Digital Screen Time and the Risk of Female Breast Cancer: A Retrospective Matched Case-Control Study

Background

As the use of electronic devices such as mobile phones, tablets, and computers continues to rise globally, concerns have been raised about their potential impact on human health. Exposure to high energy visible (HEV) blue light, emitted from digital screens, particularly the so-called artificial light at night (ALAN), has been associated with adverse health effects, ranging from disruption of circadian rhythms to cancer. Breast cancer incidence rates are also increasing worldwide.

Objective

This study aimed at finding a correlation between breast cancer and exposure to blue light from mobile phone.

Material and Methods

In this retrospective matched case-control study, we aimed to investigate whether exposure to blue light from mobile phone screens is associated with an increased risk of female breast cancer. We interviewed 301 breast cancer patients (cases) and 294 controls using a standard questionnaire and performed multivariate analysis, chi-square, and Fisher's exact tests for data analysis.

Results

Although heavy users in the case group of our study had a statistically significant higher mean 10-year cumulative exposure to digital screens compared to the control group (7089±14985 vs 4052±12515 hours, respectively, P=0.038), our study did not find a strong relationship between exposure to HEV and development of breast cancer.

Conclusion

Our findings suggest that heavy exposure to HEV blue light emitted from mobile phone screens at night might constitute a risk factor for promoting the development of breast cancer, but further large-scale cohort studies are warranted.

Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens

BACKGROUND

Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival.

OBJECTIVE

To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.

MATERIAL AND METHODS

In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection.

RESULTS

The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%).

CONCLUSION

Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically.

Lateral neck radiography for prediction of difficult orotracheal intubation

BACKGROUND AND OBJECTIVES

Failed endotracheal intubation is a principal cause of morbidity and mortality in anesthetized patients. The aim of this study was to investigate the efficiency of lateral neck radiography in predicting difficult intubation.

METHODS

In a prospective triple-blind study, 100 patients (aged 18-89 yr), scheduled for elective surgery were randomly selected. Lateral neck X-ray was obtained from each of the patients before operation. Several angles and parameters on the X-ray were proposed to illustrate a relationship with easy or difficult intubation. A radiologist recorded these angles before the operation. An anaesthesiologist also determined the Mallampati score preoperation. At the time of intubation, two other anesthesiologists performed a laryngoscopy and, according to established criteria, identified the patients as easy or difficult intubation. The results were then compared with each other.

RESULTS

Fifteen patients were identified as having difficult intubation (laryngoscopy Grades III and IV). Sensitivity and specificity of the Mallampati Class test were 26% and 100%, respectively. The sensitivity and specificity of the lateral neck X-ray for three measured angles were 100%. The positive and negative predictive values (NPVs) for those angles were 100% and for Mallampati classification were 100% and 80%, respectively.

CONCLUSIONS

Compared to the Mallampati Class test, our method of analyzing the lateral X-ray, although not as easy and universally applicable as Mallampati Class test, proved to be a suitable method for predicting difficult intubation.