Web-Based AI Assistant for Medical Imaging: A Case Study on Predicting Spontaneous Preterm Birth via Ultrasound Images

The potential for artificial intelligence (AI) in analyzing medical images is vast and promises significant future advancements. It brings opportunities for community and remote-area hospitals to be equipped with professional capabilities once exclusive to top-tier medical institutions. However, applying in-lab AI methods to real-world applications of medical imaging is challenging due to the complexity of gathering training datasets as well as the need for intricate systems and specialized devices. In this paper, we demonstrate how the web platform could benefit the application of AI methods in medical imaging based on the lightweight design, cross-platform portability, streamlined distribution and deployment of the web. Specifically, we design and implement a web-based assistant for predicting spontaneous preterm births via ultrasound images. During the development phase, we leverage crowdsourcing on the web to annotate ultrasound images and gather domain-specific features to train the AI model for predicting spontaneous preterm birth. During the deployment phase, we employ WebAR to present AI-assisted diagnostic insights for physicians. Evaluation results show that our system achieves an AUC of 0.769, nearing the diagnostic proficiency of top-tier physicians. Besides, our WebAR system exhibits only 527.2–1754.2 ms latency, enabling effective assisted diagnosis.