Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.

Objective: To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). Materials and Methods: One hundred routine-dose (RD) abdominal CT studies reconstructed using FBP were used to train the DLA. Simulated CT images were made at dose levels of 13%, 25%, and 50% of the RD (DLA-1, -2, and -3) and reconstructed using FBP. We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. LD CT images of the phantom and patients were processed using FBP, ADMIRE, and DLAs (LD-FBP, LD-ADMIRE, and LD-DLA images, respectively). To compare the image quality, we measured the noise power spectrum and modulation transfer function (MTF) of phantom images. For patient data, we measured the mean image noise and performed qualitative image analysis. We evaluated the presence of additional artifacts in the LD-DLA images. Results: LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE for both phantom and patient data (all p < 0.001). LD-DLAs trained with a lower radiation dose showed less image noise. However, the MTFs of the LD-DLAs were lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose. In the qualitative image analysis, the overall image quality of LD-DLAs was best for DLA-3 (50% simulated radiation dose) and not significantly different from LD-ADMIRE. There were no additional artifacts in LD-DLA images. Conclusion: DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. The DLA trained with 50% simulated radiation dose showed the best overall image quality.

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ObjectiveThe number of Korean women of childbearing age who drink alcohol and binge drink has increased remarkably in recent years. In the present study, we examined self-reported rates of alcohol use before and during pregnancy and identified maternal characteristics associated with drinking in pregnancy.MethodsOne thousand pregnant Korean women who visited the Department of Obstetrics and Gynecology (OB/GYN) completed a self-administered questionnaire that sought information on their demographic characteristics and incorporated features of the Alcohol Use Disorder Identification Test (AUDIT)-C to investigate their use of alcohol, including binge drinking, during three time periods ("in the year before this pregnancy," "during this pregnancy," and "in the previous 30 days").ResultsOf these participants, 16.4% reported using alcohol during their pregnancy, 12.2% had used alcohol in the previous 30 days, and 1.7% reported binge drinking during their pregnancy. In the year before pregnancy, 77.1% had used alcohol, and 22.3% had binge drunk. The group using any amount of any alcohol during pregnancy showed a lower educational level, a lower rate of planned pregnancy, a lower level of knowledge relating to the risks of drinking alcohol during pregnancy, and a higher frequency of alcohol drinking in the year before pregnancy when compared with the abstinent group. Low educational level and unplanned pregnancy were revealed to be significant risk factors for alcohol consumption in pregnant women.ConclusionThis is the first study to examine any alcohol and binge alcohol drinking during pregnancy in Korea. Clinical attention and monitoring system on alcohol use during pregnancy are necessary in Korea.