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Bioengineering, Volume 10, Issue 11 (November 2023) – 101 articles

Cover Story (view full-size image): Current methods to repair CMF bone and tooth defects use a multi-step approach consisting of bone repair followed by dental implant placement. Here, we describe a novel CMF defect repair treatment consisting of TyroFill [E1001(1K)/dicalcium phosphate dihydrate (DCPD)] scaffolds supporting titanium dental implants. Human DPSC/HUVEC seeded constructs were grown in a critical-sized rabbit mandible defect for 1 or 3 months.  Micro-CT and histological/IHC analyses demonstrated that cell-seeded TyroFill constructs showed significant new bone formation around the implant, indicating the potential use of cell-seeded TyroFill scaffolds for coordinated bone-tooth defect repair. View this paper
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11 pages, 968 KiB  
Article
Improving the Accuracy of Otitis Media with Effusion Diagnosis in Pediatric Patients Using Deep Learning
Bioengineering 2023, 10(11), 1337; https://doi.org/10.3390/bioengineering10111337 - 20 Nov 2023
Viewed by 811
Abstract
Otitis media with effusion (OME), primarily seen in children aged 2 years and younger, is characterized by the presence of fluid in the middle ear, often resulting in hearing loss and aural fullness. While deep learning networks have been explored to aid OME [...] Read more.
Otitis media with effusion (OME), primarily seen in children aged 2 years and younger, is characterized by the presence of fluid in the middle ear, often resulting in hearing loss and aural fullness. While deep learning networks have been explored to aid OME diagnosis, prior work did not often specify if pediatric images were used for training, causing uncertainties about their clinical relevance, especially due to important distinctions between the tympanic membranes of small children and adults. We trained cross-validated ResNet50, DenseNet201, InceptionV3, and InceptionResNetV2 models on 1150 pediatric tympanic membrane images from otoendoscopes to classify OME. When assessed using a separate dataset of 100 pediatric tympanic membrane images, the models achieved mean accuracies of 92.9% (ResNet50), 97.2% (DenseNet201), 96.0% (InceptionV3), and 94.8% (InceptionResNetV2), compared to the seven otolaryngologists that achieved accuracies between 84.0% and 69.0%. The results showed that even the worst-performing model trained on fold 3 of InceptionResNetV2 with an accuracy of 88.0% exceeded the accuracy of the highest-performing otolaryngologist at 84.0%. Our findings suggest that these specifically trained deep learning models can potentially enhance the clinical diagnosis of OME using pediatric otoendoscopic tympanic membrane images. Full article
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16 pages, 3491 KiB  
Article
Unilateral Mitochondrial–Hemodynamic Coupling and Bilateral Connectivity in the Prefrontal Cortices of Young and Older Healthy Adults
Bioengineering 2023, 10(11), 1336; https://doi.org/10.3390/bioengineering10111336 - 20 Nov 2023
Viewed by 750
Abstract
A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial–hemodynamic coupling and bilateral connectivity in infraslow oscillation frequencies in young adults. The infraslow oscillation includes three distinct vasomotions [...] Read more.
A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial–hemodynamic coupling and bilateral connectivity in infraslow oscillation frequencies in young adults. The infraslow oscillation includes three distinct vasomotions with endogenic (E), neurogenic (N), and myogenic (M) frequency bands. The goal of this study was to prove the hypothesis that there are significant differences between young and older adults in the unilateral coupling (uCOP) and bilateral connectivity (bCON) in the prefrontal cortex. Accordingly, we performed measurements from 24 older adults (67.2 ± 5.9 years of age) using the same two-channel broadband near-infrared spectroscopy (bbNIRS) setup and resting-state experimental protocol as those in the recent study. After quantification of uCOP and bCON in three E/N/M frequencies and statistical analysis, we demonstrated that older adults had significantly weaker bilateral hemodynamic connectivity but significantly stronger bilateral metabolic connectivity than young adults in the M band. Furthermore, older adults exhibited significantly stronger unilateral coupling on both prefrontal sides in all E/N/M bands, particularly with a very large effect size in the M band (>1.9). These age-related results clearly support our hypothesis and were well interpreted following neurophysiological principles. The key finding of this paper is that the neurophysiological metrics of uCOP and bCON are highly associated with age and may have the potential to become meaningful features for human brain health and be translatable for future clinical applications, such as the early detection of Alzheimer’s disease. Full article
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11 pages, 761 KiB  
Article
Diagnostic Validation of the Screening Corneal Objective Risk of Ectasia Analyzer Evaluated by Swept Source Optical Coherence Tomography for Keratoconus in an Asian Population
Bioengineering 2023, 10(11), 1335; https://doi.org/10.3390/bioengineering10111335 - 20 Nov 2023
Viewed by 865
Abstract
We aimed to investigate the diagnostic accuracy of Screening Corneal Objective Risk of Ectasia (SCORE) Analyzer software using ANTERION, a swept-source optical coherence tomography device, for keratoconus diagnosis in an Asian population. A total of 151 eyes of 151 patients were included in [...] Read more.
