Background In some cases, a dentist cannot solve the difficulties a patient has with an implant because the implant system is unknown. Therefore, there is a need for a system for identifying the implant system of a patient from limited data that does not depend on the dentist’s knowledge and experience. The purpose of this study was to identify dental implant systems using a deep learning method. Methods A dataset of 1282 panoramic radiograph images with implants were used for deep learning. An object detection algorithm (Yolov3) was used to identify the six implant systems by three manufactures. To implement the algorithm, TensorFlow and Keras deep-learning libraries were used. After training was complete, the true positive (TP) ratio and average precision (AP) of each implant system as well as the mean AP (mAP), and mean intersection over union (mIoU) were calculated to evaluate the performance of the model. Results The number of each implant system varied from 240 to 1919. The TP ratio and AP of each implant system varied from 0.50 to 0.82 and from 0.51 to 0.85, respectively. The mAP and mIoU of this model were 0.71 and 0.72, respectively. Conclusions The results of this study suggest that implants can be identified from panoramic radiographic images using deep learning-based object detection. This identification system could help dentists as well as patients suffering from implant problems. However, more images of other implant systems will be necessary to increase the learning performance to apply this system in clinical practice.

Purpose: The purpose of this study was to develop a method for classifying dental arches using a convolutional neural network (CNN) as the first step in a system for designing removable partial dentures. Methods: Using 1184 images of dental arches (maxilla: 748 images; mandible: 436 images), arches were classified into four arch types: edentulous, intact dentition, arches with posterior tooth loss, and arches with bounded edentulous space. A CNN method to classify images was developed using Tensorflow and Keras deep learning libraries. After completion of the learning procedure, the diagnostic accuracy, precision, recall, F-measure and area under the curve (AUC) for each jaw were calculated for diagnostic performance of learning. The classification was also predicted using other images, and percentages of correct predictions (PCPs) were calculated. The PCPs were compared with the Kruskal-Wallis test (p = 0.05). Results: The diagnostic accuracy was 99.5% for the maxilla and 99.7% for the mandible. The precision, recall, and F-measure for both jaws were 0.25, 1.0 and 0.4, respectively. The AUC was 0.99 for the maxilla and 0.98 for the mandible. The PCPs of the classifications were more than 95% for all types of dental arch. There were no significant differences among the four types of dental arches in the mandible. Conclusions:The results of this study suggest that dental arches can be classified and predicted using a CNN. Future development of systems for designing removable partial dentures will be made possible using this and other AI technologies.

We measured primary production by phytoplankton in the south basin of Lake Baikal, Russia, by in situ 13 Cbicarbonate incubations within the period March-October in two consecutive years (1999 and 2000). Primary production was highest in the subsurface layer, possibly due to near-surface photoinhibition of photosynthesis, even under 0.8 m of ice cover in March. Areal primary production varied from 79 mg C m Ϫ2 day Ϫ1 (March) to 424 mg C m Ϫ2 day Ϫ1 (August), and annual primary production was roughly estimated as 75 g C m Ϫ2 year Ϫ1 , both of which are within the lower range of previous estimates. Size fractionation measurements revealed that phytoplankton in the Ͻ20 µm fraction accounted for 72%, 96%, and 85% of total primary production in March, August, and October, respectively. The contribution of picophytoplankton (Ͻ2 µm) to total primary production ranged from 41% to 62%. A large fraction (82%-98%) of particulate organic carbon was associated with particles in the Ͻ20 µm fraction. These results suggest that nano-and picophytoplankton play an important role as primary producers in the pelagic ecosystem of Lake Baikal.

Simple correlation and multiple regression analyses were performed to examine the relationship between primary productivity and environmental factors in the north basin of Lake Biwa. The primary production rates used in the analyses were estimated monthly or bimonthly during the growing season (April–November) in 1992, 1996 and 1997 with the 13C method. Elemental (C, N and P) contents of seston were used to assess nutrient conditions. Analyses revealed that 86% of variance in depth‐integrated primary production rates (areal PP) can be explained by changes in light intensity, and sestonic C, N and P concentrations. Water temperature had no effect on areal PP. To assess relative effects of light and nutrients on PP, the P:B ratio was estimated by normalizing PP with sestonic C. The areal P:B ratio correlated most significantly with the sestonic N:P ratio, followed by light intensity. When regression analyses were made at each depth, however, the P:B ratio correlated significantly only with the sestonic N:P ratio at 0 and 1 m depths, while light intensity was also incorporated into the regressions at deeper than 2.5 m. In these regressions, the P:B ratio was negatively correlated with sestonic N:P ratio but positively with light intensity. The results suggest that the primary production rate in this lake was mainly limited by P relative to N supply rates, but was not free from light limitation in a large part of the epilimnion. In Lake Biwa, the vertical water mixing regime as well as the nutrient supply seem to be important in determining the growth and composition of primary producers, since the surface mixing layer extends into 10–15 m depths during most of the growing season.

The purpose of this study is to develop a method for recognizing dental prostheses and restorations of teeth using a deep learning. A dataset of 1904 oral photographic images of dental arches (maxilla: 1084 images; mandible: 820 images) was used in the study. A deep-learning method to recognize the 11 types of dental prostheses and restorations was developed using TensorFlow and Keras deep learning libraries. After completion of the learning procedure, the average precision of each prosthesis, mean average precision, and mean intersection over union were used to evaluate learning performance. The average precision of each prosthesis varies from 0.59 to 0.93. The mean average precision and mean intersection over union of this system were 0.80 and 0.76, respectively. More than 80% of metallic dental prostheses were detected correctly, but only 60% of tooth-colored prostheses were detected. The results of this study suggest that dental prostheses and restorations that are metallic in color can be recognized and predicted with high accuracy using deep learning; however, those with tooth color are recognized with moderate accuracy.