{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T17:17:21Z","timestamp":1793985441027,"version":"4.8.0"},"reference-count":37,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Health WA and Curtin University","award":["DoH202310626\/7"],"award-info":[{"award-number":["DoH202310626\/7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Streptococcus pharyngitis (strep throat) poses a significant health challenge in rural and remote Indigenous communities in Australia, where access to medical resources is limited. Delays in diagnosis and treatment increase the risk of serious complications, including acute rheumatic fever and rheumatic heart disease. This paper presents a proof-of-concept AI-based diagnostic model designed to support clinicians in underserved communities. The model combines a lightweight Swin Transformer\u2013based image classifier with a BLIP-2-based explainable image annotation system. The classifier predicts strep throat from throat images, while the explainable model enhances transparency by identifying key clinical features such as tonsillar swelling, erythema, and exudate, with synthetic labels generated using GPT-4o-mini. The classifier achieves 97.1% accuracy and an ROC-AUC of 0.993, with an inference time of 13.8 ms and a model size of 28 million parameters; these results demonstrate suitability for deployment in resource-constrained settings. As a proof-of-concept, this work illustrates the potential of AI-assisted diagnostics to improve healthcare access and could benefit similar research efforts that support clinical decision-making in remote and underserved regions.<\/jats:p>","DOI":"10.3390\/s25185636","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T10:27:12Z","timestamp":1757500032000},"page":"5636","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards AI-Based Strep Throat Detection and Interpretation for Remote Australian Indigenous Communities"],"prefix":"10.3390","volume":"25","author":[{"given":"Prasanna","family":"Asokan","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematics Sciences, Curtin University, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh Thu","family":"Truong","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematics Sciences, Curtin University, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4006-7803","authenticated-orcid":false,"given":"Duc Son","family":"Pham","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematics Sciences, Curtin University, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4949-7647","authenticated-orcid":false,"given":"Kit Yan","family":"Chan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematics Sciences, Curtin University, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-8221","authenticated-orcid":false,"given":"Susannah","family":"Soon","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing and Mathematics Sciences, Curtin University, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0681-1707","authenticated-orcid":false,"given":"Andrew","family":"Maiorana","sequence":"additional","affiliation":[{"name":"Curtin School of Allied Health, Curtin University, Perth, WA 6102, Australia"},{"name":"Exercise Physiology Department, Fiona Stanley Hospital, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cate","family":"Hollingsworth","sequence":"additional","affiliation":[{"name":"School of Molecular and Life Sciences, Curtin University, Perth, WA 6102, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wiegele, S., McKinnon, E., van Schaijik, B., Enkel, S., Noonan, K., Bowen, A.C., and Wyber, R. (2023). The epidemiology of superficial Streptococcal A (impetigo and pharyngitis) infections in Australia: A systematic review. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0288016"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Australian Institute of Health and Welfare (2021). Acute Rheumatic Fever and Rheumatic Heart Disease in Australia, 2016\u20132020, AIHW.","DOI":"10.1016\/B978-0-323-63982-8.00010-6"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nolan-Isles, D., Macniven, R., Hunter, K., Gwynn, J., Lincoln, M., Moir, R., Dimitropoulos, Y., Taylor, D., Agius, T., and Finlayson, H. (2021). Enablers and barriers to accessing healthcare services for Aboriginal people in New South Wales, Australia. Int. J. Environ. Res. Public Health, 18.","DOI":"10.20944\/preprints202102.0473.v1"},{"key":"ref_4","unstructured":"American Medical Association (2019). 2019 AMA Rural Health Issues Survey, AMA."},{"key":"ref_5","unstructured":"Asokan, P. (2024). The Role of AI in Remote Indigenous Healthcare: Detection and Annotation of Strep Throat. [Bachelor\u2019s Thesis, Curtin University]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yoo, T.K., Choi, J.Y., Jang, Y., Oh, E., and Ryu, I.H. (2020). Toward automated severe pharyngitis detection with smartphone camera using deep learning networks. Comput. Biol. Med., 125.","DOI":"10.1016\/j.compbiomed.2020.103980"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, Y., Lang, S., and Gong, Y. (2023). Swin-Transformer-Based YOLOv5 for Small-Object Detection in Remote Sensing Images. Sensors, 23.","DOI":"10.3390\/s23073634"},{"key":"ref_9","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_10","unstructured":"Li, J., Li, D., Savarese, S., and Hoi, S. (2023, January 17\u201324). BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada."},{"key":"ref_11","unstructured":"Ashurst, J.V., and Edgerley-Gibb, L. (2018). Streptococcal pharyngitis. StatPearls [Internet], StatPearls Publishing."