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Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study

Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study

Initial manual annotations were performed by a single annotator who placed bounding boxes around the structures of interest using a VATIC backend [23] and a self-designed XML administrator web page on the data server desktop. The annotation tool was subsequently changed to CVAT [24] to increase functionality by including segmentation and point annotation with more attributes. Irrespective of the tool used, frames were not annotated where there was significant motion artifact.

Alice Self, Qingchao Chen, Bapu Koundinya Desiraju, Sumeet Dhariwal, Alexander D Gleed, Divyanshu Mishra, Ramachandran Thiruvengadam, Varun Chandramohan, Rachel Craik, Elizabeth Wilden, Ashok Khurana, The CALOPUS Study Group, Shinjini Bhatnagar, Aris T Papageorghiou, J Alison Noble

JMIR Res Protoc 2022;11(9):e37374

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