Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI

A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner. All cases of CAD/CAM CR crowns were manufactured from April 2014 to November 2015 at the Division of Prosthodontics, Osaka University Dental Hospital (Ethical Review Board at Osaka University, approval H27-E11). The data set consisted of a total of 24 cases: 12 trouble-free and 12 debonding as known labels. A total of 8,640 images were randomly divided into 6,480 training and validation images and 2,160 test images. Deep learning with a CNN method was conducted to develop a learning model to predict the debonding probability. The prediction accuracy, precision, recall, F-measure, receiver operating characteristic, and area under the curve of the learning model were assessed for the test images. Also, the mean calculation time was measured during the prediction for the test images. The prediction accuracy, precision, recall, and F-measure values of deep learning with a CNN method for the prediction of the debonding probability were 98.5%, 97.0%, 100%, and 0.985, respectively. The mean calculation time was 2 ms/step for 2,160 test images. The area under the curve was 0.998. Artificial intelligence (AI) technology—that is, the deep learning with a CNN method established in this study—demonstrated considerably good performance in terms of predicting the debonding probability of a CAD/CAM CR crown with 3D stereolithography models of a die scanned from patients.Yamaguchi S., Lee C., Karaer O., et al. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. Journal of Dental Research, 98(11), 1234-1238. © 2019 Sage Publications. DOI: 10.1177/0022034519867641

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

Certify or not? An analysis of organic food supply chain with competing suppliers

Customers expect companies to provide clear health-related information for the products they purchase in a big data environment. Organic food is data-enabled with the organic label, but the certification cost discourages small-scale suppliers from certifying their product. This lack of a label means that product that satisfies the organic standard is regarded as conventional product. By considering the trade-off between the profit gained from organic label and additional costs of certification, this paper investigates an organic food supply chain where a leading retailer procures from two suppliers with different brands. Customers care about both the brand-value and quality (more specifically, if food is organic or not) when purchasing the product. We explore the organic certification and wholesale pricing strategies for suppliers, and the supplier selection and retail pricing strategies for the retailer. We find that when two suppliers adopt asymmetric certification strategy, the retailer tends to procure the product with organic label. The supplier without a brand name can compensate with organic certification, which leads to more profits than the branded rival. As the risk of being abandoned by the retailer increases, the supplier without a brand name is more eager than the rival to obtain the organic label. If both suppliers certify the product, however, they will fall into a prisoner’s dilemma under situation with low health utility from organic label and high certification cost