Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866911404683952128 |
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| author | Wong, Chun Kit Ngo, Mary Lin, Manxi Bashir, Zahra Heen, Amihai Svendsen, Morten Bo Søndergaard Tolsgaard, Martin Grønnebæk Christensen, Anders Nymark Feragen, Aasa |
| author_facet | Wong, Chun Kit Ngo, Mary Lin, Manxi Bashir, Zahra Heen, Amihai Svendsen, Morten Bo Søndergaard Tolsgaard, Martin Grønnebæk Christensen, Anders Nymark Feragen, Aasa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound Wong, Chun Kit Ngo, Mary Lin, Manxi Bashir, Zahra Heen, Amihai Svendsen, Morten Bo Søndergaard Tolsgaard, Martin Grønnebæk Christensen, Anders Nymark Feragen, Aasa Human-Computer Interaction Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2404.00032 |