Association of maternal obesity with growth of fetal fractional limb volume.

BACKGROUND: Maternal obesity influences birth weight and newborn adiposity. Fetal fractional limb volume has recently been introduced as a useful parameter for the proxy of fetal adiposity. However, the association between maternal adiposity and the growth of fetal fractional limb volume has not been examined. AIMS: To investigate the association of maternal pre-pregnancy BMI with the growth of fetal fractional limb volume. STUDY DESIGN: Prospective cohort study. SUBJECTS: Women with singleton uncomplicated pregnancies enrolled between July 2017 and June 2020. OUTCOME MEASURES: Fetal fractional limb volume was assessed between 20 and 40 weeks' gestation, measured as cylindrical limb volume based on 50 % of the total diaphysis length. The measured fractional limb volume at each gestational week were converted to z-scores based on a previous report. The association between pre-pregnancy BMI and fetal fractional limb volume was examined. Maternal age, parity, gestational weight gain and fetal sex were considered as potential confounding variables. RESULTS: Ultrasound scans of 455 fractional arm volume and thigh volume were obtained. Fractional limb volume increased linearly until the second trimester of gestation, then increased exponentially in the third trimester. Maternal pre-pregnancy BMI was significantly correlated with z-scores of fractional arm volume and thigh volume across gestation. The post-hoc analysis showed the association between pre-pregnancy BMI and fractional arm volume was significant especially between 34 and 40 weeks. CONCLUSIONS: Maternal obesity influences the growth pattern of fetal fractional limb volume. Fractional arm volume may potentially provide a useful surrogate marker of fetal nutritional status in late gestation.

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.