We aimed to investigate the diagnostic accuracy of Screening Corneal Objective Risk of Ectasia (SCORE) Analyzer software using ANTERION, a swept-source optical coherence tomography device, for keratoconus diagnosis in an Asian population. A total of 151 eyes of 151 patients were included in this retrospective study as follows: 60, 45, and 46 keratoconus, keratoconus suspects, and normal control eyes, respectively. Parameters in the SCORE calculation, including six indices, were compared for the three groups. The receiver operating characteristic curve analysis and cut-off value were estimated to assess the diagnostic ability to differentiate keratoconus and keratoconus suspect eyes from the normal group. The SCORE value and six indices were significantly correlated—“AntK max” (R = 0.864), “AntK oppoK” (R = 0.866), “Ant inf supK” (R = 0.943), “Ant irre 3mm” (R = 0.741), “post elevation at the thinnest point” (R = 0.943), and “minimum corneal thickness” (R = −0.750). The SCORE value showed high explanatory power (98.1%), sensitivity of 81.9%, and specificity of 78.3% (cut-off value: 0.25) in diagnosing normal eyes from the keratoconus suspect and keratoconus eyes. The SCORE Analyzer was found to be valid and consistent, showing good sensitivity and specificity for keratoconus detection in an Asian population. Full article
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12 pages, 2978 KiB  
Article
Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
Bioengineering 2023, 10(11), 1334; https://doi.org/10.3390/bioengineering10111334 - 20 Nov 2023
Viewed by 846
Abstract
Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This [...] Read more.
Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future. Full article
(This article belongs to the Section Regenerative Engineering)
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11 pages, 3728 KiB  
Article
SpineHRformer: A Transformer-Based Deep Learning Model for Automatic Spine Deformity Assessment with Prospective Validation
Bioengineering 2023, 10(11), 1333; https://doi.org/10.3390/bioengineering10111333 - 20 Nov 2023
Viewed by 859
Abstract
The Cobb angle (CA) serves as the principal method for assessing spinal deformity, but manual measurements of the CA are time-consuming and susceptible to inter- and intra-observer variability. While learning-based methods, such as SpineHRNet+, have demonstrated potential in automating CA measurement, their accuracy [...] Read more.
The Cobb angle (CA) serves as the principal method for assessing spinal deformity, but manual measurements of the CA are time-consuming and susceptible to inter- and intra-observer variability. While learning-based methods, such as SpineHRNet+, have demonstrated potential in automating CA measurement, their accuracy can be influenced by the severity of spinal deformity, image quality, relative position of rib and vertebrae, etc. Our aim is to create a reliable learning-based approach that provides consistent and highly accurate measurements of the CA from posteroanterior (PA) X-rays, surpassing the state-of-the-art method. To accomplish this, we introduce SpineHRformer, which identifies anatomical landmarks, including the vertices of endplates from the 7th cervical vertebra (C7) to the 5th lumbar vertebra (L5) and the end vertebrae with different output heads, enabling the calculation of CAs. Within our SpineHRformer, a backbone HRNet first extracts multi-scale features from the input X-ray, while transformer blocks extract local and global features from the HRNet outputs. Subsequently, an output head to generate heatmaps of the endplate landmarks or end vertebra landmarks facilitates the computation of CAs. We used a dataset of 1934 PA X-rays with diverse degrees of spinal deformity and image quality, following an 8:2 ratio to train and test the model. The experimental results indicate that SpineHRformer outperforms SpineHRNet+ in landmark detection (Mean Euclidean Distance: 2.47 pixels vs. 2.74 pixels), CA prediction (Pearson correlation coefficient: 0.86 vs. 0.83), and severity grading (sensitivity: normal-mild; 0.93 vs. 0.74, moderate; 0.74 vs. 0.77, severe; 0.74 vs. 0.7). Our approach demonstrates greater robustness and accuracy compared to SpineHRNet+, offering substantial potential for improving the efficiency and reliability of CA measurements in clinical settings. Full article
(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)
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18 pages, 3655 KiB  
Article
Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency
Bioengineering 2023, 10(11), 1332; https://doi.org/10.3390/bioengineering10111332 - 19 Nov 2023
Cited by 2 | Viewed by 991
Abstract
In the advancement of medical image super-resolution (SR), the Deep Residual Feature Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This work presents DRFDCAN, a model that innovates traditional SR approaches by introducing a channel attention block that is tailored for [...] Read more.
In the advancement of medical image super-resolution (SR), the Deep Residual Feature Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This work presents DRFDCAN, a model that innovates traditional SR approaches by introducing a channel attention block that is tailored for high-frequency features—crucial for the nuanced details in medical diagnostics—while streamlining the network structure for enhanced computational efficiency. DRFDCAN’s architecture adopts a residual-within-residual design to facilitate faster inference and reduce memory demands without compromising the integrity of the image reconstruction. This design strategy, combined with an innovative feature extraction method that emphasizes the utility of the initial layer features, allows for improved image clarity and is particularly effective in optimizing the peak signal-to-noise ratio (PSNR). The proposed work redefines efficiency in SR models, outperforming established frameworks like RFDN by improving model compactness and accelerating inference. The meticulous crafting of a feature extractor that effectively captures edge and texture information exemplifies the model’s capacity to render detailed images, necessary for accurate medical analysis. The implications of this study are two-fold: it presents a viable solution for deploying SR technology in real-time medical applications, and it sets a precedent for future models that address the delicate balance between computational efficiency and high-fidelity image reconstruction. This balance is paramount in medical applications where the clarity of images can significantly influence diagnostic outcomes. The DRFDCAN model thus stands as a transformative contribution to the field of medical image super-resolution. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Medical Image Processing)
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11 pages, 1637 KiB  
Article
Evaluation of the Impact of Calcium Silicate-Based Sealer Insertion Technique on Root Canal Obturation Quality: A Micro-Computed Tomography Study
Bioengineering 2023, 10(11), 1331; https://doi.org/10.3390/bioengineering10111331 - 19 Nov 2023
Viewed by 767
Abstract
Background: Calcium silicate-based sealers have gained in popularity over time due to their physicochemical/biological properties and their possible use with single-cone obturation. The single cone technique is a sealer-based obturation and there is still a knowledge gap regarding the potential impact of the [...] Read more.
Background: Calcium silicate-based sealers have gained in popularity over time due to their physicochemical/biological properties and their possible use with single-cone obturation. The single cone technique is a sealer-based obturation and there is still a knowledge gap regarding the potential impact of the sealer insertion method on the root canal-filling quality. Therefore, the aim of this micro-CT study was to assess the impact of the calcium silicate-based sealer insertion technique on void occurrence and on the sealer extrusion following single-cone obturation. Methods: Thirty-six single-rooted mandibular premolars with one canal were shaped with Reciproc® R25 (VDW, Munich, Germany) then divided randomly into four groups of nine canals, each depending on the TotalFill® BC Sealer insertion technique used with single cone obturation: injection in the coronal two-thirds (group A); injection in the coronal two-thirds followed by direct sonic activation (group B); injection in the coronal two-thirds followed by indirect ultrasonic activation on tweezers (group C); sealer applied only on the master-cone (control group D). Samples were then scanned using micro-CT for voids and sealer extrusion calculation. Data were statistically analyzed using v.26 IBM; Results: No statistically significant differences were noted between the four groups in terms of voids; nevertheless, sonic activation (group B) followed by ultrasonic activation on the tweezers (group C) showed the best results (p = 0.066). Group D showed significantly less sealer extrusion when compared with group C (p = 0.044), with no statistically significant differences between groups D, A and B (p > 0.05). Conclusions: Despite no significant differences observed between the different sealer placement techniques, the use of sonic and ultrasonic activation might be promising to reduce void occurrence. Further investigations are needed to demonstrate the potential benefit of calcium silicate-based sealer activation especially in wide and oval root canals in order to improve the quality of the single-cone obturation. Full article
(This article belongs to the Section Regenerative Engineering)
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12 pages, 9407 KiB  
Article
Functional Load Capacity of Teeth with Reduced Periodontal Support: A Finite Element Analysis
Bioengineering 2023, 10(11), 1330; https://doi.org/10.3390/bioengineering10111330 - 18 Nov 2023
Cited by 1 | Viewed by 1042
Abstract
The purpose of this study was to investigate the functional load capacity of the periodontal ligament (PDL) in a full arch maxilla and mandible model using a numerical simulation. The goal was to determine the functional load pattern in multi- and single-rooted teeth [...] Read more.
