ORCID Profile
0000-0001-5905-1572
Current Organisations
Hospital Universiti Sains Malaysia
,
The University of Adelaide Faculty of Health and Medical Sciences
,
Universiti Sains Malaysia - Kampus Kesihatan
,
University of Adelaide
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Publisher: Public Library of Science (PLoS)
Date: 13-09-2023
Publisher: Medical Communications Sp. z.o.o.
Date: 29-11-2019
Publisher: Oxford University Press (OUP)
Date: 11-10-2023
DOI: 10.1093/EJO/CJAD061
Publisher: Elsevier BV
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 20-10-2020
Publisher: Springer Science and Business Media LLC
Date: 28-01-2023
DOI: 10.1038/S41598-023-28442-1
Abstract: The current multiphase, invitro study developed and validated a 3-dimensional convolutional neural network (3D-CNN) to generate partial dental crowns (PDC) for use in restorative dentistry. The effectiveness of desktop laser and intraoral scanners in generating data for the purpose of 3D-CNN was first evaluated (phase 1). There were no significant differences in surface area [t-stat(df) = − 0.01 (10), mean difference = − 0.058, P 0.99] and volume [t-stat(df) = 0.357(10)]. However, the intraoral scans were chosen for phase 2 as they produced a greater level of volumetric details (343.83 ± 43.52 mm 3 ) compared to desktop laser scanning (322.70 ± 40.15 mm 3 ). In phase 2, 120 tooth preparations were digitally synthesized from intraoral scans, and two clinicians designed the respective PDCs using computer-aided design (CAD) workflows on a personal computer setup. Statistical comparison by 3-factor ANOVA demonstrated significant differences in surface area ( P 0.001), volume ( P 0.001), and spatial overlap ( P 0.001), and therefore only the most accurate PDCs (n = 30) were picked to train the neural network (Phase 3). The current 3D-CNN produced a validation accuracy of 60%, validation loss of 0.68–0.87, sensitivity of 1.00, precision of 0.50–0.83, and serves as a proof-of-concept that 3D-CNN can predict and generate PDC prostheses in CAD for restorative dentistry.
Publisher: Springer Science and Business Media LLC
Date: 29-12-2022
Publisher: Springer Science and Business Media LLC
Date: 05-10-2023
Publisher: MDPI AG
Date: 16-05-2023
DOI: 10.3390/JPM13050840
Abstract: Objective. To investigate the influence of endogenous and exogenous neuroendocrine analogues on the range and motion of jaw movement, mandibular growth, and factors affecting condylar guidance in patients with temporomandibular joint disorders using clinical assessment and radiographic imaging. Material and Methods. Eligible articles were extracted from eleven databases in early 2023 and screened following PRISMA protocols. Certainty of evidence and potential biases were assessed using the GRADE approach. Results. Nineteen articles were screened, with four deemed to be of high quality, eight of moderate quality, and the remaining seven of low to very low quality. Corticosteroids improve maximal incisal opening but not TMJ disorder symptoms. Higher doses worsen jaw movement and cause osseous deformity. Growth hormone affects occlusal development, and delayed treatment affects arch width. Sex hormone correlation with TMJ disorder is complex, with some studies showing a correlation between menstrual cycle phases and pain/limited mobility. Conclusions. The evaluation of neuroendocrine influencers in relation to jaw movement in patients with temporomandibular joint disorders involves the complex interplay of potentially confounding factors that each require careful consideration to ensure accurate diagnoses and evaluations.
Publisher: Elsevier BV
Date: 06-0007
DOI: 10.1016/J.JORMAS.2019.10.003
Abstract: A systematic review was conducted in early 2019 to evaluate the articles published that dealt with digital workflow, CAD, rapid prototyping and digital image processing in the rehabilitation by maxillofacial prosthetics. The objective of the review was to primarily identify the recorded cases of orofacial rehabilitation made by maxillofacial prosthetics using computer assisted 3D printing. Secondary objectives were to analyze the methods of data acquisition recorded with challenges and limitations documented with various software in the workflow. Articles were searched from Scopus, PubMed and Google Scholar based on the predetermined eligibility criteria. Thirty-nine selected papers from 1992 to 2019 were then read and categorized according to type of prosthesis described in the papers. For nasal prostheses, Common Methods of data acquisition mentioned were computed tomography, photogrammetry and laser scanners. After image processing, computer aided design (CAD) was used to design and merge the prosthesis to the peripheral healthy tissue. Designing and printing the mold was more preferred. Moisture and muscle movement affected the overall fit especially for prostheses directly designed and printed. For auricular prostheses, laser scanning was most preferred. For unilateral defects, CAD was used to mirror the healthy tissue over to the defect side. Authors emphasized on the need of digital library for prostheses selection, especially for bilateral defects. Printing the mold and conventionally creating the prosthesis was most preferred due to issues of proper fit and color matching. Orbital prostheses follow a similar workflow as auricular prosthesis. 3D photogrammetry and laser scans were more preferred and directly printing the prosthesis was favored in various instance. However, ocular prostheses fabrication was recorded to be a challenge due to difficulties in appropriate volume reconstruction and inability to mirror healthy globe. Only successful cases of digitally designed and printed iris were noted.
