Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.

Introduction: Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications. Methods: The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model. Results: The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias. Conclusion: This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.

Effectiveness and Feasibility of Using Local Medical Universities for Capacity Building to Improve the Immunization Program in Ethiopia: Quasi-Experimental Study.

INTRODUCTION: Supportive supervision is one of the five essential components of the WHO's Reaching Every District (RED) strategy. However, it is generally not practiced based on the standard schedule because of capacity and low number of staff in the health system. Thus, this study aimed to test the feasibility and effectiveness of a capacity building and mentorship program in immunization by health science colleges to supplement the existing approach. METHODS: This study applied a pre-post quasi-experimental research design. The study included health workers of 30 health facilities (15 intervention and 15 control) followed for six months. A total of 90 health workers were included. To assess the effectiveness of the intervention package on immunization coverage and a change in RED strategy implementation over time, difference in difference (DID) analysis was used. Finally, a RE-AIM framework was used to evaluate the implementation process. RESULTS: The study indicated that the intervention package has a significant effect (P = 0.0001) on the overall implementation of RED outcomes. The DID analysis also indicated that health facilities in the intervention district have shown a higher Penta III coverage (17.4%) and complete vaccination coverage (16.6%) that are attributable to the intervention package. Similarly, knowledge and skills of health workers improved significantly (P < 0.05) after the intervention. The key informants also mentioned that the new approach was effective and acceptable. CONCLUSION: The newly introduced capacity building and mentorship program by well-trained personnel of medical universities had positive effects on the immunization program. Thus, it is recommended to facilitate policy adoption and readiness for routine use at large scale.

Healthcare providers' digital competency: a cross-sectional survey in a low-income country setting.

BACKGROUND: Healthcare providers across all clinical practice settings are progressively relying and adapting information communication technologies to perform their professional activities. In this era of technology, healthcare providers especially in lower income countries should have at least basic digital competency if a successful application of technology is to be achieved. The aim of this study was to assess digital competency of healthcare providers among seven public health centers in North-West Ethiopia. METHODS: A cross-sectional study design was applied to assess the basic digital competency of healthcare providers working in seven public health centers in North-west Amhara regional state, Ethiopia. Self-administered questionnaire adopted from the European commission's digital competency framework for assessing digital competency were used. A multivariable logistic regression was performed to identify factors associated with basic digital competency with p-value< 0.05 as a rule out for statistical significance. The strength of association was explained in terms of coefficient estimate, adjusted odds ratio and a 95% confidence interval (CI). RESULT: From the total of 193 healthcare providers included in the study, 167 of them responded which is a response rate of 86.5%. The majority of respondents 88 (52.7%) were males and the mean age was 28.2 years with a standard deviation of 5.5 years. The result indicated that all items demonstrated an adequate level of internal consistency with Cronbach alpha > 0 .7. Healthcare providers in those public health centers reported that problem solving, safety and communication are the most common challenges encountered. The multivariable logistic regression model indicated that factors such as sex, educational status, profession type, monthly income and years of experience are statistically significant predictors. CONCLUSION: Basic digital competency level of healthcare providers working in public health centers in this setting is relatively low. The results highlight the need to improve digital competency among healthcare providers focusing on the identified skill gaps.

E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey.

BACKGROUND: Chronic patients persistently seek for health information on the internet for medication information seeking, nutrition, disease management, information regarding disease preventive actions and so on. Consumers ability to search, find, appraise and use health information from the internet is known as eHealth literacy skill. eHealth literacy is a congregate set of six basic skills (traditional literacy, health literacy, information literacy, scientific literacy, media literacy and computer literacy). The aim of this study was to assess eHealth literacy level and associated factors among internet user chronic patients in North-west Ethiopia. METHODS: Institutional based cross-sectional study design was conducted. Stratified sampling technique was used to select 423 study participants among chronic patients. The eHealth literacy scale (eHEALS) was used for data collection. The eHEALS is a validated eight-item Likert scaled questionnaire used to asses self-reported capability of eHealth consumers to find, appraise, and use health related information from the internet to solve health problems. Statistical Package for Social science version 20 was used for data entry and further analysis. Multivariable logistic regression was used to examine the association between the eHealth literacy skill and associated factors. Significance was obtained at 95% CI and p < 0.05. RESULT: In total, 423 study subjects were approached and included in the study from February to May, 2019. The response rate to the survey was 95.3%. The majority of respondents 268 (66.3%) were males and mean age was 35.58 ± 14.8 years. The multivariable logistic regression model indicated that participants with higher education (at least having the diploma) are more likely to possess high eHealth literacy skill with Adjusted Odds Ratio (AOR): 3.48, 95% CI (1.54, 7.87). similarly, being government employee AOR: 1.71, 95% CI (1.11, 2.68), being urban resident AOR: 1.37, 95% CI (0.54, 3.49), perceived good health status AOR: 3.97, 95% CI (1.38, 11.38), having higher income AOR: 4.44, 95% CI (1.32, 14.86), Daily internet use AOR: 2.96, 95% CI (1.08, 6.76), having good knowledge about the availability and importance of online resources AOR: 3.12, 95% CI (1.61, 5.3), having positive attitude toward online resources AOR: 2.94, 95% CI (1.07, 3.52) and higher level of computer literacy AOR: 3.81, 95% CI (2.19, 6.61) were the predictors positively associated with higher eHealth literacy level. CONCLUSION: Besides the mounting indication of efficacy, the present data confirm that internet use and eHealth literacy level of chronic patients in this setting is relatively low which clearly implicate that there is a need to fill the skill gap in eHealth literacy among chronic patients which might help them in finding and evaluating relevant online sources for their health-related decisions.

Effect of mobile text message reminders on routine childhood vaccination: a systematic review and meta-analysis.

BACKGROUND: The World Health Organization estimates that 29% of under-five mortality could be prevented with existing vaccines. However, non-consistent attendance for immunization appointments remains a global challenge to healthcare providers. Thus, innovative strategies are required to reach the last mile where technology could be effectively utilized to achieve better compliance with children immunization schedules. Therefore, the aim of the review was to systematically collect and summarize the available evidence on the effectiveness of text message reminders on childhood vaccination. METHODS: This review was conducted according to a priori published protocol on PROSPERO. A systematic literature search of databases (PubMed/MEDLINE, EMBASE, Cochrane/Wiley library, and Science direct) was conducted. Eligibility and risk of bias assessments were performed independently by two reviewers. PRISMA flow diagrams were used to summarize the study selection process. Taking into account the level of heterogeneity, a random effects model was used and risk ratios with their 95% CI were used to present the pooled estimates. To investigate the sources of heterogeneity, subgroup analysis and meta-regression analysis were also considered. In this review, publication bias was assessed statistically using Harbord test. RESULTS: A total of 1771 articles were searched. Out of those 1771 articles, 558 duplicated articles were removed. About 1213 articles were further screened, and finally, ten articles met the inclusion criteria. The meta-analysis showed that there is a significant effect of text message reminders on childhood vaccination coverage (RR = 1.11; 95% CI 1.05-1.17) with a moderate level of heterogeneity (I2 = 64.3%, P = 0.003). The results from the Harbord test suggested that there is no evidence for publication bias (P = 0.340). CONCLUSION: This review highlights the potential benefits of incorporating mobile text message reminders into the standard management of childhood immunizations, especially in low- and middle-income countries. The frequency and timing of the text message reminders are also crucial in determining the effectiveness of text message reminders. Hence, mHealth interventions deserve more attention as a potential innovation to improve healthcare programs. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017074230.