The purpose of this study was to investigate the functional load capacity of the periodontal ligament (PDL) in a full arch maxilla and mandible model using a numerical simulation. The goal was to determine the functional load pattern in multi- and single-rooted teeth with full and reduced periodontal support. CBCT data were used to create 3D models of a maxilla and mandible. The DICOM dataset was used to create a CAD model. For a precise description of the surfaces of each structure (enamel, dentin, cementum, pulp, PDL, gingiva, bone), each tooth was segmented separately, and the biomechanical characteristics were considered. Finite Element Analysis (FEA) software computed the biomechanical behavior of the stepwise increased force of 700 N in the cranial and 350 N in the ventral direction of the muscle approach of the masseter muscle. The periodontal attachment (cementum–PDL–bone contact) was subsequently reduced in 1 mm increments, and the simulation was repeated. Quantitative (pressure, tension, and deformation) and qualitative (color-coded images) data were recorded and descriptively analyzed. The teeth with the highest load capacities were the upper and lower molars (0.4–0.6 MPa), followed by the premolars (0.4–0.5 MPa) and canines (0.3–0.4 MPa) when vertically loaded. Qualitative data showed that the areas with the highest stress in the PDL were single-rooted teeth in the cervical and apical area and molars in the cervical and apical area in addition to the furcation roof. In both single- and multi-rooted teeth, the gradual reduction in bone levels caused an increase in the load on the remaining PDL. Cervical and apical areas, as well as the furcation roof, are the zones with the highest functional stress. The greater the bone loss, the higher the mechanical load on the residual periodontal supporting structures. Full article
(This article belongs to the Special Issue Computational Biomechanics, Volume II)
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18 pages, 3009 KiB  
Article
Genomic Insights on the Carbon-Negative Workhorse: Systematical Comparative Genomic Analysis on 56 Synechococcus Strains
Bioengineering 2023, 10(11), 1329; https://doi.org/10.3390/bioengineering10111329 - 18 Nov 2023
Viewed by 1196
Abstract
Synechococcus, a type of ancient photosynthetic cyanobacteria, is crucial in modern carbon-negative synthetic biology due to its potential for producing bioenergy and high-value products. With its high biomass, fast growth rate, and established genetic manipulation tools, Synechococcus has become a research focus [...] Read more.
