Reliability of offline inter-recti distance measurement on ultrasound images captured by novice examiners

Research Square (Research Square)(2023)

Cited 0|Views7
No score
Abstract
Abstract Background: With the increased interest in inter-recti distance measurement using ultrasound imaging, there is a question of measurement reliability, and the importance of the examiner’s experience. Methods: The study aimed to investigate the reliability of the inter-recti distance measurement performed offline by an experienced radiologist on linea alba images captured by two novice examiners. Additionally, it was aimed to determinethe number of image repetitions that provide an acceptable measurement reliability level. Ultrasound images were acquired by two novice examiners on repeated occasions (sessions A and B) in twenty-eight nulliparous women of reproductive age. Five images were captured at supraumbilical, umbilical, and infraumbilical points during each session. Results: The excellent intra-examiner reliability of inter-recti distance measurements was shown at the supraumbilical and umbilical levels (ICC=0.894-0.983). Infraumbilical measurements had good to excellent reliability (ICC=0.894-0.972). Session A inter-examiner reliability was excellent for the mean measurements of two, three, four, and five images taken at each location (ICC=0.913-0.954). Session B inter-examiner reliability was excellent for the mean measurements of two, three, four, and five images taken at the supraumbilical and umbilical (ICC=0.94-0.98) and good (ICC≥0.81) at the infraumbilical locations. Some images were unusable (1.6% of images of Examiner 1 and 2.2% of Examiner 2). Conclusions: Novice examiners were able to capture good-quality ultrasound images of the linea alba that allowed for excellent intra- and inter-examiner reliability of supraumbilical and umbilical measurements, and good to excellent reliability of inframbilical measurements in nulliparas.
More
Translated text
Key words
ultrasound images,novice examiners,reliability,inter-recti
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined