Clinical trial NCT06002412: Quality Control of Ultrasound Images During Early Pregnancy Via AI
Official title: Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Early Pregnancy
Keywords: Early Pregnancy
Ultrasound
Quality Control
Lead sponsor: Chinese Academy of Sciences
Sponsor class: OTHER_GOV
Collaborators: name: Beijing Obstetrics and Gynecology Hospital
name: Peking University Third Hospital
name: Changsha Hospital for Maternal and Child Health Care
name: Second Xiangya Hospital of Central South University
First posted: 2023-08-21
Enrollment: 400
Sex: FEMALE
Minimum age (years): 20
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Women in early pregnancy

Brief summary: This research integrates artificial intelligence to enhance early pregnancy ultrasonography quality control, focusing on specific fetal sections. In collaboration with prominent medical institutions, the investigators have amassed extensive fetal ultrasound data. The investigators aim to develop a deep learning model that can accurately identify essential anatomical areas in ultrasound images and evaluate their quality. This tool is expected to significantly decrease misdiagnoses of conditions like Down Syndrome and neural system deformities by ensuring real-time image quality assessment.

Detailed description: This research is dedicated to integrating artificial intelligence technology to optimize the quality control process of early pregnancy ultrasonography. The ultrasound images involved primarily focus on the median sagittal section, NT section, and choroid plexus of the fetus during early pregnancy. In this regard, the investigators have collaborated with renowned medical institutions such as Beijing Obstetrics and Gynecology Hospital, Peking University Third Hospital, Changsha Hospital for Maternal and Child Health Care, and Second Xiangya Hospital of Central South University to retrospectively and prospectively collect a vast amount of early pregnancy fetal ultrasound image data. Based on this, the investigators plan to establish a model rooted in deep learning. This model will be capable of precisely identifying key anatomical regions in standard ultrasound scan images. Furthermore, by recognizing these anatomical structures, the model will determine whether the ultrasound image meets the standard scanning quality. This model is anticipated to serve as a powerful auxiliary tool in obstetric ultrasonography, enabling real-time assessment of ultrasound image quality, thereby significantly reducing the rates of missed and misdiagnosed fetal diseases such as Down Syndrome and neural system malformations.

Eligibility criteria: Inclusion Criteria:

* Women in early pregnancy who have detailed personal information and ultrasound images.
* The ultrasound images should clearly show the fetus's median sagittal, NT, and choroid plexus views.

Exclusion Criteria:

* Ultrasound images from women in mid to late pregnancy.
* Ultrasound images that are unclear or blurry, making evaluation difficult.
* Women who did not provide complete personal and medical information during the ultrasound scan.

Primary outcomes:
measure: PR curve of image quality control module
timeFrame: one month
Secondary outcomes:
measure: The accuracy of intelligent analysis system in image quality control module
timeFrame: one month
Other outcomes:
Locations:
city: Beijing
country: China
facility: Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University
city: Beijing
country: China
facility: Peking University Third Hospital
city: Changsha
country: China
facility: Changsha Hospital for Maternal and Child Health Care
city: Changsha
country: China
facility: Second Xiangya Hospital of Central South University
Study officials:
References: