Effectiveness of transferring ultrasound deep learning models from adults to pediatrics for frame based pneumonia classification

Hand-held ultrasound devices are emerging as a promising intervention to aid in diagnosing deadly early childhood pneumonia in the developing world. Lung ultrasound (LUS) data, however, can be difficult to read and interpret accurately, and thus require trained professionals. A variety of deep learning (DL) models have been developed to aid professionals in this task, but the difficulty curating quality training datasets limits the generalization capabilities of these models. To combat this data scarcity, we utilized a variety of DL models trained on LUS data collected from adult COVID-19 patients in Italy and were able to apply transfer learning to increase pneumonia related imaging pattern classification in LUS data from children and infants in rural Zambia. In this regard, we found that transfer learning significantly increased frame classification F1 performance as compared to training models from scratch. These findings indicate the promising potential for developing generalizable AI for LUS classification in diverse contexts given limited data. This analysis demonstrates that DL models effectively transfer their capabilities when fine tuned on a new population demographics, allowing the possibility to clinically deploy these models without the need to acquire large amounts of new data.