Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network

The Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. The captured cervical images must be understood for diagnosis, prognosis and treatment planning of the anomalies. This Cervix image understanding is generally performed by skilled medical professionals. However, the scarcity of human medical experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. This paper, the model uses Regional Based Convolutional Neural Network (R-CNN) to effectively visualize of pre-cancerous lesions and to aid in diagnosis of the disease. The model was trained, on a dataset comprising of 10,383 cervical images samples. The datasets were derived from public dataset repositories. The training samples comprised of type class 1, 2 and 3 traits of cervical precancerous traits. The performance was evaluated using K-nearest -neighbor model over R-CNN. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous lesions. This study findings established that the proposed model in provision of the better accuracy and misclassifications performance than various testing algorithms

Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson’s correlation techniques are utilized for feature selection. Since the data was imbalanced, the Borderline-SMOTE technique was used to balance the data. The final stacked machine learning model obtained an accuracy, precision, recall, F1-score, area under curve (AUC) and average precision of 98%, 97%, 99%, 98%, 100% and 100%, respectively. To make the model explainable and interpretable to clinicians, explainable artificial intelligence algorithms such as Shapley additive values (SHAP), local interpretable model agnostic explanation (LIME), random forest and ELI5 have been effectively utilized. The optimistic results indicate the potential of automated frameworks to assist doctors and medical professionals in diagnosing and screening potential cervical cancer patients

Cytology interpretation after a change to HPV testing in primary cervical screening : observational study from the English pilot

Background Overcalling of abnormalities has been a concern for using cytology triage after positive high-risk human papillomavirus (HPV) tests in cervical screening. Methods The authors studied the detection of cytological and histological abnormalities at age 24 to 64 years, using data from the English HPV pilot. The pilot compared routine implementation of primary cervical screening based on cytology (N = 931,539), where HPV test results were not available before cytology reporting, with that based on HPV testing (N = 403,269), where cytology was only required after positive HPV tests. Results Revealed HPV positivity was associated with a higher direct referral to colposcopy after any abnormality (adjusted odds ratio [ORadj], 1.16; 95% confidence interval [CI], 1.14-1.18). Laboratories with higher direct referral referred fewer persistently HPV-positive women after early recall. The detection of high-grade cervical intraepithelial neoplasia (CIN2+) after direct referral increased with an ORadj of 1.17 (95% CI, 1.13-1.20) for informed versus uninformed cytology. Generally, the positive predictive value (PPV) of colposcopy for CIN2+ remained comparable under both conditions of interpreting cytology. In women 50 to 64 years old with high-grade dyskaryosis, however, the PPV increased from 71% to 83% after revealing HPV positivity (ORadj, 2.05; 95% CI, 1.43-2.93). Conclusions Quality-controlled cervical screening programs can avoid inappropriate overgrading of HPV-positive cytology

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

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

Study protocol for a two-site clinical trial to validate a smartphone-based artificial intelligence classifier identifying cervical precancer and cancer in HPV-positive women in Cameroon.

INTRODUCTION: Cervical cancer remains a major public health challenge in low- and middle-income countries (LMICs) due to financial and logistical issues. WHO recommendation for cervical cancer screening in LMICs includes HPV testing as primary screening followed by visual inspection with acetic acid (VIA) and treatment. However, VIA is a subjective procedure dependent on the healthcare provider's experience. Its accuracy can be improved by computer-aided detection techniques. Our aim is to assess the performance of a smartphone-based Automated VIA Classifier (AVC) relying on Artificial Intelligence to discriminate precancerous and cancerous lesions from normal cervical tissue. METHODS: The AVC study will be nested in an ongoing cervical cancer screening program called "3T-study" (for Test, Triage and Treat), including HPV self-sampling followed by VIA triage and treatment if needed. After application of acetic acid on the cervix, precancerous and cancerous cells whiten more rapidly than non-cancerous ones and their whiteness persists stronger overtime. The AVC relies on this key feature to determine whether the cervix is suspect for precancer or cancer. In order to train and validate the AVC, 6000 women aged 30 to 49 years meeting the inclusion criteria will be recruited on a voluntary basis, with an estimated 100 CIN2+, calculated using a confidence level of 95% and an estimated sensitivity of 90% +/-7% precision on either side. Diagnostic test performance of AVC test and two current standard tests (VIA and cytology) used routinely for triage will be evaluated and compared. Histopathological examination will serve as reference standard. Participants' and providers' acceptability of the technology will also be assessed. The study protocol was registered under ClinicalTrials.gov (number NCT04859530). EXPECTED RESULTS: The study will determine whether AVC test can be an effective method for cervical cancer screening in LMICs

Performance of visual inspection of the cervix with acetic acid (VIA) for triage of HPV screen-positive women: results from the ESTAMPA study

