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Use of AI-driven ultrasound systems to improve diagnostic accuracy in prenatal medicine - ADINOPRE study

Use of AI-driven ultrasound systems to improve diagnostic accuracy in prenatal medicine - ADINOPRE study - ADINOPRE

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
DRKS
Registry ID
DRKS00039350
Enrollment
100
Registered
2026-02-18
Start date
2026-02-05
Completion date
Unknown
Last updated
2026-06-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Z34

Interventions

Group 1: Arm 1 – Sequence: Manual ? AI-assisted
ultrasound examination by a standard examiner (scan A: manual, then AI-assisted) and by an expert examiner (scan B: manual, then AI-assisted). Independent image evaluation by a DEGUM II/III expert. Pa
Ultrasound examination by a standard examiner (scan A: AI-assisted, followed by manual) and by an expert examiner (scan B: AI-assisted, followed by manual). Independent image evaluation by a DEGUM II/

Sponsors

Department für Frauengesundheit
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: - Singleton pregnancy - Gestational age 19+0 – 23+6 weeks (crown–rump length or reliable dating) - Written informed consent

Exclusion criteria

Exclusion criteria: - Multiple pregnancy - Intra uterine fetal demise - Maternal condition precluding adequate scan (e.g. critical illness, abdominal wall infection) Inability to give consent (language barrier, cognitive impairment)

Design outcomes

Primary

MeasureTime frame
Determining whether standard-level physicians and experienced physicians can reduce the duration of second-trimester ultrasound examinations by using AI tools (Live ViewAssist) for automated plane detection and automatic biometry compared to manual scanning.

Secondary

MeasureTime frame
To establish whether standard-level physicians and experienced physicians can improve the image quality of the second trimester ultrasound examination by using AI tools for an automated plane recognition and quality prompts compared to manual scanning. Target variables for the secondary objective: Quality of the ultrasound images with and without AI classified by an independent DEGUM II or III expert reviewer.

Countries

Germany

Contacts

Public ContactKarl Oliver Kagan

Department für Frauengesundheit

Oliver.Kagan@med.uni-tuebingen.de+497071 29-82211

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Jun 29, 2026