Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study

STI/HIV risk prediction model development-A novel use of public data to forecast STIs/HIV risk for men who have sex with men

A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG Boost are applied and evaluated on the Demographic and Health Surveys Program (DHSP), with thirteen features ultimately selected as the most predictive features. Classification and generalization experiments are conducted to test the accuracy, F1-score, precision, and area under the curve (AUC) performance of these four algorithms. Two imbalanced data solutions are also applied to reduce bias for classification performance improvement. The experimental results of these models demonstrate that the Random Forest algorithm yields the best results on HIV prediction, whereby the highest accuracy, and AUC are 0.99 and 0.99, respectively. The performance of the STI prediction achieves the best when the Synthetic Minority Oversampling Technique (SMOTE) is applied (Accuracy = 0.99, AUC = 0.99), which outperforms the state-of-the-art baselines. Two possible factors that may affect the classification and generalization performance are further analyzed. This automatic classification model helps to improve convenience and reduce the cost of HIV testing.

Identifying High-Risk Populations for Sexually Transmitted Infections in Chinese Men Who Have Sex With Men: A Cluster Analysis

Background

This study aimed to identify subpopulations of Chinese men who have sex with men (MSM) with distinct sexual behavioral patterns and explore their correlations with sexually transmitted infections (STIs).

Methods

We recruited 892 eligible MSM in Xi'an, China, collecting sociodemographic, sexual behavior, and STI data. Cluster analysis identified distinct sexual behavioral patterns, allowing us to examine STI differences across clusters.

Results

Among the 892 MSM analyzed, 3 clusters were identified. Cluster 1 (n = 157) exhibited high-risk sexual behavioral patterns, including the highest median number of sexual partners (5 vs 1 in cluster 2 vs 3 in cluster 3, P < .001), lowest consistent condom use for insertive anal sex (0% vs 64.12% vs 99.76%, P = .004) and receptive anal sex (9.22% vs 67.71% vs 98.91%, P = .006), highest uncertainty of partners' STIs (77.07% vs 57.89% vs 64.5%, P < .001), all recent partners being casual, longest length of sequential sexual acts (6 vs 5 vs 5, P = .045), and highest rates of gonorrhea (20.38% vs 10.09% vs 14.99%, P = .019) and chlamydia (16.56% vs 8.33% vs 13.21%, P = .045). Cluster 2 (n = 228) showed the lowest engagement in high-risk behaviors and STIs, characterized by the fewest sexual partners, highest certainty of partner's STIs, and all recent partners being regular. Cluster 3 (n = 507) showed moderate levels of high-risk behaviors and STIs, with the highest consistent condom use during anal sex.

Conclusions

This study identified 3 subpopulations of Chinese MSM with distinct sexual behavioral patterns. Targeted public health interventions to the most at-risk subpopulations of MSM are essential for STI prevention.

Exploring the diversity of sexual acts in Chinese men who have sex with men and its impacts on the risk of HIV and sexually transmitted infections

OBJECTIVES

High-risk sexual behaviour contributes to sexually transmitted infections (STIs), but the diversity of sexual acts in men who have sex with men (MSM) was understudied. We aimed to identify the diversity of sexual acts in Chinese MSM and its impacts on HIV/STI risk.

STUDY DESIGN

Cross-sectional study.

METHODS

Between January and September 2022, the study was conducted in Xi'an, China, to identify sexual acts performed during the last sexual episode, which was measured by the Shannon diversity index.

RESULTS

Of the 931 MSM, 2.9 % tested positive for HIV, 5.7 % for syphilis, 13.6 % for gonorrhoea and 12.9 % for chlamydia. The Shannon diversity index for individual sexual acts was 1.609 (IQR 0.693-1.946), whereas the index for sexual act pairs was 1.386 (IQR 0-1.792). MSM infected with gonorrhoea exhibited significantly greater diversity in individual sexual acts (1.792 vs. 1.609) and sexual act pairs (1.609 vs. 1.386) than otherwise. Compared with MSM having one partner over the past 3 months, MSM with 2-5 partners was 69.7 % more diverse in individual sexual acts (aOR = 1.697, 1.489-1.935) and 59.4 % more diverse in sexual act pairs (aOR = 1.594, 1.401-1.811). For MSM with >5 partners, the corresponding percentages were 84.8 % (aOR = 1.848, 1.624-2.104) and 56.2 % (aOR = 1.562, 1.368-1.782). Compared with those who did not use saliva as a lubricant, MSM who did were less diverse in individual sexual acts (aOR = 0.763, 0.662-0.878) and sexual act pairs (aOR = 0.752, 0.654-0.866).

