RESUMO
OBJECTIVE: To establish reference ranges of fetal intracranial markers during the first trimester and develop the first novel artificial intelligence (AI) model to measure key markers automatically. METHODS: This retrospective study used two-dimensional (2D) ultrasound images from 4233 singleton normal fetuses scanned at 11+0-13+6 weeks of gestation at the Affiliated Suzhou Hospital of Nanjing Medical University from January 2018 to July 2022. We analyzed 10 key markers in three important planes of the fetal head. Based on these, reference ranges of 10 fetal intracranial markers were established and an AI model was developed for automated marker measurement. AI and manual measurements were compared to evaluate differences, correlations, consistency, and time consumption based on mean error, Pearson correlation analysis, intraclass correlation coefficients (ICCs), and average measurement time. RESULTS: The results of AI and manual methods had strong consistency and correlation (all ICC values >0.75, all r values >0.75, and all P values <0.001). The average absolute error of both only ranged from 0.124 to 0.178 mm. AI achieved a 100% detection rate for abnormal cases. Additionally, the average measurement time of AI was only 0.49 s, which was more than 65 times faster than the manual measurement method. CONCLUSION: The present study first established the normal standard reference ranges of fetal intracranial markers based on a large Chinese population data set. Furthermore, the proposed AI model demonstrated its capability to measure multiple fetal intracranial markers automatically, serving as a highly effective tool to streamline sonographer tasks and mitigate manual measurement errors, which can be generalized to first-trimester scanning.
RESUMO
OBJECTIVE: To develop an algorithm for the automated localization and measurement of levator hiatus (LH) dimensions (AI-LH) using 3-D pelvic floor ultrasound. METHODS: The AI-LH included a 3-D plane regression model and a 2-D segmentation model, which first achieved automated localization of the minimal LH dimension plane (C-plane) and measurement of the hiatal area (HA) on maximum Valsalva on the rendered LH images, but not on the C-plane. The dataset included 600 volumetric data. We compared AI-LH with sonographer difference (ASD) as well as the inter-sonographer differences (IESD) in the testing dataset (n = 240). The assessment encompassed the mean absolute error (MAE) for the angle and center point distance of the C-plane, along with the Dice coefficient, MAE, and intra-class correlation coefficient (ICC) for HA, and included the time consumption. RESULTS: The MAE of the C-plane of ASD was 4.81 ± 2.47° with 1.92 ± 1.54 mm. AI-LH achieved a mean Dice coefficient of 0.93 for LH segmentation. The MAE on HA of ASD (1.44 ± 1.12 mm²) was lower than that of IESD (1.63 ± 1.58 mm²). The ICC on HA of ASD (0.964) was higher than that of IESD (0.949). The average time costs of AI-LH and manual measurement were 2.00 ± 0.22 s and 59.60 ± 2.63 s (t = 18.87, p < 0.01), respectively. CONCLUSION: AI-LH is accurate, reliable, and robust in the localization and measurement of LH dimensions, which can shorten the time cost, simplify the operation process, and have good value in clinical applications.
