Deep Learning for the Radiographic Detection of Periodontal Bone Loss.
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ABSTRACT:
SUBMITTER: Krois J
PROVIDER: S-EPMC6560098 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this study, a deep learning-based method for developing an automated diagnostic support system that detects periodontal bone loss in the panoramic dental radiographs is proposed. The presented method called DeNTNet not only detects lesions but also provides the corresponding teeth numbers of the lesion according to dental federation notation. DeNTNet applies deep convolutional neural networks(CNNs) using transfer learning and clinical prior knowledge to overcome the morphological variation of the lesions and imbalanced training dataset. With 12,179 panoramic dental radiographs annotated by experienced dental clinicians, DeNTNet was trained, validated, and tested using 11,189, 190, and 800 panoramic dental radiographs, respectively. Each experimental model was subjected to comparative study to demonstrate the validity of each phase of the proposed method. When compared to the dental clinicians, DeNTNet achieved the F1 score of 0.75 on the test set, whereas the average performance of dental clinicians was 0.69.
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Project description:The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.
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Project description:BackgroundConsidering the prevalence of Periodontitis, new tools to help improve its diagnostic workflow could be beneficial. Machine Learning (ML) models have already been used in dentistry to automate radiographic analysis.AimsTo determine the efficacy of an ML model for automatically measuring Periodontal Bone Loss (PBL) in panoramic radiographs by comparing it to dentists.MethodsA dataset of 2010 images with and without PBL was segmented using Label Studio. The dataset was split into n = 1970 images for building a training dataset and n = 40 images for building a testing dataset. We propose a model composed of three components. Firstly, statistical inference techniques find probability functions that best describe the segmented dataset. Secondly, Convolutional Neural Networks extract visual information from the training dataset. Thirdly, an algorithm calculates PBL as a percentage and classifies it in stages. Afterwards, a standardized test compared the model to two radiologists, two periodontists and one general dentist. The test was built using the testing dataset, 40 questions long, done in controlled conditions, with radiologists considered as ground truth. Presence or absence, percentage, and stage of PBL were asked, and time to answer the test was measured in seconds. Diagnostic indices, performance metrics and performance averages were calculated for each participant.ResultsThe model had an acceptable performance for diagnosing light to moderate PBL (weighted sensitivity 0.23, weighted F1-score 0.29) and was able to achieve real-time diagnosis. However, it proved incapable of diagnosing severe PBL (sensitivity, precision, and F1-score = 0).ConclusionsWe propose a Machine Learning model that automates the diagnosis of Periodontal Bone Loss in panoramic radiographs with acceptable performance.
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Project description:To explore the feasibility of an automatic machine-learning algorithm-based quality control system for the practice of diagnostic radiography, performance of a convolutional neural networks (CNN)-based algorithm for identifying radiographic (X-ray) views at different levels was examined with a retrospective, HIPAA-compliant, and IRB-approved study performed on 15,046 radiographic images acquired between 2013 and 2018 from nine clinical sites affiliated with our institution. Images were labeled according to four classification levels: level 1 (anatomy level, 25 classes), level 2 (laterality level, 41 classes), level 3 (projection level, 108 classes), and level 4 (detailed level, 143 classes). An Inception V3 model pre-trained with ImageNet dataset was trained with transfer learning to classify the image at all levels. Sensitivity and positive predictive value were reported for each class, and overall accuracy was reported for each level. Accuracy was also reported when we allowed for "reasonable errors". The overall accuracy was 0.96, 0.93, 0.90, and 0.86 at levels 1, 2, 3, and 4, respectively. Overall accuracy increased to 0.99, 0.97, 0.94, and 0.88 when "reasonable errors" were allowed. Machine learning algorithms resulted in reasonable model performance for identifying radiographic views with acceptable accuracy when "reasonable errors" were allowed. Our findings demonstrate the feasibility of building a quality-control program based on machine-learning algorithms to identify radiographic views with acceptable accuracy at lower levels, which could be applied in a clinical setting.
