Soe Nyi Nyi, Towns Janet M, Latt Phyu Mon, Woodberry Owen, Chung Mark, Lee David, Ong Jason J, Chow Eric P F, Zhang Lei, Fairley Christopher K
Melbourne Sexual Health Centre, Alfred Health, 580 Swanston Street, Carlton, Melbourne, VIC, 3053, Australia.
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
BMC Infect Dis. 2024 Dec 18;24(1):1408. doi: 10.1186/s12879-024-10285-4.
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.
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.
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.
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.
相当一部分有性传播感染（STI）症状的人会延迟或避免寻求医疗保健，而数字诊断工具可能会促使他们更早地寻求医疗保健。不幸的是，目前可用的工具都不能完全模拟临床评估，也不能涵盖广泛的性传播感染。
2015年至2018年期间，我们前瞻性地邀请了墨尔本性健康中心出现性传播感染相关症状的就诊者，使用计算机辅助自我访谈系统回答涵盖12种常见性传播感染症状的针对性别问卷。然后，我们使用贝叶斯网络开发了一个在线症状检查器（iSpySTI.org）。在本研究中，对各种机器学习算法进行了训练和评估，以了解它们预测这些性传播感染和肛门生殖器疾病的能力。我们使用Z检验将它们的ROC曲线下平均面积（AUC）得分与贝叶斯网络进行比较，以评估诊断准确性。
研究人群包括6162名男性（中位年龄30岁，四分位距：26 - 38岁；其中约40%在过去12个月内与男性发生过性行为）和4358名女性（中位年龄27岁，四分位距：24 - 31岁）。男性中最常见的疾病是非淋菌性尿道炎（NGU）（23.6%，1447/6121）、尖锐湿疣（11.7%，718/6121）和龟头炎（8.9%，546/6121）。女性中最常见的疾病是念珠菌病（16.6%，722/4538）和细菌性阴道病（16.2%，707/4538）。在对未见过的数据集进行评估时，机器学习模型对大多数男性疾病的表现良好，AUC范围为0.81至0.95，但尿路感染（UTI）除外（AUC为0.72）。同样，这些模型对女性疾病的AUC范围为0.75至0.95，但宫颈炎除外（AUC为0.58）。尿道分泌物和其他泌尿系统症状是预测尿道淋病、非淋菌性尿道炎和尿路感染的重要特征。同样，参与者选择的皮肤图像与他们自己的病变相似，肛门生殖器皮肤病变的位置也是很强的预测因素。阴道分泌物（气味、颜色）和瘙痒是细菌性阴道病和念珠菌病的重要预测因素。对于男性龟头炎、传染性软疣和尖锐湿疣，机器学习模型的表现明显优于贝叶斯模型（P < 0.05），但在其他疾病方面表现相似。
使用前瞻性收集的12种性传播感染和其他常见肛门生殖器疾病的数据，机器学习和贝叶斯模型都能以合理的准确性预测正确诊断。进一步的工作应扩大肛门生殖器疾病的数量，并寻求提高准确性的方法，可能利用患者收集的图像来补充问卷数据。
