Detection Of Plaques Research Articles

PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography.

To develop and evaluate a deep learning model for segmentation of the coronary artery vessels and coronary plaques in coronary computed tomography angiography (CCTA). CCTA image data from the Swedish CardioPulmonary BioImage Study (SCAPIS) was used for model development (n = 463 subjects) and testing (n = 123) and for an interobserver study (n = 65). A dataset from Linköping University Hospital (n = 28) was used for external validation. The model's ability to detect coronary artery disease (CAD) was tested in a separate SCAPIS dataset (n = 684). A deep ensemble (k = 6) of a customized 3D vision transformer model was used for voxelwise classification. The Dice coefficient, the average surface distance, Pearson's correlation coefficient, analysis of segmented volumes by intraclass correlation coefficient (ICC), and agreement (sensitivity and specificity) were used to analyze model performance. PlaqueViT segmented coronary plaques with a Dice coefficient = 0.55, an average surface distance = 0.98 mm and ICC = 0.93 versus an expert reader. In the interobserver study, PlaqueViT performed as well as the expert reader (Dice coefficient = 0.51 and 0.50, average surface distance = 1.31 and 1.15 mm, ICC = 0.97 and 0.98, respectively). PlaqueViT achieved 88% agreement (sensitivity 97%, specificity 76%) in detecting any coronary plaque in the test dataset (n = 123) and 89% agreement (sensitivity 95%, specificity 83%) in the CAD detection dataset (n = 684). We developed a deep learning model for fully automatic plaque detection and segmentation that identifies and delineates coronary plaques and the arterial lumen with similar performance as an experienced reader. Question A tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination. Findings Segmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance. Clinical relevance This novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.

Natural and Synthetic LDL-Based Imaging Probes for the Detection of Atherosclerotic Plaques

Natural and Synthetic LDL-Based Imaging Probes for the Detection of Atherosclerotic Plaques

Abstract TMP54: Development and testing of a fully automated tool for the detection, segmentation, and characterization of cervical carotid atherosclerotic disease

Background: Rapid, accurate diagnosis and characterization of carotid atherosclerosis can help prevent disabling strokes. Although carotid plaques can be identified on CT angiography (CTA), interpretation is challenging for frontline physicians. Quantification of plaque volume/composition requires much manual effort. We developed and tested a fully automated tool for segmenting carotid lumens and delineating atherosclerotic plaque, distinguishing between calcific and hypodense components. Methods: We used 528 consecutive cases with CTA head/neck from a population of 7,745 patients with ischemic stroke and transient ischemic attack in an entire province (Alberta) presenting from 1-April-2016 to 31-March-2017. Trained readers supervised by a radiologist manually segmented right and left carotid artery lumens from 3 vertebral bodies below the bulb to 3 above, and segmented regions of carotid atherosclerotic plaque, regardless of degree of stenosis, separately labelling calcific and hypodense components. Cases were split 80/20 between training and testing datasets. The fully automated pipeline included coarse-scale detection of regions of interest, a two-stream U-shaped network for detection and segmentation of lumens and plaques, and a geometry-based inference algorithm to distinguish left and right labels. We evaluated plaque detection using diagnostic performance measures and segmentation using the Dice coefficient. Results: 422 cases were used for training and 106 for testing. In testing data, the fully automated tool achieved excellent performance for bilateral segmentation of carotid lumens (Dice 0.91, 95%CI:0.90-0.92, e.g. Figure 1 ). For detection of calcific and hypodense plaque components, respectively, the model achieved sensitivity of 96.5% (95%CI:89.3-99.1%) and 97.3% (89.6-99.5%), specificity of 95.2% (74.1-99.8%) and 75.8% (57.4-88.3%), positive predictive value of 98.8% (92.6-99.9%) and 89.9% (80.5-95.2%), negative predictive value of 87.0% (65.3-96.6%) and 92.6% (74.2-98.7%), and accuracy of 96.2% (90.1-98.8%) and 90.6% (82.9-95.1%, e.g. Figure 2 ). Dice score was 0.73 (0.69-0.76) for calcific plaque and 0.53 (0.49-0.57) for hypodense plaque. Conclusions: Our fully automated tool achieved good performance for detection and segmentation of carotid plaques. Calcific and hypodense plaque volumes can be automatically generated from these labels. Efforts are underway to further optimize the specificity and segmentation performance for hypodense plaques.

A continuous-action deep reinforcement learning-based agent for coronary artery centerline extraction in coronary CT angiography images.