Synechococcus, a type of ancient photosynthetic cyanobacteria, is crucial in modern carbon-negative synthetic biology due to its potential for producing bioenergy and high-value products. With its high biomass, fast growth rate, and established genetic manipulation tools, Synechococcus has become a research focus in recent years. Abundant germplasm resources have been accumulated from various habitats, including temperature and salinity conditions relevant to industrialization. In this study, a comprehensive analysis of complete genomes of the 56 Synechococcus strains currently available in public databases was performed, clarifying genetic relationships, the adaptability of Synechococcus to the environment, and its reflection at the genomic level. This was carried out via pan-genome analysis and a detailed comparison of the functional gene groups. The results revealed an open-genome pattern, with 275 core genes and variable genome sizes within these strains. The KEGG annotation and orthology composition comparisons unveiled that the cold and thermophile strains have 32 and 84 unique KO functional units in their shared core gene functional units, respectively. Each KO functional unit reflects unique gene families and pathways. In terms of salt tolerance and comparative genomics, there are 65 unique KO functional units in freshwater-adapted strains and 154 in strictly marine strains. By delving into these aspects, our understanding of the metabolic potential of Synechococcus was deepened, promoting the development and industrial application of cyanobacterial biotechnology. Full article
(This article belongs to the Section Biochemical Engineering)
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31 pages, 8260 KiB  
Review
Exo Supportive Devices: Summary of Technical Aspects
Bioengineering 2023, 10(11), 1328; https://doi.org/10.3390/bioengineering10111328 - 17 Nov 2023
Viewed by 1042
Abstract
Human societies have been trying to mitigate the suffering of individuals with physical impairments, with a special effort in the last century. In the 1950s, a new concept arose, finding similarities between animal exoskeletons, and with the goal of medically aiding human movement [...] Read more.
Human societies have been trying to mitigate the suffering of individuals with physical impairments, with a special effort in the last century. In the 1950s, a new concept arose, finding similarities between animal exoskeletons, and with the goal of medically aiding human movement (for rehabilitation applications). There have been several studies on using exosuits with this purpose in mind. So, the current review offers a critical perspective and a detailed analysis of the steps and key decisions involved in the conception of an exoskeleton. Choices such as design aspects, base materials (structure), actuators (force and motion), energy sources (actuation), and control systems will be discussed, pointing out their advantages and disadvantages. Moreover, examples of exosuits (full-body, upper-body, and lower-body devices) will be presented and described, including their use cases and outcomes. The future of exoskeletons as possible assisted movement solutions will be discussed—pointing to the best options for rehabilitation. Full article
(This article belongs to the Special Issue Bioengineering for Physical Rehabilitation)
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19 pages, 7438 KiB  
Article
Evaluation of Cytocompatibility of PEEK-Based Composites as a Function of Manufacturing Processes
Bioengineering 2023, 10(11), 1327; https://doi.org/10.3390/bioengineering10111327 - 17 Nov 2023
Viewed by 723
Abstract
The biocompatible polymer polyetheretherketone (PEEK) is a suitable candidate to be part of potential all-polymer total joint replacements, provided its use is associated with better osseointegration, mechanical performance, and wear resistance. Seeking to meet the aforementioned requirements, respectively, we have manufactured a PEEK [...] Read more.
The biocompatible polymer polyetheretherketone (PEEK) is a suitable candidate to be part of potential all-polymer total joint replacements, provided its use is associated with better osseointegration, mechanical performance, and wear resistance. Seeking to meet the aforementioned requirements, respectively, we have manufactured a PEEK composite with different fillers: carbon fibers (CF), hydroxyapatite particles (HA) and graphene platelets (GNP). The mechanical outcomes of the composites with combinations of 0, 1.5, 3.0 wt% GNP, 5 and 15 wt% HA and 30% of wt% CF concentrations pointed out that one of the best filler combinations to achieve the previous objectives was 30 wt% CF, 8 wt% HA and 2 wt% of GNP. The study compares the bioactivity of human osteoblasts on this composite prepared by injection molding with that on the material manufactured by the Fused Filament Fabrication 3D additive technique. The results indicate that the surface adhesion and proliferation of human osteoblasts over time are better with the composite obtained by injection molding than that obtained by 3D printing. This result is more closely correlated with morphological parameters of the composite surface than its wettability behavior. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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15 pages, 8210 KiB  
Article
Novel Procedure for Automatic Registration between Cone-Beam Computed Tomography and Intraoral Scan Data Supported with 3D Segmentation
Bioengineering 2023, 10(11), 1326; https://doi.org/10.3390/bioengineering10111326 - 17 Nov 2023
Viewed by 815
Abstract
In contemporary practice, intraoral scans and cone-beam computed tomography (CBCT) are widely adopted techniques for tooth localization and the acquisition of comprehensive three-dimensional models. Despite their utility, each dataset presents inherent merits and limitations, prompting the pursuit of an amalgamated solution for optimization. [...] Read more.
