Revolutionizing Prenatal Care: AI-Assisted Detection of Fetal Intracranial Malformations

This groundbreaking study represents a pivotal step in advancing prenatal diagnosis capabilities. A total of 709 fetal neurosonographic images and videos were assessed interactively by 36 sonologists with varying levels of expertise in three distinct reading modes:

Unassisted Mode: Sonologists evaluated the images without the assistance of PAICS.

Concurrent Mode: PAICS was employed at the outset of the assessment.

Second Mode: Sonologists conducted an initial unaided interpretation, followed by PAICS assistance.

The results are truly transformative. With the support of PAICS, sonologists witnessed a significant enhancement in their diagnostic accuracy:

Unassisted Mode: 73% accuracy.

Concurrent Mode: 80% accuracy

Second Mode: 82% accuracy

In summary, the Prenatal Ultrasound Diagnosis Artificial Intelligence Conduct System (PAICS) demonstrates immense promise in elevating sonologists' capabilities for detecting fetal intracranial malformations from neurosonographic data.

Ref: Lin, M., Zhou, Q., Lei, T. et al. Deep learning system improved detection efficacy of fetal intracranial malformations in a randomized controlled trial. npj Digit. Med. 6, 191 (2023). https://doi.org/10.1038/s41746-023-00932-6