Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.

Sex and chronological age estimation are crucial in forensic investigations and research on individual identification. Although manual methods for sex and age estimation have been proposed, these processes are labor-intensive, time-consuming, and error-prone. The purpose of this study was to estimate sex and chronological age from panoramic radiographs automatically and robustly using a multi-task deep learning network (ForensicNet). ForensicNet consists of a backbone and both sex and age attention branches to learn anatomical context features of sex and chronological age from panoramic radiographs and enables the multi-task estimation of sex and chronological age in an end-to-end manner. To mitigate bias in the data distribution, our dataset was built using 13,200 images with 100 images for each sex and age range of 15-80 years. The ForensicNet with EfficientNet-B3 exhibited superior estimation performance with mean absolute errors of 2.93 ± 2.61 years and a coefficient of determination of 0.957 for chronological age, and achieved accuracy, specificity, and sensitivity values of 0.992, 0.993, and 0.990, respectively, for sex prediction. The network demonstrated that the proposed sex and age attention branches with a convolutional block attention module significantly improved the estimation performance for both sex and chronological age from panoramic radiographs of elderly patients. Consequently, we expect that ForensicNet will contribute to the automatic and accurate estimation of both sex and chronological age from panoramic radiographs.

Automatic classification of 3D positional relationship between mandibular third molar and inferior alveolar canal using a distance-aware network.

The purpose of this study was to automatically classify the three-dimensional (3D) positional relationship between an impacted mandibular third molar (M3) and the inferior alveolar canal (MC) using a distance-aware network in cone-beam CT (CBCT) images. We developed a network consisting of cascaded stages of segmentation and classification for the buccal-lingual relationship between the M3 and the MC. The M3 and the MC were simultaneously segmented using Dense121 U-Net in the segmentation stage, and their buccal-lingual relationship was automatically classified using a 3D distance-aware network with the multichannel inputs of the original CBCT image and the signed distance map (SDM) generated from the segmentation in the classification stage. The Dense121 U-Net achieved the highest average precision of 0.87, 0.96, and 0.94 in the segmentation of the M3, the MC, and both together, respectively. The 3D distance-aware classification network of the Dense121 U-Net with the input of both the CBCT image and the SDM showed the highest performance of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve, each of which had a value of 1.00. The SDM generated from the segmentation mask significantly contributed to increasing the accuracy of the classification network. The proposed distance-aware network demonstrated high accuracy in the automatic classification of the 3D positional relationship between the M3 and the MC by learning anatomical and geometrical information from the CBCT images.

Improved resin-to-dentin bond strength and durability via non-thermal atmospheric pressure plasma drying of etched dentin.

This study aimed to evaluate the improvement in strength and durability of the bond between dentin and composite resins following plasma drying of the etched dentin surface using non-thermal atmospheric pressure plasma. Plasma drying was applied to the etched dentin before applying adhesive. Conventional wet-bonding and helium (He) gas-dried bonding schemes were used as control groups. The bond strength of the composite resin to dentin was measured as the microtensile bond strength at 24 h after bonding and after 10,000 cycles of thermocycling. Hybrid layer formation was observed using micro-Raman spectroscopy and scanning electron microscopy. Although the bond-strength values were not statistically different either at 24 h after bonding or after thermocycling, the bond strength of the plasma-dried bonding group was significantly higher than the conventional wet-bonding group and He gas-dried bonding group. Micro-Raman spectral analysis revealed effective penetration of the adhesive and an improved polymerization rate of the adhesive after plasma drying. Plasma drying increased the penetration of hydrophobic resin into the collagen mesh structure, which improved mechanical bonding and long-term durability between dentin and composite resin.

Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network.

Sex determination is essential for identifying unidentified individuals, particularly in forensic contexts. Traditional methods for sex determination involve manual measurements of skeletal features on CBCT scans. However, these manual measurements are labor-intensive, time-consuming, and error-prone. The purpose of this study was to automatically and accurately determine sex on a CBCT scan using a two-stage anatomy-guided attention network (SDetNet). SDetNet consisted of a 2D frontal sinus segmentation network (FSNet) and a 3D anatomy-guided attention network (SDNet). FSNet segmented frontal sinus regions in the CBCT images and extracted regions of interest (ROIs) near them. Then, the ROIs were fed into SDNet to predict sex accurately. To improve sex determination performance, we proposed multi-channel inputs (MSIs) and an anatomy-guided attention module (AGAM), which encouraged SDetNet to learn differences in the anatomical context of the frontal sinus between males and females. SDetNet showed superior sex determination performance in the area under the receiver operating characteristic curve, accuracy, Brier score, and specificity compared with the other 3D CNNs. Moreover, the results of ablation studies showed a notable improvement in sex determination with the embedding of both MSI and AGAM. Consequently, SDetNet demonstrated automatic and accurate sex determination by learning the anatomical context information of the frontal sinus on CBCT scans.

SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network.

The objective of this study was to automatically classify surgical plans for maxillary sinus floor augmentation in implant placement at the maxillary posterior edentulous region using a 3D distance-guided network on CBCT images. We applied a modified ABC classification method consisting of five surgical approaches for the deep learning model. The proposed deep learning model (SinusC-Net) consisted of two stages of detection and classification according to the modified classification method. In detection, five landmarks on CBCT images were automatically detected using a volumetric regression network; in classification, the CBCT images were automatically classified as to the five surgical approaches using a 3D distance-guided network. The mean MRE for landmark detection was 0.87 mm, and SDR for 2 mm or lower, 95.47%. The mean accuracy, sensitivity, specificity, and AUC for classification by the SinusC-Net were 0.97, 0.92, 0.98, and 0.95, respectively. The deep learning model using 3D distance-guidance demonstrated accurate detection of 3D anatomical landmarks, and automatic and accurate classification of surgical approaches for sinus floor augmentation in implant placement at the maxillary posterior edentulous region.

V2-Net: An Attention-guided Volumetric Regression Network for Tooth Landmark Localization on CT Images with Metal Artifacts.

For virtual surgical planning in orthognathic surgery, marking tooth landmarks on CT images is an important procedure. However, the manual localization procedure of tooth landmarks is time-consuming, labor-intensive, and requires expert knowledge. Also, direct and automatic tooth landmark localization on CT images is difficult because of the lower resolution and metal artifacts of dental images. The purpose of this study was to propose an attention-guided volumetric regression network (V2-Net) for accurate tooth landmark localization on CT images with metal artifacts and lower resolution. V2-Net has an attention-guided network architecture using a coarse-to-fine-attention mechanism that guided the 3D probability distribution of tooth landmark locations within anatomical structures from the coarse V-Net to the fine V-Net for more focus on tooth landmarks. In addition, we combined attention-guided learning and a 3D attention module with optimal Pseudo Huber loss to improve the localization accuracy. Our results show that the proposed method achieves state-of-the-art accuracy of 0.85 ± 0.40 mm in terms of mean radial error, outperforming previous studies. In ablation studies, we observed that the proposed attention-guided learning and a 3D attention module improved the accuracy of tooth landmark localization in CT images of lower resolution and metal artifacts. Furthermore, our method achieved 97.92% in terms of the success detection rate within the clinically accepted accuracy range of 2.0 mm.

Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb.

Rodents have a well-developed sense of smell and are used to detect explosives, mines, illegal substances, hidden currency, and contraband, but it is impossible to keep their concentration constantly. Therefore, there is an ongoing effort to infer odors detected by animals without behavioral readings with brain-computer interface (BCI) technology. However, the invasive BCI technique has the disadvantage that long-term studies are limited by the immune response and electrode movement. On the other hand, near-infrared spectroscopy (NIRS)-based BCI technology is a non-invasive method that can measure neuronal activity without worrying about the immune response or electrode movement. This study confirmed that the NIRS-based BCI technology can be used as an odor detection and identification from the rat olfactory system. In addition, we tried to present features optimized for machine learning models by extracting six features, such as slopes, peak, variance, mean, kurtosis, and skewness, from the hemodynamic response, and analyzing the importance of individuals or combinations. As a result, the feature with the highest F1-Score was indicated as slopes, and it was investigated that the combination of the features including slopes and mean was the most important for odor inference. On the other hand, the inclusion of other features with a low correlation with slopes had a positive effect on the odor inference, but most of them resulted in insignificant or rather poor performance. The results presented in this paper are expected to serve as a basis for suggesting the development direction of the hemodynamic response-based bionic nose in the future.

Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network.

The purpose of this study was to propose a continuity-aware contextual network (Canal-Net) for the automatic and robust 3D segmentation of the mandibular canal (MC) with high consistent accuracy throughout the entire MC volume in cone-beam CT (CBCT) images. The Canal-Net was designed based on a 3D U-Net with bidirectional convolutional long short-term memory (ConvLSTM) under a multi-task learning framework. Specifically, the Canal-Net learned the 3D anatomical context information of the MC by incorporating spatio-temporal features from ConvLSTM, and also the structural continuity of the overall MC volume under a multi-task learning framework using multi-planar projection losses complementally. The Canal-Net showed higher segmentation accuracies in 2D and 3D performance metrics (p < 0.05), and especially, a significant improvement in Dice similarity coefficient scores and mean curve distance (p < 0.05) throughout the entire MC volume compared to other popular deep learning networks. As a result, the Canal-Net achieved high consistent accuracy in 3D segmentations of the entire MC in spite of the areas of low visibility by the unclear and ambiguous cortical bone layer. Therefore, the Canal-Net demonstrated the automatic and robust 3D segmentation of the entire MC volume by improving structural continuity and boundary details of the MC in CBCT images.

Improvement of osseointegration of Ti-6Al-4V ELI alloy orthodontic mini-screws through anodization, cyclic pre-calcification, and heat treatments.

BACKGROUND: Mini-screws are widely used as temporary anchorages in orthodontic treatment, but have the disadvantage of showing a high failure rate of about 10%. Therefore, orthodontic mini-screws should have high biocompatibility and retention. Previous studies have demonstrated that the retention of mini-screws can be improved by imparting bioactivity to the surface. The method for imparting bioactivity proposed in this paper is to sequentially perform anodization, periodic pre-calcification, and heat treatments with a Ti-6Al-4V ELI alloy mini-screw. MATERIALS AND METHODS: A TiO2 nanotube-structured layer was formed on the surface of the Ti-6Al-4V ELI alloy mini-screw through anodization in which a voltage of 20 V was applied to a glycerol solution containing 20 wt% H2O and 1.4 wt% NH4F for 60 min. Fine granular calcium phosphate precipitates of HA and octacalcium phosphate were generated as clusters on the surface through the cyclic pre-calcification and heat treatments. The cyclic pre-calcification treatment is a process of immersion in a 0.05 M NaH2PO4 solution and a saturated Ca(OH)2 solution at 90 °C for 1 min each. RESULTS: It was confirmed that the densely structured protrusions were precipitated, and Ca and P concentrations, which bind and concentrate endogenous bone morphogenetic proteins, increased on the surface after simulated body fluid (SBF) immersion test. In addition, the removal torque of the mini-screw fixed into rabbit tibias for 4 weeks was measured to be 8.70 ± 2.60 N cm. CONCLUSIONS: A noteworthy point in this paper is that the Ca and P concentrations, which provide a scaffold suitable for endogenous bone formation, further increased over time after SBF immersion of the APH group specimens. The other point is that our mini-screws have a significantly higher removal torque compared to untreated mini-screws. These results represent that the mini-screw proposed in this paper can be used as a mini-screw for orthodontics.

Light-Induced Fluorescence-Based Device and Hybrid Mobile App for Oral Hygiene Management at Home: Development and Usability Study.

BACKGROUND: Dental diseases can be prevented through the management of dental plaques. Dental plaque can be identified using the light-induced fluorescence (LIF) technique that emits light at 405 nm. The LIF technique is more convenient than the commercial technique using a disclosing agent, but the result may vary for each individual as it still requires visual identification. OBJECTIVE: The objective of this study is to introduce and validate a deep learning-based oral hygiene monitoring system that makes it easy to identify dental plaques at home. METHODS: We developed a LIF-based system consisting of a device that can visually identify dental plaques and a mobile app that displays the location and area of dental plaques on oral images. The mobile app is programmed to automatically determine the location and distribution of dental plaques using a deep learning-based algorithm and present the results to the user as time series data. The mobile app is also built with convergence of naive and web applications so that the algorithm is executed on a cloud server to efficiently distribute computing resources. RESULTS: The location and distribution of users' dental plaques could be identified via the hand-held LIF device or mobile app. The color correction filter in the device was developed using a color mixing technique. The mobile app was built as a hybrid app combining the functionalities of a native application and a web application. Through the scrollable WebView on the mobile app, changes in the time series of dental plaque could be confirmed. The algorithm for dental plaque detection was implemented to run on Amazon Web Services for object detection by single shot multibox detector and instance segmentation by Mask region-based convolutional neural network. CONCLUSIONS: This paper shows that the system can be used as a home oral care product for timely identification and management of dental plaques. In the future, it is expected that these products will significantly reduce the social costs associated with dental diseases.

Fabrication of Convex PDMS-Parylene Microstructures for Conformal Contact of Planar Micro-Electrode Array.

Polymer-based micro-electrode arrays (MEAs) are gaining attention as an essential technology to understand brain connectivity and function in the field of neuroscience. However, polymer based MEAs may have several challenges such as difficulty in performing the etching process, difficulty of micro-pattern generation through the photolithography process, weak metal adhesion due to low surface energy, and air pocket entrapment over the electrode site. In order to compensate for the challenges, this paper proposes a novel MEA fabrication process that is performed sequentially with (1) silicon mold preparation; (2) PDMS replica molding, and (3) metal patterning and parylene insulation. The MEA fabricated through this process possesses four arms with electrode sites on the convex microstructures protruding about 20 µm from the outermost layer surface. The validity of the convex microstructure implementation is demonstrated through theoretical background. The electrochemical impedance magnitude is 204.4 ± 68.1 kΩ at 1 kHz. The feasibility of the MEA with convex microstructures was confirmed by identifying the oscillation in the beta frequency band (13-30 Hz) in the electrocorticography signal of a rat olfactory bulb during respiration. These results suggest that the MEA with convex microstructures is promising for applying to various neural recording and stimulation studies.

Biophysical restriction of growth area using a monodispersed gold sphere nanobarrier prolongs the mitotic phase in HeLa cells.

Gold nanoparticles are widely exploited for biological and biotechnical applications owing to their stability, biocompatibility, and known effects on cellular behaviors. Many studies have focused on nanoparticles that are internalized into cells, but extracellular nanoparticles also can regulate cell behavior, a practice known as in-plane surface nanotopography. We demonstrated that nanobarriers composed of morphologically homogeneous gold nanospheres prolonged the mitotic (M) phase in the cervical cancer cell line HeLa without inducing apoptosis. The nanobarrier was formed by electrostatic deposition of nanospheres on a negatively charged, fibronectin-coated substrate. We tested the effects of differently sized nanospheres. Gold nanospheres 42 nm in diameter were found to be non-toxic, while 111 nm nanospheres induced the production of reactive oxygen species, resulting in apoptotic cell death and arrest of cytokinesis. When exposed to sufficient 83 nm gold nanospheres to fabricate a surface nanobarrier, the M phase was delayed but cells proceeded to cytokinesis and the G1 phase. Live-cell imaging showed that the M phase increased by 2.9 h, 2.4 times longer than in control cells. Biophysical analyses indicated that this could be attributed to the specific size of the nanobarrier that physically limited the growth area around the cell.

Plateau-Shaped Flexible Polymer Microelectrode Array for Neural Recording.

Conventional polymer multielectrode arrays (MEAs) have limitations resulting from a high Young's modulus, including low conformability and gaps between the electrodes and neurons. These gaps are not a problem in soft tissues such as the brain, due to the repopulation phenomenon. However, gaps can result in signal degradation when recording from a fiber bundle, such as the spinal cord. Methods: We propose a method for fabricating flexible polydimethylsiloxane (PDMS)-based MEAs featuring plateau-shaped microelectrodes. The proposed fabrication technique enables the electrodes on the surface of MEAs to make a tight connection to the neurons, because the wire of the MEA is fabricated to be plateau-shaped, as the Young's modulus of PDMS is similar to soft tissues and PDMS follows the curvature of the neural tissue due to its high conformability compared to the other polymers. Injury caused by the movement of the MEAs can therefore be minimized. Each electrode has a diameter of 130 µm and the 8-channel array has a center-to-center electrode spacing of 300 µm. The signal-to-noise ratio of the plateau-shaped electrodes was larger than that of recessed electrodes because there was no space between the electrode and neural cell. Reliable neural recordings were possible by adjusting the position of the electrode during the experiment without trapping air under the electrodes. Simultaneous multi-channel neural recordings were successfully achieved from the spinal cord of rodents. We describe the fabrication technique, electrode 3D profile, electrode impedance, and MEA performance in in vivo experiments in rodents.

Automatic detection of tooth cracks in optical coherence tomography images.

PURPOSE: The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging. METHODS: We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically. RESULTS: The detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than 100 µm. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform. CONCLUSIONS: We were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

Improved accuracy in periodontal pocket depth measurement using optical coherence tomography.

PURPOSE: The purpose of this study was to examine whether periodontal pocket could be satisfactorily visualized by optical coherence tomography (OCT) and to suggest quantitative methods for measuring periodontal pocket depth. METHODS: We acquired OCT images of periodontal pockets in a porcine model and determined the actual axial resolution for measuring the exact periodontal pocket depth using a calibration method. Quantitative measurements of periodontal pockets were performed by real axial resolution and compared with the results from manual periodontal probing. RESULTS: The average periodontal pocket depth measured by OCT was 3.10±0.15 mm, 4.11±0.17 mm, 5.09±0.17 mm, and 6.05±0.21 mm for each periodontal pocket model, respectively. These values were similar to those obtained by manual periodontal probing. CONCLUSIONS: OCT was able to visualize periodontal pockets and show attachment loss. By calculating the calibration factor to determine the accurate axial resolution, quantitative standards for measuring periodontal pocket depth can be established regardless of the position of periodontal pocket in the OCT image.

Fabrication of polydimethylsiloxane (PDMS) - based multielectrode array for neural interface.

Flexible multielectrode arrays (MEAs) are being developed with various materials, and polyimide has been widely used due to the conveniece of process. Polyimide is developed in the form of photoresist. And this enable precise and reproducible fabrication. PDMS is another good candidate for MEA base material, but it has poor surface energy and etching property. In this paper, we proposed a better fabrication process that could modify PDMS surface for a long time and open the site of electrode and pad efficiently without PDMS etching.