Q1Q1Pacientes con Virus del Papiloma Humano (VPH)VIA is recommended for triage of HPV-positive women attending cervical screening. In the multicentric ESTAMPA study, VIA performance for detection of cervical intraepithelial neoplasia grade 3 or worse (CIN3+) among HPV-positive women was evaluated. Women aged 30-64 years were screened with HPV testing and cytology and referred to colposcopy if either test was positive. At colposcopy visit, study-trained midwives/nurses/GPs performed VIA ahead of colposcopy. VIA was considered positive if acetowhite lesions were observed in or close to the transformation zone. Ablative treatment eligibility was assessed for VIA positives. Performance indicators were estimated. Three thousand one hundred and forty-two HPV-positive women were included. Sensitivity for CIN3+ was 85.9% (95% CI 81.2-89.5) among women <50 years and, although not significant, slightly lower in women 50+ (78.0%, 95% CI 65.9-86.6). Overall specificity was 58.6% (95% CI 56.7-60.5) and was significantly higher among women 50+ (70.3%, 95% CI 66.8-73.5) compared to women <50 (54.3%, 95% CI 52.1-56.5). VIA positivity was lower among women 50+ (35.2%, 95% CI 31.9-38.6) compared to women <50 (53.2, 95% CI 51.1-55.2). Overall eligibility for ablative treatment was 74.5% and did not differ by age. VIA sensitivity, specificity, and positivity, and ablative treatment eligibility varied highly by provider (ranges: 25%-95.4%, 44.9%-94.4%, 8.2%-65.3%, 0%-98.7%, respectively). VIA sensitivity for cervical precancer detection among HPV-positive women performed by trained providers was high with an important reduction in referral rates. However, scaling-up HPV screening triaged by VIA will be challenging due to the high variability of VIA performance and providers' need for training and supervision.https://orcid.org/0000-0001-7187-9946Revista Internacional - IndexadaA1N

Cervical Cancer Screening Management in Primary Care: A Quality Improvement Project

Cervical cancer screening has evolved throughout the years into the current, very effective, algorithms for screening and management. The success of improved early detection of cervical cancer has saved many lives (Lees, Erickson, & Huh, 2016). The addition of human papillomavirus testing and genotyping has allowed for more efficient, and less invasive, management of cervical cancer screening (Cox, 2009). While there are significant advantages to these new guidelines, there are barriers to applying them in practice. The clinical site for the project was identified to be in need of a quality improvement project aimed at creating an improved patient notification, tracking and reminder system as well as improving provider adherence with the evidence-based guidelines. There were 48 total eligible providers that were included in the project. After identification of the problem, a review of the literature was undertaken to identify an evidence-based strategy for addressing practice gaps. This literature review focused on provider guideline adherence with cervical cancer screening guidelines and patient notification, tracking and reminder systems. Current literature demonstrates a gap in provider guideline adherence nationwide as well as strategies aimed at improving both provider and patient adherence with the reccomendations. These include use of consistent patient notification processes, implementation of an electronic tracking and reminder system, and provider educational sessions aimed at improving guideline compliance. Donabedian’s (2005) quality improvement framework was utilized to divide the literature findings into those interventions that effect outcomes, structure, and process of care in order to form the project plan and methods. Following this in-depth look at the background and existing literature, the project plan was established. The plan consisted of two phases: the first focusing on creation of project materials and preparation for project implementation, and the second focusing on the roll out of the new process and data collection for project analysis. Two objectives were identified for this project: improve provider adherence to the 2012 American Society of Colposcopy and Cervical Pathology Guidelines and implementation of an electronic patient notification, tracking and reminder system. A plan for data collection and analysis through pre- and post-implementation provider surveys and chart audits was established. After project implementation, data collection and analysis occurred. Objective One was evaluated in order to determine if the project implementation correlated with an increase in provider guideline adherence. The quality improvement project did find an improvement in guideline adherence in recommending appropriate follow-up for patients following receipt of cervical cancer screening results. For their survey responses on a series of patient vignettes, as well as whether patients were actually screened at an appropriate interval according to the recommendations, the providers were not found to show a statistical improvement following implementation of the project. In evaluating Objective Two, there was found to be moderate compliance on the part of the providers with the new process in the weeks following project implementation. Nursing participants in the new process were found to be 100% compliant with following the process. No statistical difference was found in provider beliefs regarding the practice’s tracking and reminder system pre- and post-intervention. Limitations existed in this study that limit the ability of the researcher to make assumptions based on the findings. Regardless, this project served to address the need for a robust notification tracking and reminder system. This system helps to ensure that patients receive timely, clear, and concise communication regarding their cervical cancer screening results and what these results mean for them. Additionally, they are notified and reminded to follow-up as needed. This is all done in an attempt to continue to drive down cervical cancer rates while also reducing unnecessary, and costly, procedures and testing

Automated image analysis in multispectral system for cervical cancer diagnostic

Uterine cervical cancer is the second most common cancer in women worldwide. The accuracy of colposcopy is highly dependent on the physicians individual skills. In expert hands, colposcopy has been reported to have a high sensitivity (96%) and a low specificity (48%) when differentiating abnormal tissues. This leads to a significant interest to activities aimed at the new diagnostic systems and new automatic methods of coloposcopic images analysis development. The presented paper is devoted to developing method based on analyses fluorescents images obtained with different excitation wavelength. The sets of images were obtained in clinic by multispectral colposcope LuxCol. The images for one patient includes: images obtained with white light illumination and with polarized white light; fluorescence image obtained by excitation at wavelength of 360nm, 390nm, 430nm and 390nm with 635 nm laser. Our approach involves images acquisition, image processing, features extraction, selection of the most informative features and the most informative image types, classification and pathology map creation. The result of proposed method is the pathology map - the image of cervix shattered on the areas with the definite diagnosis such as norm, CNI (chronic nonspecific inflammation), CIN(cervical intraepithelial neoplasia). The obtained result on the border CNI/CIN sensitivity is 0.85, the specificity is 0.78. Proposed algorithms gives possibility to obtain correct differential pathology map with probability 0.8. Obtained results and classification task characteristics shown possibility of practical application pathology map based on fluorescents images