AI detection of M. Tuberculosis pathogens using Generative Adversarial Network (GAN) analyses
Abstract Background Rapid identification of pathogens is critical to outbreak detection and sentinel surveillance; however most diagnoses are made in laboratory settings. Advancements in artificial intelligence (AI) and computer vision offer unprecedented opportunities to facilitate detection and reduce response time in field settings. An initial step is the creation of analysis algorithms for offline mobile computing applications. Methods AI models to identify objects using computer vision are typically “trained” on previously labeled images. The scarcity of labeled image-libraries creates a bottleneck, requiring thousands of labor hours to annotate images by hand to create “training data.” We describe the applicability of Generative Adversarial Network (GAN) methods to amass sufficient training data with minimal manual input. Results Our AI models are built with a performance score of 0.84-0.93 for M. Tuberculosis, a measure of the AI model's accuracy using precision and recall. Our results demonstrate that our GAN pipeline boosts model robustness and learnability of sparse open source data. Conclusions The use of labeled training data to identify M. Tuberculosis developed using our GAN pipeline techniques demonstrates the potential for rapid identification of known pathogens in field settings. Our work paves the way for the development of offline mobile computing applications to identify pathogens outside of a laboratory setting. Advancements in artificial intelligence (AI) and computer vision offer unprecedented opportunities to decrease detection time in field settings by combining these technologies. Further development of these capabilities can improve time-to-detection and outbreak response significantly. Key messages Rapidly deploy AI detectors to aid in disease outbreak and surveillance. Our concept aligns with deploying responsive alerting capabilities to address dynamic threats in low resource, offline computing environs.