Exploring artificial intelligence for differentiating early syphilis from other skin lesions: a pilot study.

Background: Early diagnosis of syphilis is vital for its effective control. This study aimed to develop an Artificial Intelligence (AI) diagnostic model based on radiomics technology to distinguish early syphilis from other clinical skin lesions.

Methods: The study collected 260 images of skin lesions caused by various skin infections, including 115 syphilis and 145 other infection types.

Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.

Background: A significant proportion of individuals with symptoms of sexually transmitted infection (STI) delay or avoid seeking healthcare, and digital diagnostic tools may prompt them to seek healthcare earlier. Unfortunately, none of the currently available tools fully mimic clinical assessment or cover a wide range of STIs.

Methods: We prospectively invited attendees presenting with STI-related symptoms at Melbourne Sexual Health Centre to answer gender-specific questionnaires covering the symptoms of 12 common STIs using a computer-assisted self-interviewing system between 2015 and 2018.

A web-based tool for cancer risk prediction for middle-aged and elderly adults using machine learning algorithms and self-reported questions.

Background: From a global perspective, China is one of the countries with higher incidence and mortality rates for cancer.

Objective: Our objective is to create an online cancer risk prediction tool for middle-aged and elderly Chinese adults by leveraging machine learning algorithms and self-reported data.

Method: Drawing from a cohort of 19,798 participants aged 45 and above from the China Health and Retirement Longitudinal Study (2011 - 2018), we employed nine machine learning algorithms (LR: Logistic Regression, Adaboost: Adaptive Boosting, SVM: Support Vector Machine, RF: Random Forest, GNB: Gaussian Naive Bayes, GBM: Gradient Boosting Machine, LGBM: Light Gradient Boosting Machine, XGBoost: eXtreme Gradient Boosting, KNN: K - Nearest Neighbors), which are mainly used for classification and regression tasks, to construct predictive models for various cancers.

Preferences for attributes of an artificial intelligence-based risk assessment tool for HIV and sexually transmitted infections: a discrete choice experiment.

Introduction: Early detection and treatment of HIV and sexually transmitted infections (STIs) are crucial for effective control. We previously developed MySTIRisk, an artificial intelligence-based risk tool that predicts the risk of HIV and STIs. We examined the attributes that encourage potential users to use it.

What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment.

Background: One of the World Health Organization (WHO) recommendations to achieve its global targets for sexually transmitted infections (STIs) is the increased use of digital technologies. Melbourne Sexual Health Centre (MSHC) has developed an AI-assisted screening application (app) called AiSTi for the detection of common STI-related anogenital skin conditions. This study aims to understand the community's preference for using the AiSTi app.

User experiences of an AI application for predicting risk of sexually transmitted infections.

Objective: Awareness of one's individual risk of sexually transmitted infections (STIs) and human immunodeficiency virus (HIV) is a necessary precursor to engagement with prevention strategies and sexual health care. Web-based sexual health applications may improve engagement in sexual health prevention and care by providing individualised and evidence-based sexual health information. The STARTOnline () study sought the views of sexual health service users on three web-based sexual health applications to better understand their usefulness, acceptability and accessibility.

Clinical features-based machine learning models to separate sexually transmitted infections from other skin diagnoses.

Introduction: Many sexual health services are overwhelmed and cannot cater for all the individuals who present with sexually transmitted infections (STIs). Digital health software that separates STIs from non-STIs could improve the efficiency of clinical services. We developed and evaluated a machine learning model that predicts whether patients have an STI based on their clinical features.

Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence-Based Risk Assessment Tool.

Background: We have previously developed an artificial intelligence-based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresholds to identify individuals at high risk for HIV/STIs.

Methods: Using 2008-2022 data from 216 252 HIV, 227 995 syphilis, 262 599 gonorrhea, and 320 355 chlamydia consultations at a sexual health center, we applied machine learning models to estimate infection risk scores.

Assessing the effectiveness of HIV/STI risk communication displays among Melbourne Sexual Health Centre attendees: a cross-sectional, observational and vignette-based study.

Introduction: Increasing rates of sexually transmitted infections (STIs) over the past decade underscore the need for early testing and treatment. Communicating HIV/STI risk effectively can promote individuals' intention to test, which is critical for the prevention and control of HIV/STIs. We aimed to determine which visual displays of risk would be the most likely to increase testing or use of prevention strategies.

Assessing disparity in the distribution of HIV and sexually transmitted infections in Australia: a retrospective cross-sectional study using Gini coefficients.

Introduction: The risk of HIV and sexually transmitted infections (STIs) varies substantially across population groups in Australia. We examined this disparity in HIV/STI distribution using Gini coefficients, where scores closer to one indicate greater disparity.

Methods: We used demographic and sexual behaviour data from the Melbourne Sexual Health Centre, between 2015 and 2018.

Should human papillomavirus vaccination target women over age 26, heterosexual men and men who have sex with men? A targeted literature review of cost-effectiveness.

Background: Human papillomavirus (HPV) vaccination for young women up to age 26 is highly cost-effective and has been implemented in 65 countries globally. We investigate the cost-effectiveness for HPV vaccination program in older women (age > 26 years), heterosexual men and men who have sex with men (MSM).

Method: A targeted literature review was conducted on PubMed for publications between January 2000 and January 2017 according to the PRISMA guidelines.

Modelling the Epidemiological Impact and Cost-Effectiveness of PrEP for HIV Transmission in MSM in China.

Risk of HIV infection is high in Chinese MSM, with an annual HIV incidence ranging from 3.41 to 13.7/100 person-years.

Prevalence of human papillomavirus by geographical regions, sexual orientation and HIV status in China: a systematic review and meta-analysis.

Objective: Human papillomavirus (HPV) infection causes multiple cancers in both women and men. In China, both HPV vaccination and cervical cancer screening coverages are low. We aim to investigate the temporal and geographical trends of HPV DNA prevalence in heterosexual men, women, men who have sex with men (MSM) and people living with HIV (PLHIV) in China.