Improving intervention design to promote cervical cancer screening among hard-to-reach women: assessing beliefs and predicting individual attendance probabilities in Bogotá, Colombia

Impact of virtual education based on health belief model on cervical cancer screening behavior in middle-aged women: A quasi-experimental study

BACKGROUND

Cervical cancer is one of the most common cancers in women worldwide and a cause of high mortality among people. Pap smear screening is an appropriate method to prevent cervical cancer and reduce its mortality.

AIM

This study aimed to determine the effect of web-based education based on the Health Belief Model (HBM) on cervical cancer screening behavior in middle-aged women.

METHODS AND RESULTS

This study is a quasi-experimental interventional research that was conducted on 240 middle-aged women aged 40-59 years in Isfahan, Iran, in 2022. An online educational intervention based on the constructs of the Health Belief Model was conducted for the intervention group using the Triple-B platform. The information on the intervention and control groups was collected before, immediately after the intervention, and 2 months later using a valid questionnaire. The gathered Data was analyzed using ANOVA and LSD post-hoc, independent samples t test, chi-square, and MANCOVA statistical tests in SPSS 26 software. After the intervention, the mean scores of knowledge, perceived susceptibility, perceived severity, perceived benefits, self-efficacy, and internal cues to action in the intervention group increased and the mean score of perceived barriers decreased (p < .001). The mean score of the external cues to action did not show a significant difference between the intervention and control groups before, immediately, and 2 months after the intervention. Two months after the intervention, 32 women (26.2%) in the intervention group and two women (1.7%) in the control group performed the Pap smear test.

CONCLUSION

Web-based educational intervention based on HBM using different strategies such as question and answer, presentation of infographics, lectures, brainstorming, showing videos and numerous educational images can be an effective way for increasing knowledge and cognitive variables of women and doing Pap smear test.

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.