Publisher: SAGE Publications
Date: 18-08-2021
Abstract: The virtual cone beam computed tomography–derived 3-dimensional model was compared with the scanned conventional model used in the fabrication of a palatal obturator for a patient with a large palatal defect. A digitally derived 3-dimensional maxillary model incorporating the palatal defect was generated from the patient’s existing cone beam computerized tomography data and compared with the scanned cast from the conventional impression for linear dimensions, area, and volume. The digitally derived cast was 3-dimensionally printed and the obturator fabricated using traditional techniques. Similarly, an obturator was fabricated from the conventional cast and the fit of both final obturator bulbs were compared in vivo. The digitally derived model produced more accurate volumes and surface areas within the defect. The defect margins and peripheries were overestimated which was reflected clinically. The digitally derived model provided advantages in the fabrication of the palatal obturator however, further clinical research is required to refine consistency.
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.PROSDENT.2020.07.039
Abstract: Computer-aided design (CAD) of maxillofacial prostheses is a hardware-intensive process. The greater the mesh detail is, the more processing power is required from the computer. A reduction in mesh quality has been shown to reduce workload on computers, yet no reference value of reduction is present for intraoral prostheses that can be applied during the design. The purpose of this simulation study was to establish a reference percentage value that can be used to effectively reduce the size and polygons of the 3D mesh without drastically affecting the dimensions of the prosthesis itself. Fifteen different maxillary palatal defects were simulated on a dental cast and scanned to create 3D casts. Digital bulbs were fabricated from the casts. Conventional bulbs for the defects were fabricated, scanned, and compared with the digital bulb to serve as a control. The polygon parameters of digital bulbs were then reduced by different percentages (75%, 50%, 25%, 10%, 5%, and 1% of the original mesh) which created a total of 105 meshes across 7 mesh groups. The reduced mesh files were compared in idually with the original design in an open-source point cloud comparison software program. The parameters of comparison used in this study were Hausdorff distance (HD), Dice similarity coefficient (DSC), and volume. The reduction in file size was directly proportional to the amount of mesh reduction. There were minute yet insignificant differences in volume (P>.05) across all mesh groups, with significant differences (P<.001) in HD. The differences were, however, only found with DB1. DSC showed a progressive dissimilarity until DB25 (0.17%), after which the increase was more prominent (0.46% to 4.02%). A reduction of up to 75% polygons (25% of the original mesh) was effectively carried out on simulated casts without substantially affecting the amount of similarity in volume and geometry.
Publisher: MDPI AG
Date: 27-10-2023
DOI: 10.3390/DJ11110250
Publisher: Public Library of Science (PLoS)
Date: 29-08-2022
DOI: 10.1371/JOURNAL.PONE.0273029
Abstract: The study aimed to evaluate 1) the amount of color variations presents within clinical images of maxillofacial prosthetic silicone specimens when photographed under different clinically relevant ambient lighting conditions, and 2) whether white balance calibration (WBC) methods were able to mitigate variations in ambient lighting. 432 measurements were acquired from standardized images of the pigmented prosthetic silicone specimens within different ambient lighting conditions (i.e., 2 windowed and 2 windowless clinics) at noon with no light modifying apparatus. The specimens were photographed once without any white balance calibration (raw), then independently alongside an 18% neutral gray card and Macbeth color chart for calibration in a post-processing (PPWBC) software, and once after camera calibration (CWBC) using a gray card. The LAB color values were extracted from the images and color variations (ΔE) were calculated after referring to the corresponding spectrophotometric values as control. Images in windowless and windowed clinics exhibited highly significant differences (p 0.001) with spectrophotometer (control). CWBC demonstrated no significant differences (p 0.05) in LAB values across windowed clinics. PPWBC using Macbeth color chart produced no significant differences for a* values (p 0.05) across all clinics while PPWBC by gray card showed no significant differences (p 0.05) in LAB values when only similar clinics (either windowed or windowless) were compared. Significant color variations were present for maxillofacial prosthetic specimens owing to natural ambient light. CWBC and PPWBC using color charts were more suitable for color correction across windowed clinics while CWBC and PPWBC using gray cards had better outcomes across windowless setups.
Publisher: MDPI AG
Date: 12-07-2020
Abstract: Although numerous studies have demonstrated the benefits of incorporating filler particles into maxillofacial silicone elastomer (MFPSE), a review of the types, concentrations and effectiveness of the particles themselves was lacking. The purpose of this systematic review and meta-analysis was to review the effect of different types of filler particles on the mechanical properties of MFPSE. The properties in question were (1) tensile strength, (2) tear strength, (3) hardness, and (4) elongation at break. The findings of this study can assist operators, technicians and clinicians in making relevant decisions regarding which type of fillers to incorporate based on their needs. The systematic review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 original articles from 1970 to 2019 were selected from the databases, based on predefined eligibility criteria by two reviewers. The meta-analyses of nine papers were carried out by extracting data from the systematic review based on scoring criteria and processed using Cochrane Review Manager 5.3. Overall, there were significant differences favoring filler particles when incorporated into MFPSE. Nano fillers (69.23% of all studies) demonstrated superior comparative outcomes for tensile strength (P 0.0001), tear strength (P 0.00001), hardness (P 0.00001) and elongation at break (P 0.00001) when compared to micro fillers (30.76% of all studies). Micro fillers demonstrated inconsistent outcomes in mechanical properties, and meta-analysis of elongation at break argued against (P 0.01) their use. Current findings suggest that 1.5% ZrSiO4, 3% SiO2, 1.5% Y2O3, 2–6% TiO2, 2–2.5% ZnO, 2–2.5% CeO2, 0.5% TiSiO4 and 1% Ag-Zn Zeolite can be used to reinforce MFPSE, and help the materials better withstand mechanical degradation.
Publisher: MDPI AG
Date: 04-04-2023
DOI: 10.3390/ORAL3020016
Abstract: The current study aimed to implement and validate an automation system to detect carious lesions from smartphone images using different one-stage deep learning techniques. 233 images of carious lesions were captured using a smartphone camera system at 1432 × 1375 pixels, then classified and screened according to a visual caries classification index. Following data augmentation, the YOLO v5 model for object detection was used. After training the model with 1452 images at 640 × 588 pixel resolution, which included the ones that were created via image augmentation, a discrimination experiment was performed. Diagnostic indicators such as true positive, true negative, false positive, false negative, and mean average precision were used to analyze object detection performance and segmentation of systems. YOLO v5X and YOLO v5M models achieved superior performance over the other models on the same dataset. YOLO v5X’s mAP was 0.727, precision was 0.731, and recall was 0.729, which was higher than other models of YOLO v5, which generated 64% accuracy, with YOLO v5M producing slightly inferior results. Overall mAPs of 0.70, precision of 0.712, and recall of 0.708 were achieved. Object detection through the current YOLO models was able to successfully extract and classify regions of carious lesions from smartphone photographs of in vitro tooth specimens with reasonable accuracy. YOLO v5M was better fit to detect carious microcavitations while YOLO v5X was able to detect carious changes without cavitation. No single model was capable of adequately diagnosing all classifications of carious lesions.