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yeoh, D.K., Anderson, A., Cleland, G., and Bowen, A.C. (2017). Are scabies and impetigo \u201cnormalised\u201d? A cross-sectional comparative study of hospitalised children in northern Australia assessing clinical recognition and treatment of skin infections. PLoS Neglected Trop. Dis., 11.","DOI":"10.1371\/journal.pntd.0005726"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1221932","DOI":"10.3389\/fsurg.2023.1221932","article-title":"Treatment of recurrent acute tonsillitis\u2014A systematic review and clinical practice recommendations","volume":"10","author":"Ronen","year":"2023","journal-title":"Front. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e245","DOI":"10.3399\/bjgp20X708833","article-title":"Comparison of Centor and McIsaac scores in primary care: A meta-analysis over multiple thresholds","volume":"70","author":"Willis","year":"2020","journal-title":"Br. J. Gen. Pract."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1186\/s40001-025-02603-6","article-title":"An analytic research and review of the literature on practice of artificial intelligence in healthcare","volume":"30","author":"Mizna","year":"2025","journal-title":"Eur. J. Med Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"130","DOI":"10.5539\/ies.v9n7p130","article-title":"Will medical technology deskill doctors?","volume":"9","author":"Lu","year":"2016","journal-title":"Int. Educ. Stud."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1001\/jama.2023.22295","article-title":"Measuring the impact of AI in the diagnosis of hospitalized patients: A randomized clinical vignette survey study","volume":"330","author":"Jabbour","year":"2023","journal-title":"JAMA"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Askarian, B., Yoo, S.C., and Chong, J.W. (2019). Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone. Sensors, 19.","DOI":"10.3390\/s19153307"},{"key":"ref_20","unstructured":"Tobias, R.R., De Jesus, L.C., Mital, M.E., Lauguico, S., Bandala, A., Vicerra, R., and Dadios, E. (2019, January 12\u201314). Throat Detection and Health Classification Using Neural Network. Proceedings of the 2019 International Conference on Contemporary Computing and Informatics (IC3I), Singapore."},{"key":"ref_21","first-page":"127","article-title":"Throat Infection Detection Using Deep Learning","volume":"12","author":"Vamsi","year":"2022","journal-title":"UGC Care Group I J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.4258\/hir.2024.30.1.42","article-title":"Deep Learning Model and its Application for the Diagnosis of Exudative Pharyngitis","volume":"30","author":"Chng","year":"2024","journal-title":"Healthc. Inform. Res."},{"key":"ref_23","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the International Conference on Learning Representations (ICLR), Virtual Event."},{"key":"ref_24","unstructured":"Dai, Z., Liu, H., Le, Q.V., and Tan, M. (2021). CoAtNet: Marrying Convolution and Attention for All Data Sizes. arXiv."},{"key":"ref_25","unstructured":"Li, L.H., Yatskar, M., Yin, D., Hsieh, C.J., and Chang, K.W. (2019). VisualBERT: A simple and performant baseline for vision and language. arXiv."},{"key":"ref_26","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning transferable visual models from natural language supervision. Proceedings of the International Conference on Machine Learning, PMLR, Virtual Event."},{"key":"ref_27","first-page":"23716","article-title":"Flamingo: A visual language model for few-shot learning","volume":"35","author":"Alayrac","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, L., Wang, W., and Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE international Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cambay, V.Y., Barua, P.D., Baig, A.H., Dogan, S., Baygin, M., Tuncer, T., and Acharya, U.R. (2024). Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images. Sensors, 24.","DOI":"10.3390\/s24237710"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huang, Y., Liang, S., Cui, T., Mu, X., Luo, T., Wang, S., and Wu, G. (2024). Edge Computing and Fault Diagnosis of Rotating Machinery Based on MobileNet in Wireless Sensor Networks for Mechanical Vibration. Sensors, 24.","DOI":"10.3390\/s24165156"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Perez, H., Tah, J.H.M., and Mosavi, A. (2019). Deep Learning for Detecting Building Defects Using Convolutional Neural Networks. Sensors, 19.","DOI":"10.20944\/preprints201908.0068.v1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Vo, M., Hyatt, J., and Wang, Z. (2025). AI-Powered Visual Sensors and Sensing: Where We Are and Where We Are Going. Sensors, 25.","DOI":"10.3390\/s25061758"},{"key":"ref_34","unstructured":"Smith, L.N. (2015). Cyclical learning rates for training neural networks. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1007\/s00330-024-11035-5","article-title":"Assessing GPT-4 multimodal performance in radiological image analysis","volume":"35","author":"Brin","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_36","unstructured":"Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., and Lin, X.V. (2022). Opt: Open pre-trained transformer language models. arXiv."},{"key":"ref_37","unstructured":"Reid, M., Savinov, N., Teplyashin, D., Lepikhin, D., Lillicrap, T., Alayrac, J.b., Soricut, R., Lazaridou, A., Firat, O., and Schrittwieser, J. (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5636\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:43:09Z","timestamp":1760035389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5636"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,10]]},"references-count":37,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25185636"],"URL":"https:\/\/doi.org\/10.3390\/s25185636","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,10]]}}}