study was conducted at a local health care center in rural Kenya.ExposuresDetection of squamous cell atypia in the digital samples by analysis with the DLS.Main outcomes and measuresThe accuracy of the DLS in the detection of low- and high-grade squamous intraepithelial lesions in Papanicolaou test whole-slide images.ResultsPapanicolaou test results from 740 HIV-positive women (mean [SD] age, 41.8 [10.3] years) were collected. The DLS was trained using 350 whole-slide images and validated on 361 whole-slide images (average size, 100 387 × 47 560 pixels). For detection of cervical cellular atypia, sensitivities were 95.7% (95% CI, 85.5%-99.5%) and 100% (95% CI, 82.4%-100%), and specificities were 84.7% (95% CI, 80.2%-88.5%) and 78.4% (95% CI, 73.6%-82.4%), compared with the pathologist assessment of digital and physical slides, respectively. Areas under the receiver operating characteristic curve were 0.94 and 0.96, respectively. Negative predictive values were high (99%-100%), and accuracy was high, particularly for the detection of high-grade lesions. Interrater agreement was substantial compared with the pathologist assessment of digital slides (κ = 0.72) and fair compared with the assessment of glass slides (κ = 0.36). No samples that were classified as high grade by manual sample analysis had false-negative assessments by the DLS.Conclusions and relevanceIn this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis.

Project description:Introduction A limitation to expanding laparoscopic simulation training programs is the scarcity of expert evaluators. In 2019, a new digital platform for remote and asynchronous laparoscopic simulation training was validated. Through this platform, 369 trainees have been trained in 14 institutions across Latin America, collecting 6729 videos of laparoscopic training exercises. The use of artificial intelligence (AI) has recently emerged in surgical simulation, showing usefulness in training assessment, virtual reality scenarios, and laparoscopic virtual reality simulation. An AI algorithm to assess basic laparoscopic simulation training exercises was developed. This study aimed to analyze the agreement between this AI algorithm and expert evaluators in assessing basic laparoscopic-simulated training exercises. Methods The AI algorithm was trained using 400-bean drop (BD) and 480-peg transfer (PT) videos and tested using 64-BD and 43-PT randomly selected videos, not previously used to train the algorithm. The agreement between AI and expert evaluators from the digital platform (EE) was then analyzed. The exercises being assessed involve using laparoscopic graspers to move objects across an acrylic board without dropping any objects in a determined time (BD < 24 s, PT < 55 s). The AI algorithm can detect object movement, identify if objects have fallen, track grasper clamps location, and measure exercise time. Cohen’s Kappa test was used to evaluate the agreement between AI assessments and those performed by EE, using a pass/fail nomenclature based on the time to complete the exercise. Results After the algorithm was trained, 79.69% and 93.02% agreement were observed in BD and PT, respectively. The Kappa coefficients test observed for BD and PT were 0.59 (moderate agreement) and 0.86 (almost perfect agreement), respectively. Conclusion This first approach of AI use in basic laparoscopic skills simulated training assessment shows promising results, providing a preliminary framework to expand the use of AI to other basic laparoscopic skills exercises. Supplementary Information The online version contains supplementary material available at 10.1007/s00464-022-09576-1.

Dataset Information

The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women.

Publications

The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women.

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