Publisher: Bangladesh Journals Online (JOL)
Date: 30-08-2019
Abstract: Objective:This case report describes the rehabilitation process of a case of acquired eye defect with patient specific or custom made ocular prosthesis for a patient who had her left eye surgically enucleated as a treatment step for retinoblastoma. Method: After primary evaluations, an intraorbital impression was taken while reproducing natural eye movements to ensure accuracy of the impression. The impression was cast and a transparent acrylic conformer was made from the mould in the cast. The conformer was adjusted as required and Iris position determined. The conformer was then used to cast the final custom ocular prosthesis. The patient was then instructed on its usage and maintenance. Result: A custom made ocular prosthesis was provided to the patient and it was to her satisfaction. Conclusion: Custom made ocular prosthesis is highly recommended in rehabilitation of facial defects of a co-operative patient as it does not have most of the limitations which a stock ocular prosthesis does. Bangladesh Journal of Medical Science Vol.18(4) 2019 p.823-826
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 10-2022
DOI: 10.1016/J.PROSDENT.2020.12.041
Abstract: The anatomic complexity of the ear challenges conventional maxillofacial prosthetic rehabilitation. The introduction of specialized scanning hardware integrated into computer-aided design and computer-aided manufacturing (CAD-CAM) workflows has mitigated these challenges. Currently, the scanning hardware required for digital data acquisition is expensive and not readily available for prosthodontists in developing regions. The purpose of this virtual analysis study was to compare the accuracy and precision of 3-dimensional (3D) ear models generated by scanning gypsum casts with a smartphone camera and a desktop laser scanner. Six ear casts were fabricated from green dental gypsum and scanned with a laser scanner. The resultant 3D models were exported as standard tessellation language (STL) files. A stereophotogrammetry system was fabricated by using a motorized turntable and an automated microcontroller photograph capturing interface. A total of 48 images were captured from 2 angles on the arc (20 degrees and 40 degrees from the base of the turntable) with an image overlap of 15 degrees, controlled by a stepper motor. Ear 1 was placed on the turntable and captured 5 times with smartphone 1 and tested for precision. Then, ears 1 to 6 were scanned once with a laser scanner and with smartphones 1 and 2. The images were converted into 3D casts and compared for accuracy against their laser scanned counterparts for surface area, volume, interpoint mismatches, and spatial overlap. Acceptability thresholds were set at 0.70 for spatial overlap. The test for smartphone precision in comparison with that of the laser scanner showed a difference in surface area of 774.22 ±295.27 mm Smartphone cameras used to capture 48 overlapping gypsum cast ear images in a controlled environment generated 3D models parametrically similar to those produced by standard laser scanners.
Publisher: Hindawi Limited
Date: 26-04-2021
DOI: 10.1155/2021/6659133
Abstract: Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method. Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted. Results. 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. Conclusion. Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.
Publisher: Wiley
Date: 09-03-2023
DOI: 10.1111/JOOR.13440
Abstract: This review aimed to systematically analyse the influence of clinical variables, diagnostic parameters and the overall image acquisition process on automation and deep learning in TMJ disorders. Articles were screened in late 2022 according to a predefined eligibility criteria adhering to the PRISMA protocol. Eligible studies were extracted from databases hosted by MEDLINE, EBSCOHost, Scopus, PubMed and Web of Science. Critical appraisals were performed on in idual studies following Nature Medicine's MI‐CLAIM checklist while a combined appraisal of the image acquisition procedures was conducted using Cochrane's GRADE approach. Twenty articles were included for full review following eligibility screening. The average experience possessed by the clinical operators within the eligible studies was 13.7 years. Bone volume, trabecular number and separation, and bone surface‐to‐volume ratio were clinical radiographic parameters while disc shape, signal intensity, fluid collection, joint space narrowing and arthritic changes were successful parameters used in MRI‐based deep machine learning. Entropy was correlated to sclerosis in CBCT and was the most stable radiomic parameter in MRI while contrast was the least stable across thermography and MRI. Adjunct serum and salivary biomarkers, or clinical questionnaires only marginally improved diagnostic outcomes through deep learning. Substantial data was classified as unusable and subsequently discarded owing to a combination of suboptimal image acquisition and data augmentation procedures. Inadequate identification of the participant characteristics and multiple studies utilising the same dataset and data acquisition procedures accounted for serious risks of bias. Deep‐learned models diagnosed osteoarthritis as accurately as clinicians from 2D and 3D radiographs but, in comparison, performed poorly when detecting disc disorders from MRI datasets. Complexities in clinical classification criteria non‐standardised diagnostic parameters errors in image acquisition cognitive, contextual or implicit biases were influential variables that generally affected analyses of inflammatory joint changes and disc disorders.