In contemporary practice, intraoral scans and cone-beam computed tomography (CBCT) are widely adopted techniques for tooth localization and the acquisition of comprehensive three-dimensional models. Despite their utility, each dataset presents inherent merits and limitations, prompting the pursuit of an amalgamated solution for optimization. Thus, this research introduces a novel 3D registration approach aimed at harmonizing these distinct datasets to offer a holistic perspective. In the pre-processing phase, a retrained Mask-RCNN is deployed on both sagittal and panoramic projections to partition upper and lower teeth from the encompassing CBCT raw data. Simultaneously, a chromatic classification model is proposed for segregating gingival tissue from tooth structures in intraoral scan data. Subsequently, the segregated datasets are aligned based on dental crowns, employing the robust RANSAC and ICP algorithms. To assess the proposed methodology’s efficacy, the Euclidean distance between corresponding points is statistically evaluated. Additionally, dental experts, including two orthodontists and an experienced general dentist, evaluate the clinical potential by measuring distances between landmarks on tooth surfaces. The computed error in corresponding point distances between intraoral scan data and CBCT data in the automatically registered datasets utilizing the proposed technique is quantified at 0.234 ± 0.019 mm, which is significantly below the 0.3 mm CBCT voxel size. Moreover, the average measurement discrepancy among expert-identified landmarks ranges from 0.368 to 1.079 mm, underscoring the promise of the proposed method. Full article
(This article belongs to the Section Biomedical Engineering and Biomaterials)
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12 pages, 2823 KiB  
Article
Speech Perception Improvement Algorithm Based on a Dual-Path Long Short-Term Memory Network
Bioengineering 2023, 10(11), 1325; https://doi.org/10.3390/bioengineering10111325 - 16 Nov 2023
Viewed by 728
Abstract
Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, [...] Read more.
Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, this paper introduces a speech enhancement model designed with a dual-path structure that identifies key speech characteristics in both the time and time–frequency domains. Specifically, the time path aims to model semantic features hidden in the waveform, while the time–frequency path attempts to compensate for the spectral details via a spectral extension block. These two paths enhance temporal and spectral features via mask functions modeled as LSTM, respectively, offering a comprehensive approach to speech enhancement. Experimental results show that the proposed dual-path LSTM network consistently outperforms conventional single-domain speech enhancement methods in terms of speech quality and intelligibility. Full article
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18 pages, 4633 KiB  
Article
Gesture Classification in Electromyography Signals for Real-Time Prosthetic Hand Control Using a Convolutional Neural Network-Enhanced Channel Attention Model
Bioengineering 2023, 10(11), 1324; https://doi.org/10.3390/bioengineering10111324 - 16 Nov 2023
Viewed by 839
Abstract
Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module [...] Read more.
Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module improves the model’s capacity to extract features and focus on critical information in the sEMG data, thus simultaneously equipping the sEMG-controlled prosthetic hand systems with the characteristics of accurate gesture detection and real-time control. Furthermore, we suggest a preprocessing strategy for extracting envelope signals that incorporates Butterworth low-pass filtering and the fast Hilbert transform (FHT), which can successfully reduce noise interference and capture essential physiological information. Finally, the majority voting window technique is adopted to enhance the prediction results, further improving the accuracy and stability of the model. Overall, our multi-layered convolutional neural network model, in conjunction with envelope signal extraction and attention mechanisms, offers a promising and innovative approach for real-time control systems in prosthetic hands, allowing for precise fine motor actions. Full article
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21 pages, 371 KiB  
Review
Effects of Plasma Treatment on the Strength of Bonding to Ceramic Surfaces in Orthodontics—A Comprehensive Review
Bioengineering 2023, 10(11), 1323; https://doi.org/10.3390/bioengineering10111323 - 16 Nov 2023
Viewed by 746
Abstract
Over the past several decades, orthodontic treatment has been increasingly sought out by adults, many of whom have undergone restorative dental procedures that cover enamel. Because the characteristics of restorative materials differ from those of enamel, typical bonding techniques do not yield excellent [...] Read more.
