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Gonorrhoea, the sexually transmissible infection caused by Neisseria gonorrhoeae, has a substantial impact on sexual and reproductive health globally with an estimated 82 million new infections each year worldwide. N. gonorrhoeae antimicrobial resistance continues to escalate, and disease control is largely reliant on effective therapy as there is no proven effective gonococcal vaccine available. However, there is increasing evidence from observational cohort studies that the serogroup B meningococcal vaccine four-component meningitis B vaccine (4CMenB) (Bexsero), licensed to prevent invasive disease caused by Neisseria meningitidis, may provide cross-protection against the closely related bacterium N. gonorrhoeae. This study will evaluate the efficacy of 4CMenB against N. gonorrhoeae infection in men (cis and trans), transwomen and non-binary people who have sex with men (hereafter referred to as GBM+).
This is a double-blind, randomised placebo-controlled trial in GBM+, either HIV-negative on pre-exposure prophylaxis against HIV or living with HIV (CD4 count >350 cells/mm3), who have had a diagnosis of gonorrhoea or infectious syphilis in the last 18 months (a key characteristic associated with a high risk of N. gonorrhoeae infection). Participants are randomised 1:1 to receive two doses of 4CMenB or placebo 3 months apart. Participants have 3-monthly visits over 24 months, which include testing for N. gonorrhoeae and other sexually transmissible infections, collection of demographics, sexual behaviour risks and antibiotic use, and collection of research samples for analysis of N. gonorrhoeae-specific systemic and mucosal immune responses. The primary outcome is the incidence of the first episode of N. gonorrhoeae infection, as determined by nucleic acid amplification tests, post month 4. Additional outcomes consider the incidence of symptomatic or asymptomatic N. gonorrhoeae infection at different anatomical sites (ie, urogenital, anorectum or oropharynx), incidence by N. gonorrhoeae genotype and antimicrobial resistance phenotype, and level and functional activity of N. gonorrhoeae-specific antibodies.
Ethical approval was obtained from the St Vincent’s Hospital Human Research Ethics Committee, St Vincent’s Hospital Sydney, NSW, Australia (ref: 2020/ETH01084). Results will be disseminated in peer-reviewed journals and via presentation at national and international conferences.
Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.
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