The PAICS deep learning system has shown promising results in enhancing the diagnostic performance of sonologists in detecting fetal intracranial malformations. A study involving 36 sonologists found that the system substantially improved the accuracy of CNS malformation classification. Further research in real clinical settings is needed to fully assess the system’s assistance in identifying congenital intracranial malformations.
Revolutionizing Prenatal Diagnosis: Check Out How the PAICS Deep Learning System Enhances Detection of Fetal Intracranial Malformations from Neurosonographic Images
Artificial intelligence (AI) and deep learning (DL) have made significant advancements in medical imaging and healthcare. In a recent study, the PAICS system, powered by deep learning, was tested to assess its effectiveness in supporting the diagnosis of fetal intracranial malformations. The study involved 36 sonologists who were tasked with interpreting neurosonographic images and videos both with and without the assistance of PAICS.
The findings of the research show that the deep learning capabilities of the PAICS system greatly improve the accuracy of CNS malformation classification. This suggests that PAICS has the potential to enhance the diagnostic performance of sonologists in detecting fetal intracranial malformations.
During the study, a total of 734 fetuses with abnormal intracranial findings and 19,709 normal fetuses were scanned. The inclusion criteria led to 709 original images and videos being included in the study.
The trial results indicate that PAICS can significantly improve the diagnostic performance of sonologists in identifying fetal intracranial malformations. Further research with a larger number of cases is needed to thoroughly evaluate the assistance provided by PAICS in detecting congenital intracranial malformations in real clinical settings.
To learn more about the research, you can check out the full paper.
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