Over the past several decades, orthodontic treatment has been increasingly sought out by adults, many of whom have undergone restorative dental procedures that cover enamel. Because the characteristics of restorative materials differ from those of enamel, typical bonding techniques do not yield excellent restoration–bracket bonding strengths. Plasma treatment is an emerging surface treatment that could potentially improve bonding properties. The purpose of this paper is to evaluate currently available studies assessing the effect of plasma treatment on the shear bond strength (SBS) and failure mode of resin cement/composite on the surface of ceramic materials. PubMed and Google Scholar databases were searched for relevant studies, which were categorized by restorative material and plasma treatment types that were evaluated. It was determined that cold atmospheric plasma (CAP) treatment using helium and H2O gas was effective at raising the SBS of feldspathic porcelain to a bonding agent, while CAP treatment using helium gas might also be a potential treatment method for zirconia and other types of ceramics. More importantly, CAP treatment using helium has the potential for being carried out chairside due to its non-toxicity, low temperature, and short treatment time. However, because all the studies were conducted in vitro and not tested in an orthodontic setting, further research must be conducted to ascertain the effectiveness of specific plasma treatments in comparison to current orthodontic bonding treatments in vivo. Full article
(This article belongs to the Special Issue Application of Bioengineering to Clinical Orthodontics)
12 pages, 7066 KiB  
Article
Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification
Bioengineering 2023, 10(11), 1322; https://doi.org/10.3390/bioengineering10111322 - 16 Nov 2023
Viewed by 819
Abstract
In recent decades, the incidence of melanoma has grown rapidly. Hence, early diagnosis is crucial to improving clinical outcomes. Here, we propose and compare a classical image analysis-based machine learning method with a deep learning one to automatically classify benign vs. malignant dermoscopic [...] Read more.
In recent decades, the incidence of melanoma has grown rapidly. Hence, early diagnosis is crucial to improving clinical outcomes. Here, we propose and compare a classical image analysis-based machine learning method with a deep learning one to automatically classify benign vs. malignant dermoscopic skin lesion images. The same dataset of 25,122 publicly available dermoscopic images was used to train both models, while a disjointed test set of 200 images was used for the evaluation phase. The training dataset was randomly divided into 10 datasets of 19,932 images to obtain an equal distribution between the two classes. By testing both models on the disjoint set, the deep learning-based method returned accuracy of 85.4 ± 3.2% and specificity of 75.5 ± 7.6%, while the machine learning one showed accuracy and specificity of 73.8 ± 1.1% and 44.5 ± 4.7%, respectively. Although both approaches performed well in the validation phase, the convolutional neural network outperformed the ensemble boosted tree classifier on the disjoint test set, showing better generalization ability. The integration of new melanoma detection algorithms with digital dermoscopic devices could enable a faster screening of the population, improve patient management, and achieve better survival rates. Full article
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14 pages, 842 KiB  
Systematic Review
Non-Invasive Wearable Devices for Monitoring Vital Signs in Patients with Type 2 Diabetes Mellitus: A Systematic Review
Bioengineering 2023, 10(11), 1321; https://doi.org/10.3390/bioengineering10111321 - 16 Nov 2023
Viewed by 1233
Abstract
Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the [...] Read more.
Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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14 pages, 661 KiB  
Review
Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review
Bioengineering 2023, 10(11), 1320; https://doi.org/10.3390/bioengineering10111320 - 16 Nov 2023
Viewed by 795
Abstract
This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus [...] Read more.
This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus on investigating cells’ and molecules’ interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors’ microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes. Full article
(This article belongs to the Special Issue Mathematical and Computational Modeling of Cancer Progression)
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15 pages, 3645 KiB  
Article
Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework
Bioengineering 2023, 10(11), 1319; https://doi.org/10.3390/bioengineering10111319 - 16 Nov 2023
Viewed by 763
Abstract
This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net [...] Read more.
