Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound

Chun Kit Wong; Mary Ngo; Manxi Lin; Zahra Bashir; Amihai Heen; Morten Bo Søndergaard Svendsen; Martin Grønnebæk Tolsgaard; Anders Nymark Christensen; Aasa Feragen

arXiv · 2404.00032

Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound

Chun Kit Wong·Mary Ngo·Manxi Lin·Zahra Bashir·Amihai Heen·Morten Bo Søndergaard Svendsen·Martin Grønnebæk Tolsgaard·Anders Nymark Christensen·Aasa Feragen

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Abstract

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.

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Chun Kit Wong, Mary Ngo, Manxi Lin, Zahra Bashir, Amihai Heen, Morten Bo Søndergaard Svendsen, Martin Grønnebæk Tolsgaard, Anders Nymark Christensen, Aasa Feragen. 2024-03-22. Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound. https://arxiv.org/abs/2404.00032

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