Literature DB >> 33387982

Metrics used to evaluate obstetric ultrasound skills on simulators: A systematic review.

Maela Le Lous1, Margaux Klein2, Caroline Tesson2, Julien Berthelemy3, Vincent Lavoue4, Pierre Jannin3.   

Abstract

Obstetric ultrasound simulators are now used for training and evaluating OB/GYN students but there is a lack of literature about evaluation metrics in this setting. In this literature review, we searched MEDLINE and the COCHRANE database using the keywords: (Obstetric OR Fetal) AND (Sonography OR Ultrasound) AND Simulation. Of a total of 263 studies screened, we selected nine articles from the title and the abstract in PubMed, in the past 5 years. Two more article were added from bibliographies. A total of 11 articles were therefore included. from which nine articles were selected from the title and the abstract in PubMed. Two more articles were added from the bibliographies For each study, data about the type of simulation, and the metrics (qualitative or quantitative) used for assessment were collected. The selection of studies shows that evaluation criteria for ultrasound training were qualitative metrics (binary success/fail exercise ; dexterity quoted by an external observer ; Objective Structured Assessment of Ultrasound Skills (OSAUS) Score ; quality of images according to Salomon's score) or quantitative criteria (Accuracy of Biometry - Simulator generated metrics). Most studies used a combination of both. To date, simulator metrics used to discriminate ultrasound skills are performance score quoted by external observers and image quality scoring. Whether probe trajectory metrics can be used to discriminate skills is unknown.
Copyright © 2020. Published by Elsevier B.V.

Entities:  

Keywords:  Education; Evaluation; Metrics; Obstetrics; Simulation training; Ultrasound

Mesh:

Year:  2020        PMID: 33387982     DOI: 10.1016/j.ejogrb.2020.12.034

Source DB:  PubMed          Journal:  Eur J Obstet Gynecol Reprod Biol        ISSN: 0301-2115            Impact factor:   2.435


  1 in total

1.  Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression.

Authors:  Yaosheng Lu; Dengjiang Zhi; Minghong Zhou; Fan Lai; Gaowen Chen; Zhanhong Ou; Rongdan Zeng; Shun Long; Ruiyu Qiu; Mengqiang Zhou; Xiaosong Jiang; Huijin Wang; Jieyun Bai
Journal:  Comput Math Methods Med       Date:  2022-09-02       Impact factor: 2.809

  1 in total

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