This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net decoder, batch normalization, attention block and deepened convolution layer based on the U-Net backbone. Then, a VABC-UNet-based assessment framework was established for automatic segmentation, classification, and evaluation of valvular regurgitations. A total of 315 color Doppler echocardiography images of MR and/or TR in an apical four-chamber view were collected, including 35 images in the test dataset and 280 images in the training dataset. In comparison with the classic U-Net and VGG16-UNet models, the segmentation performance of the VABC-UNet model was evaluated via four metrics: Dice, Jaccard, Precision, and Recall. According to the features of regurgitation jet and atrium, the regurgitation could automatically be classified into MR or TR, and evaluated to mild, moderate, moderate–severe, or severe grade by the framework. The results show that the VABC-UNet model has a superior performance in the segmentation of valvular regurgitation jets and atria to the other two models and consequently a higher accuracy of classification and evaluation. There were fewer pseudo- and over-segmentations by the VABC-UNet model and the values of the metrics significantly improved (p < 0.05). The proposed VABC-UNet-based framework achieves automatic segmentation, classification, and evaluation of MR and TR, having potential to assist radiologists in clinical decision making of the regurgitations in valvular heart diseases. Full article
(This article belongs to the Special Issue Artificial Intelligence in Auto-Diagnosis and Clinical Applications)
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15 pages, 3212 KiB  
Article
Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model
Bioengineering 2023, 10(11), 1318; https://doi.org/10.3390/bioengineering10111318 - 15 Nov 2023
Viewed by 990
Abstract
Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since [...] Read more.
Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics. Full article
(This article belongs to the Section Biosignal Processing)
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14 pages, 3678 KiB  
Article
Quantitative Evaluation of Caries and Calculus with Ultrahigh-Resolution Optical Coherence Tomography
Bioengineering 2023, 10(11), 1317; https://doi.org/10.3390/bioengineering10111317 - 15 Nov 2023
Viewed by 999
Abstract
Dental caries on the crown’s surface is caused by the interaction of bacteria and carbohydrates, which then gradually alter the tooth’s structure. In addition, calculus is the root of periodontal disease. Optical coherence tomography (OCT) has been considered to be a promising tool [...] Read more.
Dental caries on the crown’s surface is caused by the interaction of bacteria and carbohydrates, which then gradually alter the tooth’s structure. In addition, calculus is the root of periodontal disease. Optical coherence tomography (OCT) has been considered to be a promising tool for identifying dental caries; however, diagnosing dental caries in the early stage still remains challenging. In this study, we proposed an ultrahigh-resolution OCT (UHR-OCT) system with axial and transverse resolutions of 2.6 and 1.8 μm for differentiating the early-stage dental caries and calculus. The same teeth were also scanned by a conventional spectral-domain OCT (SD-OCT) system with an axial resolution of 7 μm. The results indicated that early-stage carious structures such as small cavities can be observed using UHR-OCT; however, the SD-OCT system with a lower resolution had difficulty identifying it. Moreover, the estimated surface roughness and the scattering coefficient of enamel were proposed for quantitatively differentiating the different stages of caries. Furthermore, the thickness of the calculus can be estimated from the UHR-OCT results. The results have demonstrated that UHR-OCT can detect caries and calculus in their early stages, showing that the proposed method for the quantitative evaluation of caries and calculus is potentially promising. Full article
(This article belongs to the Special Issue Optical Techniques for Biomedical Engineering)
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19 pages, 3452 KiB  
Article
Towards a Corpus (and Language)-Independent Screening of Parkinson’s Disease from Voice and Speech through Domain Adaptation
Bioengineering 2023, 10(11), 1316; https://doi.org/10.3390/bioengineering10111316 - 15 Nov 2023
Viewed by 1068
Abstract
End-to-end deep learning models have shown promising results for the automatic screening of Parkinson’s disease by voice and speech. However, these models often suffer degradation in their performance when applied to scenarios involving multiple corpora. In addition, they also show corpus-dependent clusterings. These [...] Read more.
End-to-end deep learning models have shown promising results for the automatic screening of Parkinson’s disease by voice and speech. However, these models often suffer degradation in their performance when applied to scenarios involving multiple corpora. In addition, they also show corpus-dependent clusterings. These facts indicate a lack of generalisation or the presence of certain shortcuts in the decision, and also suggest the need for developing new corpus-independent models. In this respect, this work explores the use of domain adversarial training as a viable strategy to develop models that retain their discriminative capacity to detect Parkinson’s disease across diverse datasets. The paper presents three deep learning architectures and their domain adversarial counterparts. The models were evaluated with sustained vowels and diadochokinetic recordings extracted fr