Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep‐learning‐based analysis. (1st December 2022)

• Record Type: Journal Article
• Authors: Drukker, L.; Sharma, H.; Karim, J. N.; Droste, R.; Noble, J. A.; Papageorghiou, A. T.
• Is Part Of: Ultrasound in obstetrics & gynecology. Volume 60:Number 6(2022)
• Page Start: 759
• Page End: 765
• Publication Date: 2022-12-01
• Subjects: anatomy -- artificial intelligence -- automation -- big data -- clinical workflow -- computer vision -- data science -- deep learning -- image analysis -- machine learning -- neural network -- obstetrics -- pregnancy -- screening -- sonography -- ultrasound; Ultrasonics in obstetrics -- Periodicals; Generative organs, Female -- Diseases -- Diagnosis -- Periodicals; Diagnosis, Ultrasonic -- Periodicals; Genital Diseases, Female -- ultrasonography -- Periodicals; Ultrasonography, Prenatal -- Periodicals; 618.047543
• DOI: 10.1002/uog.24975
• Languages: English
• ISSNs: 0960-7692

Abstract

ABSTRACT: Objective: Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan‐acquisition order is not prescribed. Using deep‐learning‐based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second‐trimester anomaly scans. Methods: We collected prospectively full‐length video recordings of routine second‐trimester anomaly scans. Important scan events in the videos were identified by detecting automatically image freeze and image/clip save. The video immediately preceding and following the important event was extracted and labeled as one of 11 commonly acquired anatomical structures. We developed and used a purposely trained and tested deep‐learning annotation model to label automatically the large number of scan events. Thus, anomaly scans were partitioned as a sequence of anatomical planes or fetal structures obtained over time. Results: A total of 496 anomaly scans performed by 14 sonographers were available for analysis. UK guidelines specify that an image or videoclip of five different anatomical regions must be stored and these were detected in the majority of scans: head/brain was detected in 97.2% of scans, coronal face view (nose/lips) in 86.1%, abdomen in 93.1%, spine in 95.0% and femur in 92.3%. Analyzing the clinical workflow, we observed that sonographers were most likely to begin their scan by capturing the head/brain (in 24.4% of scans), spine (in 23.2%) or thorax/heart (in 22.8%). The most commonly identified two‐structure transitions were: placenta/amniotic fluid to maternal anatomy, occurring in 44.5% of scans; head/brain to coronal face (nose/lips) in 42.7%; abdomen to thorax/heart in 26.1%; and three‐dimensional/four‐dimensional face to sagittal face (profile) in 23.7%. Transitions between three or more consecutive structures in sequence were uncommon (up to 13% of scans). None of the captured anomaly scans shared an entirely identical sequence. Conclusions: We present a novel evaluation of the anomaly scan acquisition process using a deep‐learning‐based analysis of ultrasound video. We note wide variation in the number and sequence of structures obtained during routine second‐trimester anomaly scans. Overall, each anomaly scan was found to be unique in its scanning sequence, suggesting that sonographers take advantage of the fetal position and acquire the standard planes according to their visibility rather than following a strict acquisition order. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.