CONCLUSION

MSM infected with gonorrhoea and those with multiple sexual partners are more diverse in sexual acts during sexual episodes.

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. Then, we developed an online symptom checker (iSpySTI.org) using Bayesian networks. In this study, various machine learning algorithms were trained and evaluated for their ability to predict these STI and anogenital conditions. We used the Z-test to compare their average area under the ROC curve (AUC) scores with the Bayesian networks for diagnostic accuracy.

RESULTS

The study population included 6,162 men (median age 30, IQR: 26-38; approximately 40% of whom had sex with men in the past 12 months) and 4,358 women (median age 27, IQR: 24-31). Non-gonococcal urethritis (NGU) (23.6%, 1447/6121), genital warts (11.7%, 718/6121) and balanitis (8.9%, 546/6121) were the most common conditions in men. Candidiasis (16.6%, 722/4538) and bacterial vaginosis (16.2%, 707/4538) were the most common conditions in women. During evaluation with unseen datasets, machine learning models performed well for most male conditions, with the AUC ranging from 0.81 to 0.95, except for urinary tract infections (UTI) (AUC 0.72). Similarly, the models achieved AUCs ranging from 0.75 to 0.95 for female conditions, except for cervicitis (AUC 0.58). Urethral discharge and other urinary symptoms were important features for predicting urethral gonorrhoea, NGU and UTIs. Similarly, participants selected skin images that were similar to their own lesions, and the location of the anogenital skin lesions were also strong predictors. The vaginal discharge (odour, colour) and itchiness were important predictors for bacterial vaginosis and candidiasis. The performance of the machine learning models was significantly better than Bayesian models for male balanitis, molluscum contagiosum and genital warts (P < 0.05) but was similar for the other conditions.

CONCLUSIONS

Both machine learning and Bayesian models could predict correct diagnoses with reasonable accuracy using prospectively collected data for 12 STIs and other common anogenital conditions. Further work should expand the number of anogenital conditions and seek ways to improve the accuracy, potentially using patient collected images to supplement questionnaire data.

Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) has become a powerful tool for WWTP, and is a challenge due to more complex multidimensional pattern recognition. Herein, weighted average ensemble strategy was conducted to assemble multiple diverse basic models to obtain better prediction capability to optimize carbon source addition (Model-1) and further control surplus sludge yield (Model-2). The ensemble models significantly outperformed all single models with MAE of 5.82 g/m3, MSE of 60.59 and R2 value of 0.98 in Model-1 and MAE of 15.09 g/m3, MSE of 449.01 and R2 value of 0.93 in Model-2. The optimal input feature subset was explored to reduce model complexity, indicating that the final ensemble models can predict with high precision using relatively few features with MAE of 6.41 g/m3, MSE of 78.49 and R2 value of 0.97 in Model-1 and MAE of 12.82 g/m3, MSE of 232.71 and R2 value of 0.95 in Model-2. Furthermore, the final models were deployed into an offline web application to facilitate their utility in real-world settings, demonstrating 47.25 % savings in carbon source addition and 15.89 % reductions in surplus sludge yield for an extra month of running. This work offers an efficient approach for the WWTP to optimize carbon source addition and provides new insights into controlling surplus sludge yield.

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.

METHODS

Between January and March 2024, we sent text message invitations to the Melbourne Sexual Health Centre (MSHC) attendees to participate in an online survey. We also advertised the survey on social media, the clinic's website, and posters in affiliated general practice clinics. This anonymous survey used a discrete choice experiment (DCE) to examine which MySTIRisk attributes would encourage potential users. We analysed the data using random parameters logit (RPL) and latent class analysis (LCA) models.

