Abstract. In this study, a multi-state degraded system is studied, where status of system is continuously degrading over time. As time progresses, system may either deteriorate gradually and go to lower performance state, or it may fail suddenly. If the system fails, some repairs are carried out to restore the system to the previous state. When the inspections reveal that the system has reached its last acceptable state, a PM is carried out to restore the system to the higher performance states. The goal is to nd the optimal PM level, so that the mean availability of the system is maximized and the total cost of the system is minimized. In this regard, Markov process is employed to represent di erent states of system. An integrated optimization approach is also suggested based on the desirability function of statistical approach. The suggested aggregation method is robust to the potential dependency between the total cost and the mean availability. It also ensures that both objective functions fall in decision-maker's acceptable region. In order to show the e ciency of the proposed approach, a numerical example is presented and analyzed.
Dentists could fail to notice periapical lesions (PLs) while examining panoramic radiographs. Accordingly, this study aimed to develop an artificial intelligence (AI) designed to address this problem. Materials and methods: a total of 18618 periapical root areas (PRA) on 713 panoramic radiographs were annotated and classified as having or not having PLs. An AI model consisting of two convolutional neural networks (CNNs), a detector and a classifier, was trained on the images. The detector localized PRAs using a bounding-box-based object detection model, while the classifier classified the extracted PRAs as PL or not-PL using a fine-tuned CNN. The classifier was trained and validated on a balanced subset of the original dataset that included 3249 PRAs, and tested on 707 PRAs. Results: the detector achieved an average precision of 74.95%, while the classifier accuracy, sensitivity and specificity were 84%, 81% and 86%, respectively. When integrating both detection and classification models, the proposed method accuracy, sensitivity, and specificity were 84.6%, 72.2%, and 85.6%, respectively. Conclusion: a two-stage CNN model consisting of a detector and a classifier can successfully detect periapical lesions on panoramic radiographs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.