Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings

Artificial intelligence for cervical cancer screening: Scoping review, 2009-2022

BACKGROUND

The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images.

OBJECTIVES

To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR).

SEARCH STRATEGY

Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords.

SELECTION CRITERIA

Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases.

DATA COLLECTION AND ANALYSIS

A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance.

MAIN RESULTS

We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%.

CONCLUSION

We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.

Optical imaging for screening and early cancer diagnosis in low-resource settings

Low-cost optical imaging technologies have the potential to reduce inequalities in healthcare by improving the detection of pre-cancer or early cancer and enabling more effective and less invasive treatment. In this Review, we summarise technologies for in vivo widefield, multi-spectral, endoscopic, and high-resolution optical imaging that could offer affordable approaches to improve cancer screening and early detection at the point-of-care. Additionally, we discuss approaches to slide-free microscopy, including confocal imaging, lightsheet microscopy, and phase modulation techniques that can reduce the infrastructure and expertise needed for definitive cancer diagnosis. We also evaluate how machine learning-based algorithms can improve the accuracy and accessibility of optical imaging systems and provide real-time image analysis. To achieve the potential of optical technologies, developers must ensure that devices are easy to use; the optical technologies must be evaluated in multi-institutional, prospective clinical tests in the intended setting; and the barriers to commercial scale-up in under-resourced markets must be overcome. Therefore, test developers should view the production of simple and effective diagnostic tools that are accessible and affordable for all countries and settings as a central goal of their profession.

Artificial intelligence and visual inspection in cervical cancer screening

INTRODUCTION

Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.

METHODS

A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.

RESULTS

Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.

CONCLUSION

This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.

Implementing Smartphone-Based Telemedicine for Cervical Cancer Screening in Uganda: Qualitative Study of Stakeholders' Perceptions

BACKGROUND

In Uganda, cervical cancer (CaCx) is the commonest cancer, accounting for 35.7% of all cancer cases in women. The rates of human papillomavirus vaccination and CaCx screening remain low. Digital health tools and interventions have the potential to improve different aspects of CaCx screening and control in Uganda.

OBJECTIVE

This study aimed to describe stakeholders' perceptions of the telemedicine system we developed to improve CaCx screening in Uganda.

METHODS

We developed and implemented a smartphone-based telemedicine system for capturing and sharing cervical images and other clinical data, as well as an artificial intelligence model for automatic analysis of images. We conducted focus group discussions with health workers at the screening clinics (n=27) and women undergoing screening (n=15) to explore their perceptions of the system. The focus group discussions were supplemented with field observations and an evaluation survey of the health workers on system usability and the overall project.

RESULTS

In general, both patients and health workers had positive opinions about the system. Highlighted benefits included better cervical visualization, the ability to obtain a second opinion, improved communication between nurses and patients (to explain screening findings), improved clinical data management, performance monitoring and feedback, and modernization of screening service. However, there were also some negative perceptions. For example, some health workers felt the system is time-consuming, especially when it had just been introduced, while some patients were apprehensive about cervical image capture and sharing. Finally, commonplace challenges in digital health (eg, lack of interoperability and problems with sustainability) and challenges in cancer screening in general (eg, arduous referrals, inadequate monitoring and quality control) also resurfaced.

CONCLUSIONS

This study demonstrates the feasibility and value of digital health tools in CaCx screening in Uganda, particularly with regard to improving patient experience and the quality of screening services. It also provides examples of potential limitations that must be addressed for successful implementation.

Automated Assessment of Digital Images of Uterine Cervix Captured Using Transvaginal Device-A Pilot Study

In low-resource settings, a point-of-care test for cervical cancer screening that can give an immediate result to guide management is urgently needed. A transvaginal digital device, "Smart Scope®" (SS), with an artificial intelligence-enabled auto-image-assessment (SS-AI) feature, was developed. In a single-arm observational study, eligible consenting women underwent a Smart Scope®-aided VIA-VILI test. Images of the cervix were captured using SS and categorized by SS-AI in four groups (green, amber, high-risk amber (HRA), red) based on risk assessment. Green and amber were classified as SS-AI negative while HRA and red were classified as SS-AI positive. The SS-AI-positive women were advised colposcopy and guided biopsy. The cervix images of SS-AI-negative cases were evaluated by an expert colposcopist (SS-M); those suspected of being positive were also recommended colposcopy and guided biopsy. Histopathology was considered a gold standard. Data on 877 SS-AI, 485 colposcopy, and 213 histopathology were available for analysis. The SS-AI showed high sensitivity (90.3%), specificity (75.3%), accuracy (84.04%), and correlation coefficient (0.670, p = 0.0) in comparison with histology at the CINI+ cutoff. In conclusion, the AI-enabled Smart Scope® test is a good alternative to the existing screening tests as it gives a real-time accurate assessment of cervical health and an opportunity for immediate triaging with visual evidence.

Cervical pre-cancerous lesion detection: development of smartphone-based VIA application using artificial intelligence

OBJECTIVE

Visual inspection of cervix after acetic acid application (VIA) has been considered an alternative to Pap smear in resource-limited settings, like Indonesia. However, VIA results mainly depend on examiner's experience and with the lack of comprehensive training of healthcare workers, VIA accuracy keeps declining. We aimed to develop an artificial intelligence (AI)-based Android application that can automatically determine VIA results in real time and may be further developed as a health care support system in cervical cancer screening.

RESULT

A total of 199 women who underwent VIA test was studied. Images of cervix before and after VIA test were taken with smartphone, then evaluated and labelled by experienced oncologist as VIA positive or negative. Our AI model training pipeline consists of 3 steps: image pre-processing, feature extraction, and classifier development. Out of the 199 data, 134 were used as train-validation data and the remaining 65 data were used as test data. The trained AI model generated a sensitivity of 80%, specificity of 96.4%, accuracy of 93.8%, precision of 80%, and ROC/AUC of 0.85 (95% CI 0.66-1.0). The developed AI-based Android application may potentially aid cervical cancer screening, especially in low resource settings.

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

OBJECTIVE

The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions.

MATERIALS AND METHODS

Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

RESULTS

The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively.

CONCLUSION

The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

Multicontrast Pocket Colposcopy Cervical Cancer Diagnostic Algorithm for Referral Populations

Objective and Impact Statement. We use deep learning models to classify cervix images-collected with a low-cost, portable Pocket colposcope-with biopsy-confirmed high-grade precancer and cancer. We boost classification performance on a screened-positive population by using a class-balanced loss and incorporating green-light colposcopy image pairs, which come at no additional cost to the provider. Introduction. Because the majority of the 300,000 annual deaths due to cervical cancer occur in countries with low- or middle-Human Development Indices, an automated classification algorithm could overcome limitations caused by the low prevalence of trained professionals and diagnostic variability in provider visual interpretations. Methods. Our dataset consists of cervical images (n = 1,760 ) from 880 patient visits. After optimizing the network architecture and incorporating a weighted loss function, we explore two methods of incorporating green light image pairs into the network to boost the classification performance and sensitivity of our model on a test set. Results. We achieve an area under the receiver-operator characteristic curve, sensitivity, and specificity of 0.87, 75%, and 88%, respectively. The addition of the class-balanced loss and green light cervical contrast to a Resnet-18 backbone results in a 2.5 times improvement in sensitivity. Conclusion. Our methodology, which has already been tested on a prescreened population, can boost classification performance and, in the future, be coupled with Pap smear or HPV triaging, thereby broadening access to early detection of precursor lesions before they advance to cancer.

Investigating feasibility of 2021 WHO protocol for cervical cancer screening in underscreened populations: PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC)

Background

High-risk human papillomavirus (hrHPV) testing has been recommended by the World Health Organization as the primary screening test in cervical screening programs. The option of self-sampling for this screening method can potentially increase women’s participation. Designing screening programs to implement this method among underscreened populations will require contextualized evidence.

Methods

PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC) will use a multi-method approach to investigate the feasibility of implementing a cervical cancer screening strategy with hrHPV self-testing as the primary screening test in Bangladesh, India, Slovak Republic and Uganda. The primary outcomes of study include uptake and coverage of the screening program and adherence to follow-up. These outcomes will be evaluated through a pre-post quasi-experimental study design. Secondary objectives of the study include the analysis of client-related factors and health system factors related to cervical cancer screening, a validation study of an artificial intelligence decision support system and an economic evaluation of the screening strategy.

Discussion

PRESCRIP-TEC aims to provide evidence regarding hrHPV self-testing and the World Health Organization’s recommendations for cervical cancer screening in a variety of settings, targeting vulnerable groups. The main quantitative findings of the project related to the impact on uptake and coverage of screening will be complemented by qualitative analyses of various determinants of successful implementation of screening. The study will also provide decision-makers with insights into economic aspects of implementing hrHPV self-testing, as well as evaluate the feasibility of using artificial intelligence for task-shifting in visual inspection with acetic acid.

Trial registration

ClinicalTrials.gov, NCT05234112. Registered 10 February 2022

Supplementary Information

The online version contains supplementary material available at (10.1186/s12889-022-13488-z).

Smartphone-Based Visual Inspection with Acetic Acid: An Innovative Tool to Improve Cervical Cancer Screening in Low-Resource Setting

Visual inspection with acetic acid (VIA) is recommended by the World Health Organization for primary cervical cancer screening or triage of human papillomavirus-positive women living in low-resource settings. Nonetheless, traditional VIA with the naked-eye is associated with large variabilities in the detection of pre-cancer and with a lack of quality control. Digital-VIA (D-VIA), using high definition cameras, allows magnification and zooming on transformation zones and suspicious cervical regions, as well as simultaneously compare native and post-VIA images in real-time. We searched MEDLINE and LILACS between January 2015 and November 2021 for relevant studies conducted in low-resource settings using a smartphone device for D-VIA. The aim of this review was to provide an evaluation on available data for smartphone use in low-resource settings in the context of D-VIA-based cervical cancer screenings. The available results to date show that the quality of D-VIA images is satisfactory and enables CIN1/CIN2+ diagnosis, and that a smartphone is a promising tool for cervical cancer screening monitoring and for on- and off-site supervision, and training. The use of artificial intelligence algorithms could soon allow automated and accurate cervical lesion detection.