RESULTS

The median age of 415 participants was 31 years (interquartile range, 26-38 years), with a minority of participants identifying as straight or heterosexual (31.8%, n = 132). The choice to use MySTIRisk was most influenced by two attributes: cost and accuracy, followed by the availability of a pathology request form, level of anonymity, speed of receiving results, and whether the tool was a web or mobile application. LCA revealed two classes: "The Precisionists" (66.0% of respondents), who demanded high accuracy and "The Economists" (34.0% of respondents), who prioritised low cost. Simulations predicted a high uptake (97.7%) for a tool designed with the most preferred attribute levels, contrasting with lower uptake (22.3%) for the least preferred design.

CONCLUSIONS

Participants were more likely to use MySTIRisk if it was free, highly accurate, and could send pathology request forms. Tailoring the tool to distinct user segments could enhance its uptake and effectiveness in promoting early detection and prevention of HIV and STIs.

Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases

The skin, the largest organ of the human body, covers the body surface and serves as a crucial barrier for maintaining internal environmental stability. Various microorganisms such as bacteria, fungi, and viruses reside on the skin surface, and densely arranged keratinocytes exhibit inhibitory effects on pathogenic microorganisms. The skin is an essential barrier against pathogenic microbial infections, many of which manifest as skin lesions. Therefore, the rapid diagnosis of related skin lesions is of utmost importance for early treatment and intervention of infectious diseases. With the continuous rapid development of artificial intelligence, significant progress has been made in healthcare, transforming healthcare services, disease diagnosis, and management, including a significant impact in the field of dermatology. In this review, we provide a detailed overview of the application of artificial intelligence in skin and sexually transmitted diseases caused by pathogenic microorganisms, including auxiliary diagnosis, treatment decisions, and analysis and prediction of epidemiological characteristics.

Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata

BACKGROUND

Sexually transmitted infections (STIs) pose a significant global public health challenge. Early diagnosis and treatment reduce STI transmission, but rely on recognising symptoms and care-seeking behaviour of the individual. Digital health software that distinguishes STI skin conditions could improve health-seeking behaviour. We developed and evaluated a deep learning model to differentiate STIs from non-STIs based on clinical images and symptoms.

METHODS

We used 4913 clinical images of genital lesions and metadata from the Melbourne Sexual Health Centre collected during 2010-2023. We developed two binary classification models to distinguish STIs from non-STIs: (1) a convolutional neural network (CNN) using images only and (2) an integrated model combining both CNN and fully connected neural network (FCN) using images and metadata. We evaluated the model performance by the area under the ROC curve (AUC) and assessed metadata contributions to the Image-only model.

RESULTS

Our study included 1583 STI and 3330 non-STI images. Common STI diagnoses were syphilis (34.6%), genital warts (24.5%) and herpes (19.4%), while most non-STIs (80.3%) were conditions such as dermatitis, lichen sclerosis and balanitis. In both STI and non-STI groups, the most frequently observed groups were 25-34 years (48.6% and 38.2%, respectively) and heterosexual males (60.3% and 45.9%, respectively). The Image-only model showed a reasonable performance with an AUC of 0.859 (SD 0.013). The Image + Metadata model achieved a significantly higher AUC of 0.893 (SD 0.018) compared to the Image-only model (p < 0.01). Out of 21 metadata, the integration of demographic and dermatological metadata led to the most significant improvement in model performance, increasing AUC by 6.7% compared to the baseline Image-only model.

CONCLUSIONS

The Image + Metadata model outperformed the Image-only model in distinguishing STIs from other skin conditions. Using it as a screening tool in a clinical setting may require further development and evaluation with larger datasets.

Data mining and machine learning in HIV infection risk research: An overview and recommendations

In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the study presents a roadmap based on seven different aspects, utilizing various machine learning techniques for both novice researchers and experienced researchers seeking to comprehend the current state of the art in this area. While traditional regression modeling techniques have been commonly used, researchers are increasingly adopting more advanced fully supervised machine learning and deep learning techniques, which often outperform the traditional methods in predictive performance. Additionally, the study identifies nine new open research issues and outlines possible future research plans to enhance the outcomes of HIV infection risk research. This review is expected to be an insightful guide for researchers, illuminating current practices and suggesting advancements in the field.