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Project description:Recent studies indicate a causal relationship between the periodontal pathogen P. gingivalis and rheumatoid arthritis involving the production of autoantibodies against citrullinated peptides. We therefore postulated that therapeutic eradication P. gingivalis may ameliorate rheumatoid arthritis development and here turned to a mouse model in order to challenge our hypothesis. F1 (DBA/1 x B10.Q) mice were orally inoculated with P. gingivalis before collagen-induced arthritis was provoked. Chlorhexidine or metronidazole were orally administered either before or during the induction phase of arthritis and their effects on arthritis progression and alveolar bone loss were compared to intraperitoneally injected methotrexate. Arthritis incidence and severity were macroscopically scored and alveolar bone loss was evaluated via microcomputed tomography. Serum antibody titres against P. gingivalis were quantified by ELISA and microbial dysbiosis following oral inoculation was monitored in stool samples via microbiome analyses. Both, oral chlorhexidine and metronidazole reduced the incidence and ameliorated the severity of collagen-induced arthritis comparable to methotrexate. Likewise, all three therapies attenuated alveolar bone loss. Relative abundance of Porphyromonadaceae was increased after oral inoculation with P. gingivalis and decreased after treatment. This is the first study to describe beneficial effects of non-surgical periodontal treatment on collagen-induced arthritis in mice and suggests that mouthwash with chlorhexidine or metronidazole may also be beneficial for patients with rheumatoid arthritis and a coexisting periodontitis. Methotrexate ameliorated periodontitis in mice, further raising the possibility that methotrexate may also positively impact on the tooth supporting tissues of patients with rheumatoid arthritis.
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Project description:RATIONALE:Osteoporosis is common in individuals with chronic obstructive pulmonary disease. Lung-specific factors, including radiographic emphysema, independently associate with low bone mineral density in cross-sectional smoking cohorts. However, factors associated with progressive bone loss in smokers are understudied and largely unknown. OBJECTIVES:To determine the relationship between radiographic emphysema, circulating bone metabolism markers, and pulmonary function and accelerated bone mineral density loss in smokers. METHODS:Two hundred and forty male and female current and former smokers, 40 years of age or older, underwent baseline and 2-year assessments of pulmonary function, computed tomography-assessed emphysema, dual X-ray absorptiometry-measured bone mineral density, and circulating bone metabolism biomarker levels (type I collagen C-telopeptide [CTX], amino-terminal propeptide of type I procollagen [P1NP]). The association of radiographic emphysema, bone metabolism biomarker levels, and pulmonary function with accelerated hip bone mineral density loss, defined by the 75th percentile of annual hip bone mineral density decline, was determined by logistic regression modeling with adjustment for age, sex, inhaled and intermittent steroid use, active smoking, body mass index, and the presence of baseline low hip bone mineral density. RESULTS:Of those participants with accelerated hip bone mineral density loss, 22% had moderate or severe visually assessed emphysema compared with 7.2% of smokers without accelerated bone mineral density decline. Moderate to severe visually assessed emphysema (odds ratio, 2.84; 95% confidence interval, 1.01-7.98 compared with trace/mild or no visually assessed emphysema) and the 75th percentile of CTX levels (odds ratio, 2.38; 95% confidence interval, 1.20-4.72 compared with CTX levels below the 75th percentile), a marker of bone resorption, were associated with accelerated hip bone mineral density decline after adjustment for covariates and the presence of baseline low hip bone mineral density. FEV1% predicted was not associated with accelerated bone mineral density decline after adjustment for covariates. Multivariate modeling showed moderate to severe visually assessed emphysema, and the 75th percentiles of CTX were independently associated with accelerated hip bone mineral density decline after adjustment for covariates. CONCLUSIONS:Emphysema and elevated markers of bone resorption are independently associated with progressive bone mineral density loss in smokers. These clinical markers may guide targeted bone mineral density screening and monitoring in smokers at highest risk.
