A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Statistics Based Neural Networks Method for Industrial Image Inspection

Automated industrial image inspection system has attracted a great deal of interest in recent years. In this thesis, a new method is presented by combining a statistics method with a neural networks method, which could reduce the interference of machine dynamics and improve the inspection accuracy. Different from the pixel-based or feature-based methods, the proposed method is based on two indices of an image, which are the variances of the rows and columns of the image. For image inspection, first neural networks are trained using these two indices from a set of good images in order to establish a tolerance zone. Then, the two indices of each inspection image are computed through trained neural networks and compared with the tolerance zone. A defective item is detected if either index falls out of the tolerance zone. The other contributions, such as two-point based image registration method and defect simulation algorithms, also help to improve the performance of inspection. Experimental results demonstrate that the proposed approach has a better performance in comparison with traditional statistics approach.

Statistics Based Neural Networks Method for Industrial Image Inspection

Automated industrial image inspection system has attracted a great deal of interest in recent years. In this thesis, a new method is presented by combining a statistics method with a neural networks method, which could reduce the interference of machine dynamics and improve the inspection accuracy. Different from the pixel-based or feature-based methods, the proposed method is based on two indices of an image, which are the variances of the rows and columns of the image. For image inspection, first neural networks are trained using these two indices from a set of good images in order to establish a tolerance zone. Then, the two indices of each inspection image are computed through trained neural networks and compared with the tolerance zone. A defective item is detected if either index falls out of the tolerance zone. The other contributions, such as two-point based image registration method and defect simulation algorithms, also help to improve the performance of inspection. Experimental results demonstrate that the proposed approach has a better performance in comparison with traditional statistics approach.

Should Adrenal Venous Sampling Be Performed in PA Patients Without Apparent Adrenal Tumors?

IntroductionSome aldosterone-producing micro-adenomas cannot be detected through image inspection. Therefore, adrenal venous sampling (AVS) is often performed, even in primary aldosteronism (PA) patients who have no apparent adrenal tumors (ATs) on imaging. In most of these cases, however, the PA is bilateral.ObjectiveTo clarify the clinical need for AVS in PA patients without apparent ATs, taking into consideration the rates of adrenalectomy.MethodsThis is a retrospective cross-sectional study assessing 1586 PA patients without apparent ATs in the multicenter Japan PA study (JPAS). We analyzed which parameters could be used to distinguish unilateral PA patients without apparent ATs from bilateral patients. We also analyzed the prevalences of adrenalectomy in unilateral PA patients.ResultsThe unilateral subtype without an apparent AT was diagnosed in 200 (12.6%) of 1586 PA patients. Being young and female with a short hypertension duration, normokalemia, low creatinine level, low plasma aldosterone concentration, and low aldosterone-to-renin ratio (ARR) was significantly more common in bilateral than unilateral PA patients. If PA patients without apparent ATs were female and normokalemic with a low ARR (<560 pg/ml per ng/ml/h), the rate of unilateral PA was only 5 (1.1%) out of 444. Moreover, 77 (38.5%) of the 200 did not receive adrenalectomy, despite being diagnosed with the unilateral subtype based on AVS.ConclusionThe low prevalence of the unilateral subtype in PA patients without apparent ATs suggests AVS is not indicated for all of these patients. AVS could be skipped in female normokalemic PA patients without apparent ATs if their ARRs are not high. However, AVS should be considered for male hypokalemic PA patients with high ARRs because the rates of the unilateral subtype are high in these patients.

Surface Defect Inspection in Images Using Statistical Patches Fusion and Deeply Learned Features

Defect detection in images is a challenging task due to the existence of tiny and noisy patterns on surface images. To tackle this challenge, a defect detection approach is proposed in this paper using statistical data fusion. First, the proposed approach breaks a large image that contains multiple separate defects into smaller overlapping patches to detect the existence of defects in each patch, using the conventional convolutional neural network approach. Then, a statistical data fusion approach is proposed to maintain the spatial coherence of cracks in the image and aggregate the information extracted from overlapping patches to enhance the overall performance and robustness of the system. The proposed approach is evaluated using three benchmark datasets to demonstrate its superior performance in terms of both individual patch inspection and the whole image inspection.