The lumen centerline of the coronary artery allows vessel reconstruction used to detect stenoses and plaques. Discrete-action-based centerline extraction methods suffer from artifacts and plaques. This study aimed to develop a continuous-action-based method which performs more effectively in cases involving artifacts or plaques. A continuous-action deep reinforcement learning-based model was trained to predict the artery's direction and radius value. The model is based on an Actor-Critic architecture. The Actor learns a deterministic policy to output the actions made by an agent. These actions indicate the centerline's direction and radius value consecutively. The Critic learns a value function to evaluate the quality of the agent's actions. A novel DDR reward was introduced to measure the agent's action (both centerline extraction and radius estimate) at each step. The method achieved an average OV of 95.7%, OF of 93.6%, OT of 97.3%, and AI of 0.22 mm in 80 test data. In 53 cases with artifacts or plaques, it achieved an average OV of 95.0%, OF of 91.5%, OT of 96.7%, and AI of 0.23 mm. The 95% limits of agreement between the reference and estimated radius values were 0.46 mm and 0.43 mm in the 80 test data. Experiments demonstrate that the Actor-Critic architecture can achieve efficient centerline extraction and radius estimate. Compared with discrete-action-based methods, our method performs more effectively in cases involving artifacts or plaques. The extracted centerlines and radius values allow accurate coronary artery reconstruction that facilitates the detection of stenoses and plaques.

Quantifying natural amyloid plaque accumulation in the continuum of Alzheimer's disease using ADNI.

Brain amyloid beta neuritic plaque accumulation is associated with an increased risk of progression to Alzheimer's disease(AD) [Pfeil, J., et al. in Neurobiol Aging 106: 119-129, 2021]. Several studies estimate rates of change in amyloid plaque over time in clinically heterogeneous cohorts with different factors impacting amyloid plaque accumulation from ADNI and AIBL [Laccarino, L., et al. in Annals Clin and Trans Neurol 6: 1113 1120, 2019, Vos, S.J., et al. in Brain 138: 1327-1338, 2015, Lim, Y.Y., et al. in Alzheimer's Dementia 9: 538-545, 2013], but there are no reports using non-linear mixed effect model for amyloid plaque progression over time similar to that existing of disease-modifying biomarkers for other diseases [Cook, S.F. and R.R. Bies in Current Pharmacol Rep 2: 221-230, 2016, Gueorguieva, I., et al. in Alzheimer's Dementia 19: 2253-2264, 2023]. This study describes the natural progression of amyloid accumulation with population mean and between-participant variability for baseline and intrinsic progression rates quantified across the AD spectrum. 1340 ADNI participants were followed over a 10-year period with 18F-florbetapir PET scans used for amyloid plaque detection. Non-linear mixed effect with stepwise covariate modelling (scm) was used. Change in natural amyloid plaque levels over 10year period followed an exponential growth model with an intrinsic rate of approx. 3 centiloid units/year. Age, gender, APOE4 genotype and disease stage were important factors on the baseline in the natural amyloid model. In APOE4 homozygous carriers mean baseline amyloid was increased compared to APOE4 non carriers. These results demonstrate natural progression of amyloid plaque in the continuum of AD.

Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O'Leary Index.

Background: Oral diseases such as caries, gingivitis, and periodontitis are highly prevalent worldwide and often arise from plaque. This study focuses on detecting three plaque stages-new, mature, and over-mature-using state-of-the-art YOLO architectures to enhance early intervention and reduce reliance on manual visual assessments. Methods: We compiled a dataset of 531 RGB images from 177 individuals, captured via multiple mobile devices. Each sample was treated with disclosing gel to highlight plaque types, then preprocessed for lighting and color normalization. YOLOv9, YOLOv10, and YOLOv11, in various scales, were trained to detect plaque categories, and their performance was evaluated using precision, recall, and mean Average Precision (mAP@50). Results: Among the tested models, YOLOv11m achieved the highest mAP@50 (0.713), displaying superior detection of over-mature plaque. Across all YOLO variants, older plaque was generally easier to detect than newer plaque, which can blend with gingival tissue. Applying the O'Leary index indicated that over half of the study population exhibited severe plaque levels. Conclusions: Our findings demonstrate the feasibility of automated plaque detection with advanced YOLO models in varied imaging conditions. This approach offers potential to optimize clinical workflows, support early diagnoses, and mitigate oral health burdens in low-resource communities.

A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study.

To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model. We retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model's diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios. In total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 79.17%). This model also analyzed the results of different types of plaques and showed good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00, 86.87, 84.29%). Especially, in the clinical application scenario analysis, the model's diagnostic results for plaques were found to be higher than those of 4 out of 6 radiologists (p < 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists' sensitivity in diagnosing plaques. Additionally, the model's diagnostic time for plaques (6 s) was found to be significantly shorter than that all of radiologists (p < 0.001). This AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases.

