Constarium
← Search

Data · dataset · 2022

Organ-specific learning curves of sonographers performing first-trimester anatomical screening and impact of score-based evaluation on ultrasound image quality

Listed in IISH Dataverse

Introduction: First-trimester anatomical screening (FTAS) by ultrasound has been introduced in many countries as screening for aneuploidies, but also as early screening for fetal structural abnormalities.

Description

While a lot of emphasis has been put on the detection rates of FTAS, little is known about the performance of quality control programs and the sonographers’ learning curve for FTAS. The aims of the study were to evaluate the performance of a score-based quality control system for the FTAS and to assess the learning curves of sonographers by evaluating the images of the anatomical planes that were part of the FTAS protocol.

Methods

Read the rest (5 more)

Between 2012-2015, pregnant women opting for the combined test in the North-Netherlands were also invited to participate in a prospective cohort study extending the ultrasound investigation to include a first-trimester ultrasound performed according to a protocol. All anatomical planes included in the protocol were documented by pictures stored for each examination in logbooks. The logbooks of six sonographers were independently assessed by two fetal medicine experts.

For each sonographer, logbooks of examination 25-50-75 and 100 plus four additional randomly selected logbooks were scored for correct visualization of 12 organ-system planes. A plane specific score of at least 70% was considered sufficient. The intra-class correlation coefficient (ICC), was used to measure inter-assessor agreement for the cut-off scores.

Organ-specific learning curves were defined by single-cumulative sum (CUSUM) analysis. Results: Sixty-four logbooks were assessed. Mean duration of the scan was 22 ± 6 minutes and mean gestational age was 12+6 weeks.

In total 57% of the logbooks graded as sufficient. Most sufficient scores were obtained for the fetal skull (88%) and brain (70%), while the lowest scores were for the face (29%) and spine (38%). Five sonographers showed a learning curve for the skull and the stomach, four for the brain and limbs, three for the bladder and kidneys, two for the diaphragm and abdominal wall and one for the heart and spine and none for the face and neck.

Conclusion: Learning curves for FTAS differ per organ system and per sonographer. Although score-based evaluation can validly assess image quality, more dynamic approaches may better reflect clinical performance.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Heart 75% · Image 75% · Longitudinal study 65% · Reproductive medicine 68%
Provenance · 1 source records, 13 field assertions
SourceKeyLast seenRaw
IISH Dataversedoi:10.34894/EHOUTK10 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[anatomy].local:anatomy:heartenrichment · datasets iisg amsterdamkeyword-concept-rules@1.0.0title+description (75%)
concepts[field].anzsrc:group:3215enrichment · datasets iisg amsterdamtaxonomy-embedding@1.1.0title+keywords+description (68%)
concepts[field].dataverse_subject:medicine-health-and-life-sciencessource · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0/subjects
concepts[field].local:field:life-sciencesmapping · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0/subjects
concepts[field].local:field:medicine-healthmapping · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0/subjects
concepts[method].local:method:longitudinal-studyenrichment · datasets iisg amsterdamkeyword-concept-rules@1.0.0title+description (65%)
concepts[modality].local:modality:imageenrichment · datasets iisg amsterdamkeyword-concept-rules@1.0.0title+description (75%)
created_datesource · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0
descriptionsource · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0/description
publication_datesource · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0
titlesource · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0/name
updated_datesource · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0
version_labelsource · datasets iisg amsterdamconnector:datasets_iisg_amsterdam@1.0.0