Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
This addresses the gap in real-world deployment of AI models for medical practitioners in obstetric ultrasound, though it is incremental as it focuses on a case study rather than a novel breakthrough.
The authors tackled the limited real-world validation of AI models in medical imaging by deploying a generic framework for fetal ultrasound standard plane detection, evaluating it with novice and expert users in clinical settings, and found that while it offers potential benefits, navigational guidance needs improvement.
Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users. Feedback from these sessions revealed that while the model offers potential benefits to medical practitioners, the need for navigational guidance was identified as a key area for improvement. These findings underscore the importance of early deployment of AI models in real-world settings, leading to insights that can guide the refinement of the model and system based on actual user feedback.