Artificial intelligence (AI), particularly deep learning (DL), has found growing applications in the sphere of medical imaging and healthcare. A considerable portion of the research related to DL has focused on retrospectively assessing model performance using validated datasets with known ground-truth labels. Few studies have taken the following step to research how DL assistance influences the diagnostic abilities of sonologists, and even fewer have explored probably the most effective ways during which DL can assist in clinical diagnosis.

In the present study, a multi-reader, crossover randomized controlled trial (RCT) was conducted, involving the recruitment of 36 sonologists. They were tasked with interpreting fetal neurosonographic images and videos each without the help of the PAICS system and with the help of PAICS in two different modes. The first objective was to evaluate the effectiveness of PAICS in supporting the diagnosis of fetal intracranial malformations and to check it with other auxiliary diagnostic methods.

The findings of this research highlight that the 2 image and video reading modes, augmented by the deep learning capabilities of the PAICS system, substantially enhance the accuracy of CNS malformation classification. This means that the system holds significant promise in enhancing the diagnostic performance of sonologists with regards to detecting fetal intracranial malformations.

Throughout the course of the research, a complete of 734 fetuses with abnormal intracranial findings and 19,709 normal fetuses underwent scanning. Nevertheless, 254 fetuses with abnormal findings and 19,631 normal fetuses were excluded as a result of issues like image quality or redundancy. Ultimately, 709 original images and videos (549 images and 160 videos) from 558 fetuses met the inclusion criteria and were included within the study.

The trial findings suggest that PAICS has the potential to boost the diagnostic performance of sonologists in identifying fetal intracranial malformations from neurosonographic data, whether utilized concurrently or in a secondary mode. It’s price noting that the concurrent use of PAICS proved to be simpler for all readers. Further research in real clinical settings, with a bigger variety of cases, is warranted to thoroughly assess the help provided by PAICS within the detection of congenital intracranial malformations.

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