Publisher: Springer Science and Business Media LLC
Date: 25-04-2023
DOI: 10.1007/S11282-023-00685-8
Abstract: (1) To evaluate the effects of denoising and data balancing on deep learning to detect endodontic treatment outcomes from radiographs. (2) To develop and train a deep-learning model and classifier to predict obturation quality from radiomics. The study conformed to the STARD 2015 and MI-CLAIMS 2021 guidelines. 250 deidentified dental radiographs were collected and augmented to produce 2226 images. The dataset was classified according to endodontic treatment outcomes following a set of customized criteria. The dataset was denoised and balanced, and processed with YOLOv5s, YOLOv5x, and YOLOv7 models of real-time deep-learning computer vision. Diagnostic test parameters such as sensitivity (Sn), specificity (Sp), accuracy (Ac), precision, recall, mean average precision (mAP), and confidence were evaluated. Overall accuracy for all the deep-learning models was above 85%. Imbalanced datasets with noise removal led to YOLOv5x’s prediction accuracy to drop to 72%, while balancing and noise removal led to all three models performing at over 95% accuracy. mAP saw an improvement from 52 to 92% following balancing and denoising. The current study of computer vision applied to radiomic datasets successfully classified endodontic treatment obturation and mishaps according to a custom progressive classification system and serves as a foundation to larger research on the subject matter.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Wiley
Date: 02-12-2021
DOI: 10.1111/JOPR.13286
Abstract: Mesh optimization reduces the texture quality of 3D models in order to reduce storage file size and computational load on a personal computer. This study aims to explore mesh optimization using open source (free) software in the context of prosthodontic application. An auricular prosthesis, a complete denture, and anterior and posterior crowns were constructed using conventional methods and laser scanned to create computerized 3D meshes. The meshes were optimized independently by four computer‐aided design software (Meshmixer, Meshlab, Blender, and SculptGL) to 100%, 90%, 75%, 50%, and 25% levels of original file size. Upon optimization, the following parameters were virtually evaluated and compared mesh vertices, file size, mesh surface area (SA), mesh volume (V), interpoint discrepancies (geometric similarity based on virtual point overlapping), and spatial similarity (volumetric similarity based on shape overlapping). The influence of software and optimization on surface area and volume of each prosthesis was evaluated independently using multiple linear regression. There were clear observable differences in vertices, file size, surface area, and volume. The choice of software significantly influenced the overall virtual parameters of auricular prosthesis [SA: F(4,15) = 12.93, R 2 = 0.67, p 0.001. V: F(4,15) = 9.33, R 2 = 0.64, p 0.001] and complete denture [SA: F(4,15) = 10.81, R 2 = 0.67, p 0.001. V: F(4,15) = 3.50, R 2 = 0.34, p = 0.030] across optimization levels. Interpoint discrepancies were however limited to .1mm and volumetric similarity was %. Open‐source mesh optimization of smaller dental prostheses in this study produced minimal loss of geometric and volumetric details. SculptGL models were most influenced by the amount of optimization performed.
Publisher: Elsevier BV
Date: 2023
Publisher: Bangladesh Journals Online (JOL)
Date: 10-03-2020
Abstract: Purpose: For making denture in maxillectomy cases is very difficult and challenging to get the retention tomake the denture stable in its position during functioning. This case report describes a clinical condition inwhich patient was treated with a maxillary obturator with zygomatic suspension wiring due to insufficientretention in the palate. Materials and Methods: A 63-year-old patient had gone to a subtotal maxillectomybecause following myofibroblastic sarcoma andwas issued with bilateral circum-zygomatic wiring hooksimmediately after surgery. The patient had less than a third of their alveolar ridge remaining and did notprovide sufficient retention on its own. The wires were used for the retention because natural retentioncould not get due to inadequate maxillary ridge. While the denture was being fabricated, a temporaryfeeding plate was provisioned to the patient. Counter hooks were implemented on the definitive upperdenture, posterior to molars, to attach to the zygomatic suspension hook. Final upper denture was furtherreinforced with denture adhesive on it. A lower denture was also fabricated for the said case followingconventional protocols of jaw relation determination. Results: The obturator provided with adequate sealand leak proof phonetics. The zygomatic wiring coupled with denture adhesive were sufficient to provideadequate retention. Conclusion: Zygomatic suspension wires coupled with counter hooked obturatorprosthesis provide rehabilitation of patients with palatal defects with inadequate maxillary arch forretention.However long-term repeated use of such wires might expose the patient to secondary infectionsand should be kept in consideration Clinical Significance: Circum-zygomatic suspension wiring providesufficient retention to sustain the upper obturator prosthesis in place. However, the retention was stillinadequate, so denture adhesives were used to make it more stable. Bangladesh Journal of Medical Science Vol.19(3) 2020 p.582-585
Publisher: SAGE Publications
Date: 13-08-2021
Abstract: This systematic review and meta-analysis explored the factors involved in the color stability and degradation of Maxillo-Facial Prosthetic Silicone Elastomer (MFPSE). Further exploration was done to analyze past literature discussing the potential benefits to color stability when nano-particles were combined with pigmented MFPSE. The search for the articles was done according to PRISMA guidelines. Articles were searched from “Scopus” and “Web of Sciences” from the year 1970 to 2019. Searches were carried out by two reviewers until November 2019. Articles for systematic review were selected based on predefined eligibility criteria. Information regarding weathering conditions, pigments and filler-particle inclusion were extracted as appropriate. Further screening was done for meta-analyze case-control studies of red, blue and yellow pigments according to predefined scoring criteria. Meta-analysis was conducted on case-control studies that incorporated 5%,10% and 15% TiO 2 in MFPSE with the said pigments and was carried out using Cochrane Review Manager 5.3. 30 studies were selected for systematic review and 6 studies were eligible for meta-analysis. The most prominent influencers of color stability were nano-fillers and the type of color used in the mixing. Furthermore, experimental conditions, weathering, color of investment plaster and the method of color detection all affected the degree of degradation. There was an overall significant difference found when TiO 2 was incorporated with the pigmented silicone. There is significant difference when 10% ( P = 0.0004) TiO 2 is incorporated with the red pigment, 5% ( P = 0.03) TiO 2 with the yellow and 10% ( P 0.0001) and 15% ( P = 0.02) TiO 2 with the blue pigment. Type of pigment and nano-filler incorporated into the silicone play a role in influencing color stability. Incorporation of 10% TiO 2 with red pigment,5% with yellow pigment and 10% or 15% TiO 2 with blue pigment provided some protection to the silicone elastomer from color degradation.
