Use of Smartphones for the Detection of Uterine Cervical Cancer: A Systematic Review

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.

Development and validation of artificial intelligence-based analysis software to support screening system of cervical intraepithelial neoplasia

Cervical cancer, the fourth most common cancer among women worldwide, often proves fatal and stems from precursor lesions caused by high-risk human papillomavirus (HR-HPV) infection. Accurate and early diagnosis is crucial for effective treatment. Current screening methods, such as the Pap test, liquid-based cytology (LBC), visual inspection with acetic acid (VIA), and HPV DNA testing, have limitations, requiring confirmation through colposcopy. This study introduces CerviCARE AI, an artificial intelligence (AI) analysis software, to address colposcopy challenges. It automatically analyzes Tele-cervicography images, distinguishing between low-grade and high-grade lesions. In a multicenter retrospective study, CerviCARE AI achieved a remarkable sensitivity of 98% for high-risk groups (P2, P3, HSIL or higher, CIN2 or higher) and a specificity of 95.5%. These findings underscore CerviCARE AI's potential as a valuable diagnostic tool for highly accurate identification of cervical precancerous lesions. While further prospective research is needed to validate its clinical utility, this AI system holds promise for improving cervical cancer screening and lessening the burden of this deadly disease.

Digital health interventions for cervical cancer care: A systematic review and future research opportunities

BACKGROUND

Cervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature concerning digital interventions in cervical cancer care. As such, we aim to uncover prevalent research gaps and highlight prospective avenues for future investigations.

METHODS

This study adopted a Systematic Literature Review (SLR) methodology where a total of 26 articles were reviewed from an initial set of 1110 articles following an inclusion-exclusion criterion.

RESULTS

The review highlights a deficiency in existing studies that address awareness dissemination, screening facilitation, and treatment provision for cervical cancer. The review also reveals future research opportunities like explore innovative approaches using emerging technologies to enhance awareness campaigns and treatment accessibility, consider diverse study contexts, develop sophisticated machine learning models for screening, incorporate additional features in machine learning research, investigate the impact of treatments across different stages of cervical cancer, and create more user-friendly applications for cervical cancer care.

CONCLUSIONS

The findings of this study can contribute to mitigating the adverse effects of cervical cancer and improving patient outcomes. It also highlights the untapped potential of Artificial Intelligence and Machine Learning, which could significantly impact our society.

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.

Smartphone-Based High-Throughput Fluorimetric Assay for Histidine Quantification in Human Urine Using 96-Well Plates

A high-throughput fluorimetric assay for histidine was developed, using a 96-well plates platform. The analyte reacts selectively with o-phthalaldehyde under mild alkaline conditions to form a stable derivative. Instrumental-free detection was carried out using a smartphone after illumination under UV light (365 nm). The method was proved to be linear up to 100 μM histidine, with an LLOQ (lower limit of quantification) of 10 μM. The assay was only prone to interference from glutathione and histamine that exist in the urine samples at levels that are orders of magnitude lower compared to histidine. Human urine samples were analyzed following minimum treatment and were found to contain histidine in the range of 280 to 1540 μM. The results were in good agreement with an HPLC corroborative method.

Exploring usability problems of mHealth applications developed for cervical cancer: An empirical study

Objectives

Nowadays, mobile health applications are developed to raise awareness and facilitate screening and treatment of cervical cancer, while a very few studies have been conducted focusing on the measurement and assurance of usability and exploring the acceptable user experience of such applications. Usability issues become a crucial concern for such cervical-cancer-related applications because users with diverse backgrounds in terms of education, information technology literacy, and geographic reasons are required to access those applications. The objective of this research is to evaluate the usability of mobile health applications developed for cervical cancer patients.

Methods

Two evaluation studies were conducted following the expert evaluation and a questionnaire-based user study. A total of four cervical-cancer-related applications that are focusing on the Awareness and Diagnosis theme were selected and each of the applications was evaluated by four usability experts. Then, a user study (n = 80) based on the Goal Question Metric was conducted to reveal the usability problems of four selected applications. Finally, findings of both evaluations were aggregated and analyzed.

Results

Both approaches showed that all applications suffer from several usability problems while "Cervical Cancer Guide" performs better and "Cervical Cancer Tracker" showed the least in performance from the usability perspective. Again, the Goal Question Metric performs noticeably better in assessing the learnability of the applications, while the analytical heuristic evaluation performs better in identifying the issues that cause user annoyance.

Conclusion

The methodology adopted and the usability problems revealed through this study can be well utilized by the information technology professionals or user interface designers for designing, evaluating, and developing the cervical-cancer-related applications with enhanced usability and user experience.

Development and Validation of the Promising PPAR Signaling Pathway-Based Prognostic Prediction Model in Uterine Cervical Cancer

A ligand-activated transcription factor, peroxisome proliferator-activated receptor (PPAR) regulates fatty acid uptake and transport. In several studies, upregulation of PPAR expression/activity by cancer cells has been associated with cancer progression. Worldwide, cancer of the cervix ranks fourth among women's cancers. Angiogenesis inhibitors have improved treatment for recurrent and advanced cervical cancer since their introduction 5 years ago. In spite of that, the median overall survival rate for advanced cervical cancer is 16.8 months, indicating that treatment effectiveness is still lacking. Thus, it is imperative that new therapeutic methods be developed. In this work, we first downloaded the PPAR signaling pathway-related genes from the previous study. In addition, the single-sample gene set enrichment analysis (ssGSEA) algorithm was applied to calculate the PPAR score of patients with cervical cancer. Furthermore, cervical cancer patients with different PPAR scores show different sensitivity to immune checkpoint therapy. In order to screen the genes to serve as the best biomarker for cervical cancer patients, we then construct the PPAR-based prognostic prediction model. The results revealed that PCK1, MT1A, AL096855.1, AC096711.2, FAR2P2, and AC099568.2 not only play a key role in the PPAR signaling pathway but also show good predictive value in cervical cancer patients. The gene set variation analysis (GSVA) enrichment analysis also proved that the PPAR signaling pathway is one of the most enriched pathways in the prognostic prediction model. Finally, further analysis revealed that AC099568.2 may be the most promising biomarker for the diagnosis, treatment, and prognosis in cervical cancer patients. Both the survival analysis and Receiver Operating Characteristic curve demonstrated that AC099568.2 plays a key role in cervical cancer patients. However, to our knowledge, this is the first time a study focused on the role of AC099568.2 in cervical cancer patients. Our work successfully revealed a new biomarker for cervical cancer patients, which also provides a new direction for future research.

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

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.