Adapting an artificial intelligence sexually transmitted diseases symptom checker tool for Mpox detection: the HeHealth experience
Rayner Kay Jin Tan
A B * , Dilruk Perera A B , Salomi Arasaratnam B and Yudara Kularathne B
A
B
Abstract
Artificial Intelligence (AI) applications have shown promise in the management of pandemics. In response to the global Monkeypox (Mpox) outbreak, the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases (STD) to develop a digital screening test for symptomatic Mpox using AI. Before the global Mpox outbreak, the team developed a smartphone app (HeHealth) where app users can use a smartphone to photograph their own penises to screen for symptomatic STD. The AI model initially used 5000 cases and a modified convolutional neural network to output prediction scores across visually diagnosable penis pathologies including syphilis, herpes simplex virus, and human papillomavirus. A total of about 22,000 users had downloaded the HeHealth app, and ~21,000 images were analysed using HeHealth AI technology. We then used formative research, stakeholder engagement, rapid consolidation images, a validation study, and implementation of the tool. A total of 1000 Mpox-related images had been used to train the Mpox symptom checker tool. Based on an internal validation, our digital symptom checker tool showed specificity of 87% and sensitivity of 90% for symptomatic Mpox. Several hurdles identified included issues of data privacy and security for app users, initial lack of data to train the AI tool, and the potential generalisability of input data. We offer several suggestions to help others get started on similar projects in emergency situations, including engaging a wide range of stakeholders, having a multidisciplinary team, prioritising pragmatism, as well as the concept that ‘big data’ in fact is made up of ‘small data’.
Keywords: behaviour, community health, diagnostics, help-seeking behaviours, public health, South-East Asia, STIs.
References
1 Yinka-Ogunleye A, Aruna O, Dalhat M, et al. Outbreak of human monkeypox in Nigeria in 2017–18: a clinical and epidemiological report. Lancet Infect Dis 2019; 19(8): 872-879.
| Crossref | Google Scholar | PubMed |
2 Gonsalves GS, Mayer K, Beyrer C. Déjà vu all over again? Emergent monkeypox, delayed responses, and stigmatized populations. J Urban Health 2022; 99(4): 603-606.
| Crossref | Google Scholar | PubMed |
3 Tan RKJ, Hsu LY. The global emergence of monkeypox. Ann Acad Med Singap 2022; 51(8): 456-457.
| Crossref | Google Scholar | PubMed |
4 Khan M, Mehran MT, Haq ZU, et al. Applications of artificial intelligence in COVID-19 pandemic: a comprehensive review. Expert Syst Appl 2021; 185: 115695.
| Crossref | Google Scholar | PubMed |
5 Abdelhamid AA, El-Kenawy E-SM, Khodadadi N, et al. Classification of monkeypox images based on transfer learning and the Al-Biruni Earth radius optimization algorithm. Mathematics 2022; 10(19): 3614.
| Crossref | Google Scholar |
6 Ahsan MM, Uddin MR, Farjana M, Sakib AN, Momin KA, Luna SA. Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified VGG16. arXiv preprint 2022; arXiv:220601862.
| Crossref | Google Scholar |
7 Ali SN, Ahmed MT, Paul J, et al. Monkeypox skin lesion detection using deep learning models: a feasibility study. arXiv preprint 2022; arXiv:220703342.
| Crossref | Google Scholar |
8 Kumar V. Analysis of CNN features with multiple machine learning classifiers in diagnosis of monkeypox from digital skin images. medRxiv 2022; 2022.09. 11.22278797.
| Crossref | Google Scholar |
9 Sitaula C, Shahi TB. Monkeypox virus detection using pre-trained deep learning-based approaches. J Med Syst 2022; 46(11): 78.
| Crossref | Google Scholar | PubMed |
10 Chadaga K, Prabhu S, Sampathila N, et al. Application of artificial intelligence techniques for monkeypox: a systematic review. Diagnostics 2023; 13(5): 824.
| Crossref | Google Scholar |
11 Ali SN, Ahmed MT, Jahan T, et al. A web-based Mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity. ArXiv 2023; abs/2306.14169.
| Crossref | Google Scholar |
12 National Library of Medicine. Mpox resource guide. 2024. Available at https://www.nnlm.gov/guides/mpox-resource-guide [accessed 6 April 2024]
13 Kraemer MUG, Tegally H, Pigott DM, et al. Tracking the 2022 monkeypox outbreak with epidemiological data in real-time. Lancet Infect Dis 2022; 22(7): 941-942.
| Crossref | Google Scholar | PubMed |
14 Patel A, Bilinska J, Tam JCH, et al. Clinical features and novel presentations of human monkeypox in a central London centre during the 2022 outbreak: descriptive case series. BMJ 2022; 378: e072410.
| Crossref | Google Scholar | PubMed |
