# You Only Look Once (YOLO) based machine learning algorithm for real-time detection of loop-mediated isothermal amplification (LAMP) diagnostics

## TL;DR

This paper introduces a machine learning algorithm using YOLOv8 to automatically detect and classify LAMP diagnostic results in real-time, improving accuracy and reducing errors.

## Contribution

The novel contribution is applying the YOLOv8 object detection algorithm for automated classification of LAMP diagnostics with high accuracy.

## Key findings

- The trained model achieved 97.4% overall accuracy in classifying LAMP images as positive or negative.
- The model demonstrated 95.3% precision and 96.8% recall for positive cases and 93.3% precision and 95.8% recall for negative cases.

## Abstract

Loop-mediated isothermal amplification (LAMP) is a widely used rapid and affordable molecular DNA amplification method with minimal resource requirements. However, visual interpretation of results is subjective and prone to errors, leading to potential false-positive and negative results. To address this limitation, a machine-learning approach is proposed for automated LAMP classification based on digital images. The approach utilizes You Only Look Once (YOLOv8), a fast and robust object detection algorithm to locate and classify tubes within LAMP images, enabling automated categorization as positive or negative. The trained model achieved a high overall accuracy of 97.4% in classifying LAMP images into positive or negative on the test set. Additionally, the approach had a 95.3% precision and 96.8% recall for positive cases and 93.3% precision and 95.8% recall for negative cases, demonstrating its potential for real-time LAMP diagnosis and enhanced assay performance. This project demonstrated platform suitability for real-time testing, offering an easy operation and rapid results.