Accuracy of Smartphone Images of the Cervix After Acetic Acid Application for Diagnosing Cervical Intraepithelial Neoplasia Grade 2 or Greater in Women With Positive Cervical Screening: A Systematic Review and Meta-Analysis

PURPOSE

Smartphones are used in cervical screening for visual inspection after acetic acid or Lugol's iodine (VIA/VILI) application to capture and share images to improve the sensitivity and interobserver variability of VIA/VILI. We undertook a systematic review and meta-analysis assessing the diagnostic accuracy of smartphone images of the cervix at the time of VIA/VILI (termed S-VIA) in the detection of precancerous lesions in women undergoing cervical screening.

METHODS

This systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies from January 1, 2010, to June 30, 2020, were assessed. MEDLINE/PubMed, Embase, CINAHL, Cochrane, and LILACS were searched. Cohort and cross-sectional studies were considered. S-VIA was compared with the reference standard of histopathology. We excluded studies where additional technology was added to the smartphone including artificial intelligence, enhanced visual assessment, and other algorithms to automatically diagnose precancerous lesions. The primary outcome was the accuracy of S-VIA for the diagnosis of cervical intraepithelial neoplasia grade 2 or greater (CIN 2+). Data were extracted, and we plotted the sensitivity, specificity, negative predictive value, and positive predictive value of S-VIA using forest plots. This study was prospectively registered with The International Prospective Register of Systematic Reviews:CRD42020204024.

RESULTS

Six thousand three studies were screened, 71 full texts assessed, and eight studies met criteria for inclusion, with six included in the final meta-analysis. The sensitivity of S-VIA for the diagnosis of CIN 2+ was 74.56% (95% CI, 70.16 to 78.95; I2 61.30%), specificity was 61.75% (95% CI, 56.35 to 67.15; I2 95.00%), negative predictive value was 93.71% (95% CI, 92.81 to 94.61; I2 0%), and positive predictive value was 26.97% (95% CI, 24.13 to 29.81; I2 61.3%).

CONCLUSION

Our results suggest that S-VIA has accuracy in the detection of CIN 2+ and may provide additional support to health care providers delivering care in low-resource settings.

Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening

Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Transfer learning involves fine-tuning a pre-trained network with optimal values of hyperparameters such as learning rate, batch size, and number of training epochs. The process of training the network identifies the relevant features for solving a specific problem. Adapting the pre-trained network to solve a different problem requires fine-tuning until relevant features are obtained. This is facilitated through the use of large number of filters present in the convolutional layers of pre-trained network. A very few features out of these features are useful for solving the problem in a different domain, while others are irrelevant, use of which may only reduce the efficacy of the network. However, by minimizing the number of filters required to solve the problem, the efficiency of the training the network can be improved. In this study, we consider identification of relevant filters using the pre-trained networks namely AlexNet and VGG-16 net to detect cervical cancer from cervix images. This paper presents a novel hybrid transfer learning technique, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using hybrid transfer learning achieved an accuracy of 91.46%.

Reliability and diagnostic performance of smartphone colposcopy

OBJECTIVE

To evaluate the interobserver and intraobserver reliability of smartphone colposcopy (SPC) versus conventional colposcopy and to determine diagnostic performance.

METHODS

A smartphone back camera was used to capture cervical images before and after application of acetic acid, and after application of lugol solution. Captured images were reviewed independently by two experienced colposcopists and findings were noted as per colposcopy. Smartphone-based diagnostic performance was calculated, and kappa statistics were used for measurement of agreement between SPC and conventional colposcopy findings.

RESULTS

A total of 114 women were included in the study. The kappa statistic for intraobserver reliability was 0.77 for both normal colposcopic findings and the transformation zone, indicating substantial agreement. Kappa values were 0.54 for acetowhite epithelium, 0.51 for lugol staining, and 0.51-0.60 for atypical vascularization. Kappa values for interobserver reliability were 0.76 for normal colposcopic findings, 0.56 for acetowhite epithelium, and 0.60 for lugol staining. The sensitivity, specificity, PPV, and NPV of SPC for CIN2+ were 88.2 (95% CI, 72.5-96.7), 48.7 (95% CI, 37.4-60.2), 0.42 (95% CI, 0.36-0.48), and 0.91 (95% CI, 0.79-0.96), respectively.

CONCLUSION

SPC showed substantial agreement between the histologic diagnoses based on the captured images and conventional colposcopic findings.