Publisher: Mary Ann Liebert Inc
Date: 06-2012
Publisher: Springer Science and Business Media LLC
Date: 19-04-2021
DOI: 10.1038/S41598-021-87240-9
Abstract: Palatal defects are rehabilitated by fabricating maxillofacial prostheses called obturators. The treatment incorporates taking deviously unpredictable impressions to facsimile the palatal defects into plaster casts for obturator fabrication in the dental laboratory. The casts are then digitally stored using expensive hardware to prevent physical damage or data loss and, when required, future obturators are digitally designed, and 3D printed. Our objective was to construct and validate an economic in-house smartphone-integrated stereophotogrammetry (SPINS) 3D scanner and to evaluate its accuracy in designing prosthetics using open source/free (OS/F) digital pipeline. Palatal defect models were scanned using SPINS and its accuracy was compared against the standard laser scanner for virtual area and volumetric parameters. SPINS derived 3D models were then used to design obturators by using (OS/F) software. The resultant obturators were virtually compared against standard medical software designs. There were no significant differences in any of the virtual parameters when evaluating the accuracy of both SPINS, as well as OS/F derived obturators. However, limitations in the design process resulted in minimal dissimilarities. With further improvements, SPINS based prosthetic rehabilitation could create a viable, low cost method for rural and developing health services to embrace maxillofacial record keeping and digitised prosthetic rehabilitation.
Publisher: MDPI AG
Date: 31-03-2023
Abstract: Background: Access to oral healthcare is not uniform globally, particularly in rural areas with limited resources, which limits the potential of automated diagnostics and advanced tele-dentistry applications. The use of digital caries detection and progression monitoring through photographic communication, is influenced by multiple variables that are difficult to standardize in such settings. The objective of this study was to develop a novel and cost-effective virtual computer vision AI system to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. Methods: A set of 1703 augmented images was obtained from 233 de-identified teeth specimens. Images were acquired using a consumer smartphone, without any standardised apparatus applied. The study utilised state-of-the-art ensemble modeling, test-time augmentation, and transfer learning processes. The “you only look once” algorithm (YOLO) derivatives, v5s, v5m, v5l, and v5x, were independently evaluated, and an ensemble of the best results was augmented, and transfer learned with ResNet50, ResNet101, VGG16, AlexNet, and DenseNet. The outcomes were evaluated using precision, recall, and mean average precision (mAP). Results: The YOLO model ensemble achieved a mean average precision (mAP) of 0.732, an accuracy of 0.789, and a recall of 0.701. When transferred to VGG16, the final model demonstrated a diagnostic accuracy of 86.96%, precision of 0.89, and recall of 0.88. This surpassed all other base methods of object detection from free-hand non-standardised smartphone photographs. Conclusion: A virtual computer vision AI system, blending a model ensemble, test-time augmentation, and transferred deep learning processes, was developed to predict dental cavitations from non-standardised photographs with reasonable clinical accuracy. This model can improve access to oral healthcare in rural areas with limited resources, and has the potential to aid in automated diagnostics and advanced tele-dentistry applications.
Publisher: Elsevier BV
Date: 05-2023
Location: Australia
No related grants have been discovered for Taseef Farook.