Reduced menthol sensitivity in a prodromal Parkinson's disease model induced by intranasal rotenone treatment.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms, and it is associated with several prodromal non-motor symptoms, including an impaired sense of smell, taste and touch. We previously reported that bitter taste impairments occur independently of olfactory impairments in an early-stage PD animal model using short-term intranasal rotenone-treated mice. Cool temperatures also affect bitter taste perception, but it remains unclear whether or not bitter taste impairments result from an altered sensitivity for intraoral cool stimuli. We examined disturbances in the intraoral menthol sensitivity, such as coolness at low concentrations of menthol, using a brief-access test. Once a day, one solution from the 7-concentration series of (-)-menthol (0-2.3 mM) or the bitter taste quinine-HCl (0.3 mM) was randomly presented 20 times for 10 s to water-deprived mice before and 1 week after rotenone treatment. The total number of licks within 20 times was significantly decreased with the presentation of 2.3 mM menthol and quinine-HCl, compared to distilled water in untreated mice, but not in rotenone-treated mice. The correlation between the licks for quinine-HCl and that for menthol was increased after rotenone treatment. In contrast, the 2-bottle choice test for 48 h clarified that menthol sensitivity was increased after rotenone treatment. Furthermore, a thermal place preference test revealed that seeking behavior toward a cold-floored room was increased in the rotenone-treated mice despite the unchanged plantar cutaneous cold sensitivity. These results suggest that taste impairments in this model mice are at least partly due to intraoral somatosensory impairments, accompanied by peripheral/central malfunction.

Accuracy Evaluation of an Estimation System for Dental Treatment Sites by Using Image Recognition.

In dentistry, misidentification of the treatment site may occur an adverse event with irreversible consequences. Among these, the left-right tooth error is the second most common site misidentification. In this study, we developed a treatment site estimation system using image recognition, and the accuracy rate of the left and right teeth was more than 85%. The results suggest that this system can be used to prevent the misidentification of the left and right teeth.

Effect of Push-Pull HEPA Filters on Air Age in a Dental Treatment Room.

Air quality was evaluated by visualizing with CFD (Computational Fluid Dynamics) where air tends to stagnate in the dental practice space when natural ventilation and HEPA filters are used together. The results showed that natural ventilation by opening and closing windows and doors alone was not sufficient.

Panoramic Radiograph Generation and Image Reconstruction from Latent Vectors Using a Generative Adversarial Network.

In this study, StyleGAN2 was trained with panoramic radiographs, and original images were projected into the latent space of StyleGAN2. The resulting latent vectors were input into StyleGAN2, and corresponding images were generated to reconstruct the original images. The original and reconstructed images were evaluated by pediatric dentists and found to be similar. Our results suggest that StyleGAN2 could be applied to the anonymization and data compression of medical images.

Automatic dental age calculation from panoramic radiographs using deep learning: a two-stage approach with object detection and image classification.

BACKGROUND: Dental age is crucial for treatment planning in pediatric and orthodontic dentistry. Dental age calculation methods can be categorized into morphological, biochemical, and radiological methods. Radiological methods are commonly used because they are non-invasive and reproducible. When radiographs are available, dental age can be calculated by evaluating the developmental stage of permanent teeth and converting it into an estimated age using a table, or by measuring the length between some landmarks such as the tooth, root, or pulp, and substituting them into regression formulas. However, these methods heavily depend on manual time-consuming processes. In this study, we proposed a novel and completely automatic dental age calculation method using panoramic radiographs and deep learning techniques. METHODS: Overall, 8,023 panoramic radiographs were used as training data for Scaled-YOLOv4 to detect dental germs and mean average precision were evaluated. In total, 18,485 single-root and 16,313 multi-root dental germ images were used as training data for EfficientNetV2 M to classify the developmental stages of detected dental germs and Top-3 accuracy was evaluated since the adjacent stages of the dental germ looks similar and the many variations of the morphological structure can be observed between developmental stages. Scaled-YOLOv4 and EfficientNetV2 M were trained using cross-validation. We evaluated a single selection, a weighted average, and an expected value to convert the probability of developmental stage classification to dental age. One hundred and fifty-seven panoramic radiographs were used to compare automatic and manual human experts' dental age calculations. RESULTS: Dental germ detection was achieved with a mean average precision of 98.26% and dental germ classifiers for single and multi-root were achieved with a Top-3 accuracy of 98.46% and 98.36%, respectively. The mean absolute errors between the automatic and manual dental age calculations using single selection, weighted average, and expected value were 0.274, 0.261, and 0.396, respectively. The weighted average was better than the other methods and was accurate by less than one developmental stage error. CONCLUSION: Our study demonstrates the feasibility of automatic dental age calculation using panoramic radiographs and a two-stage deep learning approach with a clinically acceptable level of accuracy.

