Background Uterine artery embolisation is well established as a treatment for symptomatic fibroids, however, there remain some uncertainties. We have carried out a focused literature review on three particularly challenging aspects – post-procedure fertility, symptomatic adenomyosis and large volume fibroids and uteri, to enable operators to utilise evidence-based guidance in patient selection, consent, and management. Review Literature searches were performed of the PubMed/Medline, Google scholar, EMBASE and Cochrane databases. The outcomes of our analysis of studies which recorded fertility rates in women desiring pregnancy following UAE for symptomatic fibroids found an overall mean pregnancy rate of 39.4%, live birth rate of 69.2% and miscarriage rate of 22%. The major confounding factor was patient age with many studies including women over 40 years who already have lower fertility compared to younger cohorts. Miscarriage rates and pregnancy rates in the studies analysed were comparable to the age matched population. Treatment of pure adenomyosis and adenomyosis with co-existing uterine fibroids with UAE has been shown to produce symptomatic improvement with better outcomes in those with combined disease. Although the effectiveness is not as high as it is in pure fibroid disease, UAE provides a viable and safe alternative for patients seeking symptom relief and uterine preservation. Our analysis of studies assessing the outcomes of UAE in patients with large volume uteri and giant fibroids (> 10 cm) demonstrate no significant difference in major complication rates demonstrating that fibroid size should not be a contraindication to UAE. Conclusion Our findings suggest uterine artery embolisation can be offered to women desiring pregnancy with fertility and miscarriage rates comparable to that of the age-matched general population. It is also an effective therapeutic option for symptomatic adenomyosis as well as for the treatment of large fibroids > 10 cm in diameter. Caution is advised in those with uterine volumes greater than 1000cm3. It is however clear that the quality of evidence needs to be improved on with an emphasis on well-designed randomised controlled trials addressing all three areas and the consistent use of validated quality of life questionnaires for outcome assessment to enable effective comparison of outcomes in different studies.

Background Social anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences. Objective This study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions. Methods This study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naïve Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data. Results The severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naïve Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model. Conclusions This study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias. Trial Registration Clinical Research Information Service KCT0003854; https://cris.nih.go.kr/cris/search/detailSearch.do/13508

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