A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

Gomes et al. develop machine learning models for gestational age and fetal malpresentation assessment on fetal ultrasound. The authors optimize their system for use in low-resource settings, using novice ultrasound operators, simplified imaging protocols, and low cost ultrasound devices.

Bibliographic Details
Main 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, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T. Saensuksopa, Kris Liu, Tiya Tiyasirichokchai, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty
Format: Article
Language:English
Published: Nature Portfolio 2022-10-01
Series:Communications Medicine
Online Access:https://doi.org/10.1038/s43856-022-00194-5

Internet

https://doi.org/10.1038/s43856-022-00194-5

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