An efficient annotation method for image recognition of dental instruments.

To prevent needlestick injury and leftover instruments, and to perform efficient dental treatment, it is important to know the instruments required during dental treatment. Therefore, we will obtain a dataset for image recognition of dental treatment instruments, develop a system for detecting dental treatment instruments during treatment by image recognition, and evaluate the performance of the system to establish a method for detecting instruments during treatment. We created an image recognition dataset using 23 types of instruments commonly used in the Department of Restorative Dentistry and Endodontology at Osaka University Dental Hospital and a surgeon's hands as detection targets. Two types of datasets were created: one annotated with only the characteristic parts of the instruments, and the other annotated with the entire parts of instruments. YOLOv4 and YOLOv7 were used as the image recognition system. The performance of the system was evaluated in terms of two metrics: detection accuracy (DA), which indicates the probability of correctly detecting the number of target instruments in an image, and the average precision (AP). When using YOLOv4, the mean DA and AP were 89.3% and 70.9%, respectively, when the characteristic parts of the instruments were annotated and 85.3% and 59.9%, respectively, when the entire parts of the instruments were annotated. When using YOLOv7, the mean DA and AP were 89.7% and 80.8%, respectively, when the characteristic parts of the instruments were annotated and 84.4% and 63.5%, respectively, when the entire parts of the instruments were annotated. The detection of dental instruments can be performed efficiently by targeting the parts characterizing them.

Oral squamous cell carcinoma diagnosis in digitized histological images using convolutional neural network.

Background/purpose: Diagnostic methods of oral squamous cell carcinoma (SCC) using artificial intelligence (AI) and digital-histopathologic images have been developed. However, previous AI training methods have focused on the cellular atypia given by the training of high-magnification images, and little attention has been paid to structural atypia provided by low-power wide fields. Since oral SCC has histopathologic types with bland cytology, both cellular atypia and structural atypia must be considered as histopathologic features. This study aimed to investigate AI ability to judge oral SCC in a novel training method considering cellular and structural atypia and their suitability. Materials and methods: We examined digitized histological whole-slide images from 90 randomly selected patients with tongue SCC who attended a dental hospital. Image patches of 1000 × 1000 pixels were cut from whole-slide images at 0.3125-, 1.25-, 5-, and 20-fold magnification, and 90,059 image patches were used for training and evaluation. These image patches were resized into 224 × 224, 384 × 384, 512 × 512, and 768 × 768 pixels, and the differences in input size were analyzed. EfficientNet B0 was utilized as the convolutional neural network model. Gradient-weighted class activation mapping (Grad-CAM) was used to elucidate its validity. Results: The proposed method achieved a peak accuracy of 99.65% with an input size of 512 × 512 pixels. Grad-CAM suggested that AI focused on both cellular and structural atypia of SCC, and tended to focus on the region surrounding the basal layer. Conclusion: Training AI regarding both cellular and structural atypia using various magnification images simultaneously may be suitable for the diagnosis of oral SCC.

Numerical investigation of effects of tongue articulation and velopharyngeal closure on the production of sibilant [s].

A numerical simulation of sibilant /s/ production with the realistically moving vocal tract was conducted to investigate the flow and acoustic characteristics during the articulation process of velopharyngeal closure and tongue movement. The articulation process was simulated from the end of /u/ to the middle of /s/ in the Japanese word /usui/, including the tongue elevation and the velopharyngeal valve closure. The time-dependent vocal tract geometry was reconstructed from the computed tomography scan. The moving immersed boundary method with the hierarchical structure grid was adopted to approach the complex geometry of the human speech organs. The acoustic characteristics during the co-articulation process were observed and consistent with the acoustic measurement for the subject of the scan. The further simulations with the different closing speeds of the velopharyngeal closure showed that the far-field sound during the co-articulation process was amplified with the slower closing case, and the velum closure speed was inverse proportional to the sound amplitude with the slope value of - 35.3 dB s/m. This indicates possible phonation of indistinguishable aeroacoustics sound between /u/ and /s/ with slower velopharyngeal closure.