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

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.

METHODS

A vignette-based cross-sectional survey was conducted with 662 clients (a median age of 30 years (IQR: 25-36), 418 male, 203 female, 41 other genders) at a sexual health clinic in Melbourne, Australia, between February and June 2023. Participants viewed five distinct hypothetical formats, presented in a randomised order, designed to display the same level of high risk for HIV/STIs: icon array, colour-coded risk metre, colour-coded risk bar, detailed text report and guideline recommendation. They reported their perceived risk, concern and intent to test for each risk display. Associations between the format of the risk display and the intention to test for HIV/STI were analysed using logistic regression.

RESULTS

About 378 (57%) of participants expressed that the risk metre was the easiest to understand. The risk metre (adjusted OR (AOR)=2.44, 95% CI=1.49 to 4.01) and risk bar (AOR=2.08, CI=1.33 to 3.27) showed the greatest likelihood of testing compared with the detailed text format. The icon array was less impactful (AOR=0.73, CI=0.57 to 0.94). The risk metre also elicited the most concern but was the most preferred and understood. High-risk perception and concern levels were strongly associated with their intention to have an HIV/STI test.

CONCLUSIONS

Displaying risk differently affects an individual's perceived risk of an HIV/STI and influences their intention to test.

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.

METHODS

We manually extracted 25 demographic features and clinical features from 1315 clinical records in the electronic health record system at Melbourne Sexual Health Center. We examined 16 machine learning models to predict a binary outcome of an STI or a non-STI diagnosis. We evaluated the models' performance with the area under the ROC curve (AUC), accuracy and F1-scores.

RESULTS

Our study included 1315 consultations, of which 36.8% (484/1315) were diagnosed with STIs and 63.2% (831/1315) had non-STI conditions. The study population predominantly consisted of heterosexual men (49.5%, 651/1315), followed by gay, bisexual and other men who have sex with men (GBMSM) (25.7%), women (21.6%) and unknown gender (3.2%). The median age was 31 years (intra-quartile range (IQR) 26-39). The top 5 performing models were CatBoost (AUC 0.912), Random Forest (AUC 0.917), LightGBM (AUC 0.907), Gradient Boosting (AUC 0.905) and XGBoost (AUC 0.900). The best model, CatBoost, achieved an accuracy of 0.837, sensitivity of 0.776, specificity of 0.831, precision of 0.782 and F1-score of 0.778. The key important features were lesion duration, type of skin lesions, age, gender, history of skin disorders, number of lesions, dysuria duration, anorectal pain and itchiness.

CONCLUSIONS

Our best model demonstrates a reasonable performance in distinguishing STIs from non-STIs. However, to be clinically useful, more detailed information such as clinical images, may be required to reach sufficient accuracy.

Lightweight federated learning for STIs/HIV prediction

This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man's data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework's impact and ultimately aim to improve health outcomes/health service optimisation.

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 MySTIRisk machine learning models to estimate infection risk scores. Optimal cutoffs for determining high-risk individuals were determined using Youden's index.

Results

The HIV risk score cutoff for high risk was 0.56, with 86.0% sensitivity (95% CI, 82.9%-88.7%) and 65.6% specificity (95% CI, 65.4%-65.8%). Thirty-five percent of participants were classified as high risk, which accounted for 86% of HIV cases. The corresponding cutoffs were 0.49 for syphilis (sensitivity, 77.6%; 95% CI, 76.2%-78.9%; specificity, 78.1%; 95% CI, 77.9%-78.3%), 0.52 for gonorrhea (sensitivity, 78.3%; 95% CI, 77.6%-78.9%; specificity, 71.9%; 95% CI, 71.7%-72.0%), and 0.47 for chlamydia (sensitivity, 68.8%; 95% CI, 68.3%-69.4%; specificity, 63.7%; 95% CI, 63.5%-63.8%). High-risk groups identified using these thresholds accounted for 78% of syphilis, 78% of gonorrhea, and 69% of chlamydia cases. The odds of positivity were significantly higher in the high-risk group than otherwise across all infections: 11.4 (95% CI, 9.3-14.8) times for HIV, 12.3 (95% CI, 11.4-13.3) for syphilis, 9.2 (95% CI, 8.8-9.6) for gonorrhea, and 3.9 (95% CI, 3.8-4.0) for chlamydia.

