Literature DB >> 23620327

Does the use of automated fetal biometry improve clinical work flow efficiency?

Jimmy Espinoza1, Sara Good, Evie Russell, Wesley Lee.   

Abstract

This study was designed to compare the work flow efficiency of manual measurements of 5 fetal parameters with a novel technique that automatically measures these parameters from 2-dimensional sonograms. This prospective study included 200 singleton pregnancies between 15 and 40 weeks' gestation. Patients were randomly allocated to either manual (n = 100) or automatic (n = 100) fetal biometry. The automatic measurement was performed using a commercially available software application. A digital video recorder captured all on-screen activity associated with the sonographic examination. The examination time and number of steps required to obtain fetal measurements were compared between manual and automatic methods. The mean time required to obtain the biometric measurements was significantly shorter using the automated technique than the manual approach (P < .001 for all comparisons). Similarly, the mean number of steps required to perform these measurements was significantly fewer with automatic measurements compared to the manual technique (P < .001). In summary, automated biometry reduced the examination time required for standard fetal measurements. This approach may improve work flow efficiency in busy obstetric sonography practices.

Entities:  

Mesh:

Year:  2013        PMID: 23620327     DOI: 10.7863/ultra.32.5.847

Source DB:  PubMed          Journal:  J Ultrasound Med        ISSN: 0278-4297            Impact factor:   2.153


  5 in total

1.  Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image.

Authors:  Lei Zhang; Nicholas J Dudley; Tryphon Lambrou; Nigel Allinson; Xujiong Ye
Journal:  J Med Imaging (Bellingham)       Date:  2017-04-17

Review 2.  Automated Techniques for the Interpretation of Fetal Abnormalities: A Review.

Authors:  Vidhi Rawat; Alok Jain; Vibhakar Shrimali
Journal:  Appl Bionics Biomech       Date:  2018-06-10       Impact factor: 1.781

3.  Optimization of Fetal Biometry With 3D Ultrasound and Image Recognition (EPICEA): protocol for a prospective cross-sectional study.

Authors:  Gaëlle Ambroise Grandjean; Gabriela Hossu; Claire Banasiak; Cybele Ciofolo-Veit; Caroline Raynaud; Laurence Rouet; Olivier Morel; Marine Beaumont
Journal:  BMJ Open       Date:  2019-12-15       Impact factor: 2.692

4.  Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency.

Authors:  Jonghyon Yi; Ho Kyung Kang; Jae-Hyun Kwon; Kang-Sik Kim; Moon Ho Park; Yeong Kyeong Seong; Dong Woo Kim; Byungeun Ahn; Kilsu Ha; Jinyong Lee; Zaegyoo Hah; Won-Chul Bang
Journal:  Ultrasonography       Date:  2020-09-14

5.  Obstetric ultrasound: where are we and where are we going?

Authors:  Jacques S Abramowicz
Journal:  Ultrasonography       Date:  2020-08-25
  5 in total

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