Estimation of sibilant groove formation and sound generation from early hominin jawbones.

The speech production capability of sibilant fricatives of early hominin was assessed by interpolating the modern human vocal tract to an Australopithecine specimen based on the jawbone landmarks, and then simulating the airflow and sound generation. The landmark interpolation demonstrates the possibility to form the sibilant groove in the anterior part of the oral tract, and results of the aeroacoustic simulation indicate that the early hominins had the potential to produce the fricative broadband noise with a constant supply of airflow to the oral cavity, although the ancestor's tongue deformation ability is still uncertain, and the results are highly speculative.

Numerical simulation of air age in dental offices.

Dental professionals are at high risk of exposure to communicable diseases during clinical practice, but many dental clinics provide clinical care in closed spaces. Therefore, it is essential to develop efficient ventilation methods in dental clinics that do not rely on natural ventilation. In this study, to clarify the factors that cause air retention in dental offices, we conducted computational flow dynamics simulations focusing on (1) the flow path from the entrance to the exhaust port and (2) the presence of partitions. A three-dimensional model of a dental clinic with three dental chairs was created, and simulations were conducted for scenarios with and without partitions with different entrance and exhaust port positions. Evaluation of these simulations on the basis of the age of air, an indicator of ventilation efficiency, showed that the value of the air age near the partition was locally high in the scenarios with partitions. In the scenarios where the exhaust port was located close to the entrance, the air age near the exhaust port was high, regardless of the presence of a partition. In addition to wearing protective clothing and sterilizing instruments, it is important to consider air quality improvement as a countermeasure against airborne and droplet infections, such as virus infections, in dental clinics.

Taste Impairments in a Parkinson's Disease Model Featuring Intranasal Rotenone Administration in Mice.

BACKGROUND: Taste impairments are often accompanied by olfactory impairments in the early stage of Parkinson's disease (PD). The development of animal models is required to elucidate the mechanisms underlying taste impairments in PD. OBJECTIVE: This study was conducted to clarify whether the intranasal administration of rotenone causes taste impairments prior to motor deficits in mice. METHODS: Rotenone was administrated to the right nose of mice once a day for 1 or 4 week(s). In the 1-week group, taste, olfactory, and motor function was assessed before and after a 1-week recovery period following the rotenone administration. Motor function was also continuously examined in the 4-weeks group from 0 to 5 weeks. After a behavioral test, the number of catecholamine neurons (CA-Nos) was counted in the regions responsible for taste, olfactory, and motor function. RESULTS: taste and olfactory impairments were simultaneously observed without locomotor impairments in the 1-week group. The CA-Nos was significantly reduced in the olfactory bulb and nucleus of the solitary tract. In the 4-week group, locomotor impairments were observed from the third week, and a significant reduction in the CA-Nos was observed in the substantia nigra (SN) and ventral tegmental area (VTA) at the fifth week along with the weight loss. CONCLUSION: The intranasal administration of rotenone caused chemosensory and motor impairments in an administration time-period dependent manner. Since chemosensory impairments were expressed prior to the locomotor impairments followed by SN/VTA CA neurons loss, this rotenone administration model may contribute to the clarification of the prodromal symptoms of PD.

Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists.

Dentists need experience with clinical cases to practice specialized skills. However, the need to protect patient's private information limits their ability to utilize intraoral images obtained from clinical cases. In this study, since generating realistic images could make it possible to utilize intraoral images, progressive growing of generative adversarial networks are used to generate intraoral images. A total of 35,254 intraoral images were used as training data with resolutions of 128 × 128, 256 × 256, 512 × 512, and 1024 × 1024. The results of the training datasets with and without data augmentation were compared. The Sliced Wasserstein Distance was calculated to evaluate the generated images. Next, 50 real images and 50 generated images for each resolution were randomly selected and shuffled. 12 pediatric dentists were asked to observe these images and assess whether they were real or generated. The d prime of the 1024 × 1024 images was significantly higher than that of the other resolutions. In conclusion, generated intraoral images with resolutions of 512 × 512 or lower were so realistic that the dentists could not distinguish whether they were real or generated. This implies that the generated images can be used in dental education or data augmentation for deep learning, without privacy restrictions.