Unsaturated Fatty Acids Are Decreased in Aβ Plaques in Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-beta (Aβ) plaques in the brain, contributing to neurodegeneration. This study investigates lipid alterations within these plaques using a novel, label-free, multimodal approach. Combining infrared (IR) imaging, machine learning, laser microdissection (LMD), and flow injection analysis mass spectrometry (FIA-MS), we provide the first comprehensive lipidomic analysis of chemically unaltered Aβ plaques in post-mortem human AD brain tissue. IR imaging revealed decreased lipid unsaturation within plaques, evidenced by a reduction in the alkene (=C-H) stretching vibration band. The high spatial resolution of IR imaging, coupled with machine learning-based plaque detection, enabled precise and label-free extraction of plaques via LMD. Subsequent FIA-MS analysis confirmed a significant increase in short-chain saturated lipids and a concomitant decrease in long-chain unsaturated lipids within plaques compared to the surrounding tissue. These findings highlight a substantial depletion of unsaturated fatty acids (UFAs) in Aβ plaques, suggesting a pivotal role for lipid dysregulation and oxidative stress in AD pathology. This study advances our understanding of the molecular landscape of Aβ plaques and underscores the potential of lipid-based therapeutic strategies in AD.

Robust Joint Detection of Coronary Artery Plaque and Stenosis in Angiography Using Enhanced DCNN-GAN

Robust Joint Detection of Coronary Artery Plaque and Stenosis in Angiography Using Enhanced DCNN-GAN

Duplex Ultrasonography for Screening and Monitoring of Carotid Artery Stenosis for Risk Stratification of Ischemic Stroke

Aim: To evaluate the efficiency of Duplex Ultrasonography (DUS) for monitoring and screening of Carotid Artery Stenosis (CAS).Methods: PubMed, Web of Science, Cochrane, and SCOPUS were searched for relevant articles. The quality assessment of the included studies was conducted according to the Cochrane Risk of Bias Tool.Results: Our meta-analysis included six articles. Carotid DUS could detect mild CAS in 25%, moderate CAS in 13%, severe CAS in 14.5% of the examined vessels, detection of carotid artery plaques in 36.5% of the examined vessels, and abnormal intima thickness in 21.5% of the examined vessels.Conclusion: DUS is an important exam in the identification of patients with symptomatic CAS. It can improve clinical practice and provide cost-effective management for CAS. Carotid DUS showed high specificity and sensitivity in the diagnosis of CAS, especially a severe degree of stenosis.

Label-free Aβ plaque detection in Alzheimer’s disease brain tissue using infrared microscopy and neural networks

Label-free Aβ plaque detection in Alzheimer’s disease brain tissue using infrared microscopy and neural networks

Incremental Value of Pericoronary Adipose Tissue Radiomics Models in Identifying Vulnerable Plaques.

Inflammatory characteristics in pericoronary adipose tissue (PCAT) may enhance the diagnostic capability of radiomics techniques for identifying vulnerable plaques. This study aimed to evaluate the incremental value of PCAT radiomics scores in identifying vulnerable plaques defined by intravascular ultrasound imaging (IVUS). In this retrospective study, a PCAT radiomics model was established and validated using IVUS as the reference standard. The dataset consisted of patients with coronary artery disease who underwent both coronary computed tomography angiography and IVUS examinations at a tertiary hospital between March 2023 and January 2024. The dataset was randomly assigned to the training and validation sets in a 7:3 ratio. The diagnostic performance of various models was evaluated on both sets using the area under the curve (AUC). From 88 lesions in 79 patients, we selected 9 radiomics features (5 texture features, 1 shape feature, 1 gray matrix feature, and 2 first-order features) from the training cohort (n = 61) to build the PCAT model. The PCAT radiomics model demonstrated moderate to high AUCs (0.847 and 0.819) in both the training and test cohorts. Furthermore, the AUC of the PCAT radiomics model was significantly higher than that of the fat attenuation index model (0.847 vs 0.659, P < 0.05). The combined model had a higher AUC than the clinical model (0.925 vs 0.714, P < 0.01). The PCAT radiomics signature of coronary CT angiography enabled the detection of vulnerable plaques defined by IVUS.

Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques

Background Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular events. However, there is no study combining radiomics with deep learning techniques to predict non-calcified plaques (NCP) in coronary artery at present. Objective To investigate the value of combination of radiomics and deep learning features based on non-contrast computerized tomography (CT) scans of pericoronary adipose tissue (PCAT), integrating with clinical risk factors of patients, in identifying coronary inflammation and predicting the presence of NCP. Methods The clinical and imaging data of 353 patients were analyzed. The region of interest (ROI) of PCAT was manually outlined on non-contrast CT scan images, like coronary CT calcium score sequential images, then the radiomics and deep learning features in ROIs were extracted respectively. In training set (Center 1), after performing feature selection, radiomics and deep learning feature models were established, meanwhile, clinical models were built. Finally, combined models were developed out via integrating clinical, radiomics, and deep learning features. The predictive performance of the four feature model groups (clinical, radiomics, deep learning, and three combination) was assessed by seven different machine learning models through generation of receiver operating characteristic curves (ROC) and the calculation of area under the curve (AUC), sensitivity, specificity, and accuracy. Furthermore, the predictive performance of each model was validated in an external validation set (Center 2). Results For the single model comparation, eXtreme Gradient Boosting (XGBoost) showed the best performance among the clinical model group in the validation set. And Random Forest (RF) exhibited the best indicative performance not only among the radiomics feature group but also in the deep learning feature model group. What's more, among the combined model group, RF still displayed the best predictive performance, with the value of AUC, sensitivity, specificity, and accuracy in the validation set are 0.963, 0.857, 0.929, and 0.905. Conclusion The RF model in the combined model group based on non-contrast CT scan PCAT can predict the presence of NCP more accurately and has the potential for preliminary screening of the NCP.

Oral Conditions, Salivary pH, Flow Rate, Phosphate Level, and Phosphorus Intake of Pre- and Postmenopausal Women.

This study aimed to investigate salivary parameters, including pH, flow rate, phosphate levels, and phosphorus intake, to explore potential variations between postmenopausal and premenopausal women. This study included 68 postmenopausal and 94 premenopausal women. Inclusion criteria comprised women aged 45 to 65 years with a minimum menopausal duration of 2 years and women aged 21 to 40 years for premenopausal participants. Exclusion criteria involved systemic diseases and any dental treatments received in the last 6 months. Direct observation facilitated the visual detection of intraoral inflammation, ulcers, plaque, calculus, dental mobility, and caries. A questionnaire covering demographic data, dental pain, xerostomia, burning sensation, ulcer etiology and duration, and gingival bleeding etiology was administered. Phosphorus intake was assessed using a semiquantitative food frequency questionnaire. Unstimulated whole saliva, collected by spitting, was analyzed for various salivary parameters, such as pH, flow rate, and phosphate level. The atomic absorption spectrophotometer was used to determine subjects' salivary phosphate level. The difference in intraoral conditions between groups was analyzed using the chi-square or Fisher's exact test. For the comparison of salivary parameters and phosphorus intake between study groups, the ANOVA: univariate general linear model was utilized. The correlation between salivary phosphate levels and age, body mass index (BMI), blood pressure, and phosphorus intake was examined using Spearman's rank correlation. The postmenopausal group demonstrated a significantly higher prevalence (p < 0.005) of gingival swelling, gingival discoloration, gingival recession, plaque, calculus, caries, tooth mobility, xerostomia, and burning sensation. Following adjustments for age, BMI, and blood pressure, a statistically significant difference in salivary flow rate between groups was observed (p = 0.008). No significant differences were found in salivary pH (p = 0.764), salivary phosphate level (p = 0.142), or phosphorus intake (p = 0.323) between the two groups. There was no significant correlation between salivary phosphate levels and age (p = 0.747), BMI (p = 0.308), systolic blood pressure (p = 0.747), diastolic blood pressure (p = 0.622), and phosphorus intake (0.829) in both groups. Postmenopausal women exhibited a lower salivary flow rate compared with premenopausal women. No differences or correlations were observed in salivary phosphate level and phosphorus intake between the two groups.

Level of Agreement Between Plaque Detection with Clinical Assessment and Assessment on Intraoral Scanner.

Background/Objectives: The aim was to evaluate the agreement between plaque detection with an intraoral scanner system (IOS) and a conventional clinical method and to evaluate the inter-rater reliability for scoring 3D models with and without a disclosing agent. Methods: A total of 14 participants were recruited from the Department of Operative Dentistry, School of Dentistry, National and Kapodistrian University of Athens. Participants eligible for inclusion were adults with good general health and a minimum of 20 teeth. Participants were clinically examined with plaque assessment according to the modified Quigley-Hein plaque index before and after using a disclosing agent (GC-Tri Plaque ID-Gel, GC, Europe N.V). Before and after the application of the disclosing agent, all study participants were scanned using the IOS (TRIOS5, 3Shape TRIOS A/S). The clinical examiner and three additional examiners blinded to the clinical examination assessed plaque status on the acquired 3D models with and without disclosing agent using the same index to evaluate the inter-rater agreement. Intraclass coefficient correlation, one sample t-test, and Cronbach's α for inter-rater reliability were calculated. Results: All methods showed moderate to strong correlations (Spearman's rho ranging from 0.527 to 0.618), and Cronbach's α ranged from 0.551 to 0.766. Conclusions: The level of agreement between conventional clinical registration and registration from 3D models was acceptable overall.

