Quality Control of Ultrasound Images During Early Pregnancy Via AI

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study 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.

Eligibility

Primary outcome measure(s)

• PR curve of image quality control module — one month
  Using Precision-Recall curve and mean average percision as evaluating indicator of image quality control model.

Trial sites (4)

More Chinese Academy of Sciences trials in China

Other trials for the same condition