Toward a rapid, sensitive, user-friendly, field-deployable artificial intelligence tool for enhancing African swine...

African swine fever (ASF) is a lethal and highly contagious transboundary animal disease with the potential for rapid international spread. Lateral flow assays (LFAs) are sometimes hard to read by the inexperienced user, mainly due to the LFA sensitivity and reading ambiguities. Our objective was to develop and implement an AI-powered tool to enhance the accuracy of LFA reading, thereby improving rapid and early detection for ASF diagnosis and reporting.

Here, we focus on the development of a deep learning-assisted, smartphone-based AI diagnostic tool to provide accurate decisions with higher sensitivity. The tool employs state-of-the-art You Only Look Once (YOLO) models for image classification. The YOLO models were trained and evaluated using a dataset consisting of images where the lateral flow assays are manually labeled as positives or negatives. A prototype JavaScript website application for ASF reporting and visualization was created in Azure. The application maintains the distribution of the positive predictions on a map as the positive cases are submitted by users.

The performance of the models is evaluated using standard evaluation metrics for classification tasks, specifically accuracy, precision, recall, sensitivity, specificity, and F1 measure. We acquired 86.3 ± 7.9% average accuracy, 96.3 ± 2.04% average precision, 79 ± 13.20% average recall, and an average F1 score of 0.87 ± 0.088 across 3 different train/development/test splits of the datasets. Submitting a positive result of the deep learning model updates a map with a location marker for positive results.

Combining clinical data learning and 2-step algorithms enables a point-of-need assay with higher accuracy.

A rapid, sensitive, user-friendly, and deployable deep learning tool was developed for classifying LFA test images to enhance diagnosis and reporting, particularly in settings with limited laboratory resources.