Conclusions

Risk scores generated by the AI-based risk assessment tool MySTIRisk, together with Youden's index, are effective in determining high-risk subgroups for HIV/STIs. The thresholds can aid targeted HIV/STI screening and prevention.

Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data

BACKGROUND AND AIMS

Sexually transmitted infections (STIs) are a significant global public health challenge due to their high incidence rate and potential for severe consequences when early intervention is neglected. Research shows an upward trend in absolute cases and DALY numbers of STIs, with syphilis, chlamydia, trichomoniasis, and genital herpes exhibiting an increasing trend in age-standardized rate (ASR) from 2010 to 2019. Machine learning (ML) presents significant advantages in disease prediction, with several studies exploring its potential for STI prediction. The objective of this study is to build males-based and females-based STI risk prediction models based on the CatBoost algorithm using data from the National Health and Nutrition Examination Survey (NHANES) for training and validation, with sub-group analysis performed on each STI. The female sub-group also includes human papilloma virus (HPV) infection.

METHODS

The study utilized data from the National Health and Nutrition Examination Survey (NHANES) program to build males-based and females-based STI risk prediction models using the CatBoost algorithm. Data was collected from 12,053 participants aged 18 to 59 years old, with general demographic characteristics and sexual behavior questionnaire responses included as features. The Adaptive Synthetic Sampling Approach (ADASYN) algorithm was used to address data imbalance, and 15 machine learning algorithms were evaluated before ultimately selecting the CatBoost algorithm. The SHAP method was employed to enhance interpretability by identifying feature importance in the model's STIs risk prediction.

RESULTS

The CatBoost classifier achieved AUC values of 0.9995, 0.9948, 0.9923, and 0.9996 and 0.9769 for predicting chlamydia, genital herpes, genital warts, gonorrhea, and overall STIs infections among males. The CatBoost classifier achieved AUC values of 0.9971, 0.972, 0.9765, 1, 0.9485 and 0.8819 for predicting chlamydia, genital herpes, genital warts, gonorrhea, HPV and overall STIs infections among females. The characteristics of having sex with new partner/year, times having sex without condom/year, and the number of female vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of male STIs. Similarly, ever having anal sex with a man, age and the number of male vaginal sex partners/lifetime have been identified as the top three significant predictors for the overall risk of female STIs.

CONCLUSIONS

This study demonstrated the effectiveness of the CatBoost classifier in predicting STI risks among both male and female populations. The SHAP algorithm revealed key predictors for each infection, highlighting consistent demographic characteristics and sexual behaviors across different STIs. These insights can guide targeted prevention strategies and interventions to alleviate the impact of STIs on public health.

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 (Supporting Timely and Appropriate Review and Treatment Online) study sought the views of sexual health service users on three web-based sexual health applications to better understand their usefulness, acceptability and accessibility. This paper reports the views and experiences of users of one of the applications called MySTIRisk. MySTIRisk estimates the risk of three common STIs and HIV using data from attendees of a metropolitan sexual health service.

Methods

This study used a bespoke qualitative design, informed by a developmental evaluation approach. Melbourne Sexual Health Centre clinic attendees' views were sought using semi-structured interviews conducted between October 2023 and January 2024 via videoconferencing, telephone and on site at the clinic. Data was analysed using qualitative data analysis methods.

Results

A diverse group of 14 participants described an ideal pathway to better sexual health outcomes that might be facilitated by use of the MySTIRisk application, particularly for individuals with limited sexual health knowledge, or affected by stigma and geographical barriers. This pathway was described as: 1) being concerned about my sexual health; 2) checking my STI risk easily and privately; 3) understanding and trusting the result; and 4) deciding how to look after my health. Factors that might influence this pathway were also described, including areas for improvement in accessibility and acceptability.

Conclusion

These findings support the role of web-based sexual health applications in facilitating access to sexual health education and behavioural change and underscore the importance of codesign approaches in improving their uptake and impact.