Flourescent Based Camera (QSCAN Plus) for Dental Plaque Quantification: Validation and Reliability

ABSTRACT Introduction: Detection and quantification of dental plaque is crucial in both clinical dentistry and research. Established clinical indices used to measure dental plaque have limitations. The aim of the study was to evaluate the reliability and validity of plaque quantification using a fluorescent camera (QScan Plus). Materials and Methods: Double doubleblind study was carried out on 20 children of age ranging from 9 to 14 yrs. Plaque measurement was performed by two examiners on a total of 160 incisors at the base, 24, 36, 48, and 96 hrs. Images were captured by the fluorescent camera, digitally analyzed, and scored. Another scoring was done using Addy Plaque Index area after plaque disclosure. The average scores were used for statistical analysis. Results: The results were within the excellent reliability category for interoperator and intraoperator repeatability. Conclusion: This new system provides an improved objective method of quantifying dental plaque areas with increased accuracy.

Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models.

Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer's disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limits the accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging. We leverage a synthetic data generation pipeline to create high-resolution PET digital phantoms for model training. The proposed LDM-RR model incorporates a weighted combination of L1, L2, and MS-SSIM losses at both noise and image scales to enhance MRI-guided reconstruction. We evaluated the model's performance in improving statistical power for detecting longitudinal changes and enhancing agreement between amyloid PET measurements from different tracers. The results demonstrate that the LDM-RR approach significantly improves PET quantification accuracy, reduces inter-tracer variability, and enhances the detection of subtle changes in amyloid deposition over time. We show that deep learning has the potential to improve PET quantification in AD, effectively contributing to the early detection and monitoring of disease progression.

A semi-automatic cardiovascular annotation and quantification toolbox utilizing prior knowledge-guided feature learning

Annotation and quantification of cardiovascular in intravascular ultrasound (IVUS) images are of great significance in assisting the clinical diagnosis and treatment of coronary heart disease. However, it is time-consuming and expensive for cardiologists to manually annotate cardiovascular and calculate parameters in IVUS images using different software during the diagnosis. In this paper, we propose an IVUS analysis toolbox (SaCAQ) for semi-automatic cardiovascular annotation and quantification via prior knowledge-guided feature learning. The SaCAQ consists of two main modules: (1) a semi-automatic cardiovascular annotation module and (2) a cardiovascular quantification module. The semi-automatic cardiovascular annotation module can accurately segment the vascular wall by a IVUS images segmentation model (ISM-IVUS). The ISM-IVUS first extracts vascular morphological features and pixel intensity change features for the complex image segmentation by the expert prior knowledge feature extraction mechanism. Secondly, vascular wall is accurately segmented by the multi-scale feature extraction of attention U-Net. Moreover, the segmentation result checking mechanism is applied to verify the automatic segmentation result, which enhances the reliability of SaCAQ. Plaque regions of interest to doctors are detected by the rule-based plaque detection method, which can assist in the quantification task. Finally, image measurement methods and medically recognized calculation formulas are used in the cardiovascular quantification module to obtain clinical parameters, which significantly improves the confidence of the results. The SaCAQ is verified on our own dataset containing 5242 IVUS slice images and 383 sets of parameters. The experiments demonstrate that the SaCAQ achieves high accuracy of vascular wall segmentation: mIoU of 86.16%, Dice coefficient of 93.43%, and Hausdorff distance of 0.2171 mm. Moreover, ISM-IVUS has good segmentation speed and robustness. The calculated parameters show good correlation and p-values. All of these results indicate that the proposed SaCAQ provides an efficient, accurate, and reliable toolbox for clinical diagnosis of cardiovascular disease. The SaCAQ and user manual can be publicly available from https://github.com/zHwiz/SaCAQ.

Automated machine learning for image-based detection of dental plaque on permanent teeth

Automated machine learning for image-based detection of dental plaque on permanent teeth

Melastoma malabachtricum Extract as a Dental Plaque Detection Agent

Melastoma malabachtricum Extract as a Dental Plaque Detection Agent