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Project description:B10 cells can regulate inflammatory responses in innate immunity. Toll-like receptors (TLRs) play an important role in B cell-mediated immune responses in periodontal disease. This study aimed to determine the effects of TLR-activated B10 cells on periodontal bone loss in experimental periodontitis. Spleen B cells isolated from C57BL/6J mice were cultured with Porphyromonas gingivalis lipopolysaccharide (LPS) and cytosine-phospho-guanine (CpG) oligodeoxynucleotides for 48 h. B10-enriched CD1dhi CD5+ B cells were sorted by flow cytometry and were adoptively transferred to recipient mice through tail vein injection. At the same time, P. gingivalis-soaked ligatures were placed subgingivally around the maxillary second molars and remained there for 2 weeks before the mice were euthanized. Interleukin-10 (IL-10) production and the percentage of CD1dhi CD5+ B cells were significantly increased with treatment with P. gingivalis LPS plus CpG compared to those in mice treated with P. gingivalis LPS or CpG alone. Mice with CD1dhi CD5+ B cell transfer demonstrated reduced periodontal bone loss compared to the no-transfer group and the group with CD1dlo CD5- B cell transfer. Gingival IL-10 mRNA expression was significantly increased, whereas expressions of receptor activator of NF-?B ligand (RANKL)/osteoprotegerin (OPG), tumor necrosis factor alpha (TNF-?), and IL-1? were significantly inhibited in the CD1dhi CD5+ B cell transfer group. The percentages of CD19+ IL-10+ cells, CD19+ CD1dhi CD5+ cells, and P. gingivalis-binding CD19+ cells were significantly higher in recovered mononuclear cells from gingival tissues of the CD1dhi CD5+ B cell transfer group than in tissues of the no-transfer group and the CD1dlo CD5- B cell transfer group. This study indicated that the adoptive transfer of B10 cells alleviated periodontal inflammation and bone loss in experimental periodontitis in mice.
| S-EPMC5563573 | biostudies-literature
Project description:PurposePeriodontitis is the sixth most prevalent disease worldwide and periodontal bone loss (PBL) detection is crucial for its early recognition and establishment of the correct diagnosis and prognosis. Current radiographic assessment by clinicians exhibits substantial interobserver variation. Computer-assisted radiographic assessment can calculate bone loss objectively and aid in early bone loss detection. Understanding the rate of disease progression can guide the choice of treatment and lead to early initiation of periodontal therapy.MethodologyWe propose an end-to-end system that includes a deep neural network with hourglass architecture to predict dental landmarks in single, double and triple rooted teeth using periapical radiographs. We then estimate the PBL and disease severity stage using the predicted landmarks. We also introduce a novel adaptation of MixUp data augmentation that improves the landmark localisation.ResultsWe evaluate the proposed system using cross-validation on 340 radiographs from 63 patient cases containing 463, 115 and 56 single, double and triple rooted teeth. The landmark localisation achieved Percentage Correct Keypoints (PCK) of 88.9%, 73.9% and 74.4%, respectively, and a combined PCK of 83.3% across all root morphologies, outperforming the next best architecture by 1.7%. When compared to clinicians' visual evaluations of full radiographs, the average PBL error was 10.69%, with a severity stage accuracy of 58%. This simulates current interobserver variation, implying that diverse data could improve accuracy.ConclusionsThe system showed a promising capability to localise landmarks and estimate periodontal bone loss on periapical radiographs. An agreement was found with other literature that non-CEJ (Cemento-Enamel Junction) landmarks are the hardest to localise. Honing the system's clinical pipeline will allow for its use in intervention applications.
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Project description:OBJECTIVE:To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays. METHODS:Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance. RESULTS:The traditional model had an AUC of 0.660 (61.5% sensitivity and 64.0% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0% sensitivity and 75.5% specificity), which was significantly higher (P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5% sensitivity and specificity), which was significantly higher than the DL (P = 0.015) and traditional (P < 0.001) models. CONCLUSION:DL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors.
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Project description:Many previous studies, including the Next Generation Sequencing (NGS)-based ones, have shown the critical roles of RNA editing in biomedicine. Direct RNA sequencing emerges as another powerful technique to advance the understanding of RNA editing by new paradigms, especially in single-molecule and long-range characterization. The urgent gap is the accurate and robust identification of RNA editing at the single-molecule and single-nucleotide resolution from direct RNA sequencing. This is challenging due to the inherent nature of the context-dependence on the raw signals, which requires enormous training data with considerable diversity. Here we propose two coupled measures to address them: 1) an abductive deep learning strategy implemented as the software ReDD fully utilizes the widely accessible NGS-based RNA editing data as indirect labels of direct RNA sequencing to achieve the detection at the single-molecule level; 2) a cloud-based platform Argo-ReDD serves as a central database for assembling large and diverse data from the community to continuously train the abductive deep learning model, which also meets the community demand of a user-friendly way to perform RNA editing analyses, such as co-occurrence analysis, quantitative analysis and gene isoform-resolved analysis, based on the specific information from direct RNA sequencing.
2024-05-08 | GSE267030 | GEO