Quality Control of Ultrasound Images During Early Pregnancy Via AI

Early Pregnancy Clinical Trial

Official title:

Deep Learning-based Quality Control of Ultrasound Images During Early Pregnancy

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06002412
• Other study ID #: CASMI005
• Status: Recruiting
• Start date: September 1, 2023
• Est. completion date: July 30, 2028

Study information

• Verified date: September 2023
• Source: Chinese Academy of Sciences
• Contact: Di Dong, Ph.D
• Phone: +86 13811833760
• Email: di.dong[@]ia.ac.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 400
• Est. completion date: July 30, 2028
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 20 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Early Pregnancy

Intervention

Other:
• Image quality control
The investigators identify the region of interest in the relevant section to give a conclusion on whether the image is standard or not, guiding clinicians to standardize the operation, and reducing the rate of misdiagnosis and underdiagnosis.

Locations

Country	Name	City	State
China	Beijing Obstetrics and Gynecology Hospital affiliated to Capital Medical University	Beijing
China	Peking University Third Hospital	Beijing
China	Changsha Hospital for Maternal and Child Health Care	Changsha
China	Second Xiangya Hospital of Central South University	Changsha

Sponsors (5)

Lead Sponsor	Collaborator
Chinese Academy of Sciences	Beijing Obstetrics and Gynecology Hospital, Changsha Hospital for Maternal and Child Health Care, Peking University Third Hospital, Second Xiangya Hospital of Central South University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	PR curve of image quality control module	Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model.	one month
Secondary	The accuracy of intelligent analysis system in image quality control module	The agreement between the prediction outcome of intelligent analysis system and the golden standard	one month