Artificial Intelligence (AI) in Diagnosis, Preterm Birth
Conditions
Keywords
Preterm birth, Premature birth, Artificial intelligence, Deep learning, Image acquisition, Diagnostic accuracy, Cervical ultrasound, Prospective validation
Brief summary
This study prospectively evaluates whether the performance of an already-developed artificial intelligence (AI) model for predicting spontaneous preterm birth changes when cervical ultrasound images are obtained using different ultrasound image settings. The primary research question is whether the AI model performs differently across images acquired with different imaging settings.
Interventions
Acquisition of cervical ultrasound images with variation in image acquisition parameters.
Sponsors
Study design
Eligibility
Inclusion criteria
* Pregnant women aged ≥18 years * Attending routine second-trimester scan (and scheduled transvaginal cervical assessment per local protocol/workflow)
Exclusion criteria
* Absence of transvaginal cervical assessment at the second-trimester scan * Missing follow-up data on pregnancy outcome (gestational age at delivery) * Inadequate image quality or missing required cervical ultrasound image
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Spontaneous preterm birth <37+0 weeks | At delivery. | Birth \<37+0 weeks after spontaneous onset of labor (with or without preterm prelabor rupture of membranes \[PPROM\]), regardless of mode of delivery, and excluding medically indicated (iatrogenic) preterm births without spontaneous onset. |
Secondary
| Measure | Time frame |
|---|---|
| Spontaneous preterm birth <34+0 and <32+0 weeks | At delivery. |
Countries
Denmark
Contacts
Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark