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.

V-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.

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.

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.

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.

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.

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.

Morphologically homogeneous, pH-responsive gold nanoparticles for non-invasive imaging of HeLa cancer.

Gold nanoparticles (AuNPs) have been widely used as nanocarriers in drug delivery to improve the efficiency of chemotherapy treatment and enhance early disease detection. The advantages of AuNPs include their excellent biocompatibility, easy modification and functionalization, facile synthesis, low toxicity, and controllable particle size. This study aimed to synthesize a conjugated citraconic anhydride link between morphologically homogeneous AuNPs and doxorubicin (DOX) (DOX-AuNP).

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.

Strong Coupling of Carbon Quantum Dots in Plasmonic Nanocavities.

Confining light in extremely small cavities is crucial in nanophotonics, central to many applications. Employing a unique nanoparticle-on-mirror plasmonic structure and using a graphene film as a spacer, we create nanoscale cavities with volumes of only a few tens of cubic nanometers. The ultracompact cavity produces extremely strong optical near-fields, which facilitate the formation of single carbon quantum dots in the cavity and simultaneously empower the strong coupling between the excitons of the formed carbon quantum dot and the localized surface plasmons.

Understanding metal-enhanced fluorescence and structural properties in Au@Ag core-shell nanocubes.

Au@Ag core-shell structures have received particular interest due to their localized surface plasmon resonance properties and great potential as oxygen reduction reaction catalysts and building blocks for self-assembly. In this study, Au@Ag core-shell nanocubes (Au@AgNCs) were fabricated in a facile manner stepwise Ag reduction on Au nanoparticles (AuNPs). The size of the Au@AgNCs and their optical properties can be simply modulated by changing the Ag shell thickness.

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.

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.

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.

Tooth cracks detection and gingival sulcus depth measurement using optical coherence tomography.

The aims of this study were to develop an automatic detection technique for tooth cracks and to suggest quantitative methods for measuring gingival sulcus depth using swept-source optical coherence tomography (SS-OCT). We evaluated SS-OCT with wavelength centered at 1310 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 and gingival sulcus depth. The reliability of the SS-OCT images was verified by imaging the crack in extracted human teeth and gingival sulcus of porcine sample.

Development of the viewing distance measuring device in smartphone use.

In this paper, a low-cost lens fabrication method using a micro chamber produced by a three-dimensional (3-D) printer is presented. The shape of polydimethylsiloxane (PDMS) membrane was controlled by adjusting the pressure inside the chamber. On top of the shaped membrane, an ultraviolet-curable resin was poured.

A convex-shaped, PDMS-parylene hybrid multichannel ECoG-electrode array.

Long-term electrode implant is a challenge for successful brain-computer interfaces (BCIs). It is well known that electrocorticography (ECoG) using flexible planar electrodes is more suitable for long-term implants than intracortical neural recordings using penetrative electrodes. In this study, we propose a convex-shaped, PDMS-parylene hybrid multi-electrode array for long-term stable ECoG recording on the brain or the spinal cord.

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.

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.

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.