Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.

BACKGROUND: Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images. METHODS: A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience. RESULTS: The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers. CONCLUSION: We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.

Effects of Aspirin in Rats With Ouabain Intracerebral Treatment-Possible Involvement of Inflammatory Modulation?

Bipolar disorder (BD) is a chronic and refractory disease with high probability of morbidity and mortality. Although epidemiological studies have established a strong association between BD and immune dysfunction, the precise etiology is still debatable, and the underpinning mechanism remains poorly investigated and understood. In the present study, manic-like symptoms of BD were induced in rats after intracerebroventricular administration of ouabain. Aspirin, a commonly used anti-inflammatory agent, was used to treat the induced manic-like symptoms and inflammation. Concentrations of a spectrum of inflammatory cytokines were examined by enzyme-linked immunosorbent assay in both plasma and brain tissues, and expression of Toll-like receptors 3 and 4 were determined in rat brains. Locomotor activity was monitored with open-field test to assess the effects of ouabain challenge and to evaluate the treatment efficacy of aspirin. Ouabain administration recapitulated many mania-like features such as increased stereotypic counts, traveling distance in open-field test, and decreased expression of brain-derived neurotrophic factor, interferon gamma, and Toll-like receptor 3, which were frequently found in patients with BD. These abnormalities could be partially reversed by aspirin. Our findings suggest that aspirin could be used as a promising adjunctive therapy for BD.