AI system for fetal ultrasound in low-resource settings

• URL: http://arxiv.org/abs/2203.10139v1
• Date: Fri, 18 Mar 2022 19:39:34 GMT
• Title: AI system for fetal ultrasound in low-resource settings
• Authors: Ryan G. Gomes, Bellington Vwalika, Chace Lee, Angelica Willis, Marcin Sieniek, Joan T. Price, Christina Chen, Margaret P. Kasaro, James A. Taylor, Elizabeth M. Stringer, Scott Mayer McKinney, Ntazana Sindano, George E. Dahl, William Goodnight III, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T Saensuksopa, Kris Liu, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty
• Abstract summary: We developed and validated an artificial intelligence system that uses novice-acquired "blind sweep" ultrasound videos to estimate gestational age (GA) and fetal malpresentation. Our AI models have the potential to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings.
• Score: 6.601152168099057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption. We developed and validated an artificial intelligence (AI) system that uses novice-acquired "blind sweep" ultrasound videos to estimate gestational age (GA) and fetal malpresentation. We further addressed obstacles that may be encountered in low-resourced settings. Using a simplified sweep protocol with real-time AI feedback on sweep quality, we have demonstrated the generalization of model performance to minimally trained novice ultrasound operators using low cost ultrasound devices with on-device AI integration. The GA model was non-inferior to standard fetal biometry estimates with as few as two sweeps, and the fetal malpresentation model had high AUC-ROCs across operators and devices. Our AI models have the potential to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings.

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