Pristine Annotations-Based Multi-modal Trained Artificial Intelligence Solution to Triage Chest X-Ray for COVID-19

Tan, T.; Das, B.; Soni, R.; Fejes, M.; Ranjan, S.; Szabo, D. A.; Melapudi, V.; Shriram, K. S.; Agrawal, U.; Rusko, L.; Herczeg, Z.; Darazs, B.; Tegzes, P.; Ferenczi, L.; Mullick, R.; Avinash, G.

The COVID-19 pandemic continues to spread and impact the well-being of the global population. The front-line imaging modalities computed tomography (CT) and X-ray play an important role for triaging COVID-19 patients. Considering the limited access to resources (both hardware and trained personnel) and decontamination, CT may not be ideal for triaging suspected subjects. Artificial intelligence (AI) assisted X-ray based applications for triaging and monitoring COVID-19 patients in a timely manner with the additional ability to delineate the disease region boundary are seen as a promising solution. Our proposed solution differs from existing solutions by industry and academic communities. We demonstrates a functional AI model to triage by inferencing using a single x-ray image, while the AI model is trained using both X-ray and CT data. We report on how such a multi-modal training improves the solution compared to X-ray only training. The multi-modal solution increases the AUC (area under the receiver operating characteristic curve) from 0.89 to 0.93 for the classification between COVID-19 and non-COVID-19 cases. It also positively impacts the Dice coefficient (0.59 to 0.62) for segmenting the COVID-19 pathology. © 2021, Springer Nature Switzerland AG.