Deep learning model meets community-based surveillance of acute flaccid paralysis

Abstract

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.

Keywords: Acute flaccid paralysis; Community; Computer vision; Deep learning model; Surveillance; Transfer learning.

Fig. 1

A unified automated platform for community-based AFP surveillance.

Fig. 2

Sample suspected AFP case images from the dataset.

Fig. 3

Locations of the dataset collection points.

Fig. 4

The proposed vision transformer-based transfer learning model architecture. MLP, multi-layer perceptron; F, flatten; BN, batch normalization; D, dense layer.

Fig. 5

Performance of vision transformer models trained from scratch compared against transfer learning-based vision transformers on AFP dataset.

Fig. 6

Performance of the transfer learning method using different convolutional neural network and vision transformer architectures.

Fig. 7

Computational cost analysis of the different models used in implementing the proposed approach.

Fig. 8

Comparison of the statistical significance of the proposed method in terms of area under the receiver operating curve (ROC) (AUC). ∗∗∗ represents statistical significance value of P < 0.001. TL-CNN, convolutional neural network-based transfer learning, TL-ViT, vision transformer-based transfer learning, Scratch-ViT, vision transformer model trained from scratch. Error bars represent standard deviation of AUCs.