Reliability and diagnostic performance of smartphone colposcopy

Digital cervicography for cervical cancer screening in low-resource settings: A scoping review

Introduction

Digital cervicography (DC) is a method of capturing images for analysis during visual inspection with acetic acid (VIA) for cervical cancer screening. Cervical cancer is the 3rd leading cause of female cancer in the world with approximately 90 % of deaths due to cervical cancer occurring in low and middle income countries (LMICs). The need for cost-effective and sustainable methods for screening is vital in these settings. This scoping review systematically synthesizes published data illustrating the use of DC in screening programs. We aim to understand how digital cervicography is used, implemented, and impacted on programs.

Methods

Search of eight online databases identified 53 studies published between 1993 and 2021. Inclusion of articles were English language, cervical cancer screening program located in an LMIC, and DC as an intervention.

Results

All studies were cross-sectional studies (n = 53), with variation in terminology, uses, and device methods. Devices were grouped as either smartphones (n = 14), commercially available digital cameras (n = 17), or other (EVA®, n = 4; Cerviscope, n = 12; custom device, n = 4; or not specified, n = 2). Nineteen studies found acceptability and feasibility for DC in their screening programs. Various programs using DC found benefits such as task sharing, healthcare worker training, patient education and using images for review from a remote specialist or mentor.

Conclusion

The use of DC in LMICs is beneficial for support of healthcare workers, enhances quality improvement and demonstrates overall acceptability in screening programs. Advancing technologies for human papillomavirus (HPV) testing and cytology are common methods for cervical cancer screening, although are limited in LMICs. This scoping review demonstrates the different methods, uses, and benefit of digital cervicography in cervical cancer screening programs.

Comparison of accuracy and reproducibility of colposcopic impression based on a single image versus a two-minute time series of colposcopic images

OBJECTIVE

Colposcopy is an important part of cervical screening/management programs. Colposcopic appearance is often classified, for teaching and telemedicine, based on static images that do not reveal the dynamics of acetowhitening. We compared the accuracy and reproducibility of colposcopic impression based on a single image at one minute after application of acetic acid versus a time-series of 17 sequential images over two minutes.

METHODS

Approximately 5000 colposcopic examinations conducted with the DYSIS colposcopic system were divided into 10 random sets, each assigned to a separate expert colposcopist. Colposcopists first classified single two-dimensional images at one minute and then a time-series of 17 sequential images as 'normal,' 'indeterminate,' 'high grade,' or 'cancer'. Ratings were compared to histologic diagnoses. Additionally, 5 colposcopists reviewed a subset of 200 single images and 200 time series to estimate intra- and inter-rater reliability.

RESULTS

Of 4640 patients with adequate images, only 24.4% were correctly categorized by single image visual assessment (11% of 64 cancers; 31% of 605 CIN3; 22.4% of 558 CIN2; 23.9% of 3412 < CIN2). Individual colposcopist accuracy was low; Youden indices (sensitivity plus specificity minus one) ranged from 0.07 to 0.24. Use of the time-series increased the proportion of images classified as normal, regardless of histology. Intra-rater reliability was substantial (weighted kappa = 0.64); inter-rater reliability was fair ( weighted kappa = 0.26).

CONCLUSION

Substantial variation exists in visual assessment of colposcopic images, even when a 17-image time series showing the two-minute process of acetowhitening is presented. We are currently evaluating whether deep-learning image evaluation can assist classification.

Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis

Objective

Human papillomavirus subtypes are predictive indicators of cervical intraepithelial neoplasia (CIN) progression. While colposcopy is also an essential part of cervical cancer prevention, its accuracy and reproducibility are limited because of subjective evaluation. This study aimed to develop an artificial intelligence (AI) algorithm that can accurately detect the optimal lesion associated with prognosis using colposcopic images of CIN2 patients by utilizing objective AI diagnosis.

Methods

We identified colposcopic findings associated with the prognosis of patients with CIN2. We developed a convolutional neural network that can automatically detect the rate of high-grade lesions in the uterovaginal area in 12 segments. We finally evaluated the detection accuracy of our AI algorithm compared with the scores by multiple gynecologic oncologists.

Results

High-grade lesion occupancy in the uterovaginal area detected by senior colposcopists was significantly correlated with the prognosis of patients with CIN2. The detection rate for high-grade lesions in 12 segments of the uterovaginal area by the AI system was 62.1% for recall, and the overall correct response rate was 89.7%. Moreover, the percentage of high-grade lesions detected by the AI system was significantly correlated with the rate detected by multiple gynecologic senior oncologists (r=0.61).

Conclusion

Our novel AI algorithm can accurately determine high-grade lesions associated with prognosis on colposcopic images, and these results provide an insight into the additional utility of colposcopy for the management of patients with CIN2.

High-grade lesion occupancy in the uterovaginal area was significantly correlated with CIN2 patients’ prognosis. The number of high-grade lesions in 12 segments detected by an artificial intelligence (AI)-based system was comparable to that detected by senior colposcopists. The overall correct response rate of the AI algorithm for detecting high-grade lesions was 89.7%.