Numerical investigation of effects of incisor angle on production of sibilant /s/.

The effects of the inclination angle of the incisor on the speech production of the fricative consonant /s/ was investigated using an implicit compressible flow solver. The hierarchical structure grid was applied to reduce the grid generation time for the vocal tract geometry. The airflow and sound during the pronunciation of /s/ were simulated using the adaptively switched time stepping scheme, and the angle of the incisor in the vocal tract was changed from normal position up to 30°. The results showed that increasing the incisor angle affected the flow configuration and moved the location of the high turbulence intensity region thereby decreased the amplitudes of the sound in the frequency range from 8 to 12 kHz. Performing the Fourier transform on the velocity fluctuation, we found that the position of large magnitudes of the velocity at 10 kHz shifted toward the lip outlet when the incisor angle was increased. In addition, separate acoustic simulations showed that the shift in the potential sound source position decreased the far-field sound amplitudes above 8 kHz. These results provide the underlying insights necessary to design dental prostheses for the production of sibilant fricatives.

Predictive modeling for peri-implantitis by using machine learning techniques.

The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

Deep learning-based detection of dental prostheses and restorations.

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.

A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network.

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.

Mechanical effects of distributed fibre orientation in the periodontal ligament of an idealised geometry.

In this study, we computationally assess the effects of the distributed fibre orientation in the periodontal ligament (PDL) on mechanical responses of the tooth-PDL complex. An idealised axial-symmetric geometry of a tooth-PDL complex was constructed. The fibre orientation in the PDL was modelled as a trigonometric function based on anatomical knowledge, and the PDL was modelled as a transversely isotropic hyperelastic material dependent on fibre orientations. Parametric studies of the fibre orientation on the mechanical responses of the tooth-PDL complex were conducted. Obtained results showed that the anatomically consistent fibre orientation functions as a supporting structure against not only vertical but also horizontal loads.

Identification of dental implants using deep learning-pilot study.

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.

A simplified vocal tract model for articulation of [s]: The effect of tongue tip elevation on [s].

Fricative consonants are known to be pronounced by controlling turbulent flow inside a vocal tract. In this study, a simplified vocal tract model was proposed to investigate the characteristics of flow and sound during production of the fricative [s] in a word context. By controlling the inlet flow rate and tongue speed, the acoustic characteristics of [s] were reproduced by the model. The measurements with a microphone and a hot-wire anemometer showed that the flow velocity at the teeth gap and far-field sound pressure started oscillating before the tongue reached the /s/ position, and continued during tongue descent. This behaviour was not affected by the changes of the tongue speed. These results indicate that there is a time shift between source generation and tongue movement. This time shift can be a physical constraint in the articulation of words which include /s/. With the proposed model, we could investigate the effects of tongue speed on the flow and sound generation in a parametric way. The proposed methodology is applicable for other phonemes to further explore the aeroacoustics of phonation.

Aeroacoustic analysis on individual characteristics in sibilant fricative production.

The cause of individual acoustic characteristics of sibilant fricatives /s/ and /ʃ/ was analyzed by extracting vocal tract geometries and conducting aeroacoustic experiments and simulations on each geometry. The vocal tract geometries of five Japanese subjects while sustaining /s/ and /ʃ/ were collected by magnetic resonance imaging. Flow and sound generation in the vocal tracts was predicted by large eddy simulations of compressible flow. The characteristic dimensions of the vocal tracts were extracted and simplified vocal tract models were constructed to clarify the relationship between the geometries and the acoustic characteristics. The acoustic characteristics of sounds generated by the simplified models agreed well with the sounds predicted by the simulation, indicating that the proposed model is able to express the individual characteristics in the production of sibilant fricatives. A comparison of the models showed that the volume and length of a space downstream from the constriction are key factors controlling the acoustic characteristics of each subject.