Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiop...Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.展开更多
基金supported by a fund from the International Development Research Centre(IDRC)(Grant No.109981e001)funded by Canada’s International Development Research Centre(IDRC)(Grant No.109981-001)+3 种基金support from IDRC and UK's Foreign,Commonwealth and Development Office(FCDO)(Grant No.110554-001)support from NSERC Discovery Grant(Grant No.RGPIN-2022-04559)NSERC Discovery Launch Supplement(Grant No:DGECR-2022-00454)New Frontier in Research Fund-Exploratory(Grant No.NFRFE-2021-00879).
文摘Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.
