scholarly journals Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis

2020 ◽  
Vol 8 (5) ◽  
pp. 67-74
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Decision Making ◽  
Oral Health ◽  
Alveolar Bone ◽  
Vascular Diseases ◽  
Diagnostic Methods ◽  
Pocket Depth ◽  
Clinical Attachment Loss ◽  
Gum Disease

Artificial Intelligence, Machine learning, deep learning and image processing is becoming popular in medical sciences. The present digitalized world is remodelling each facetadditionally impacting dentistry and medical field from patient record maintenance, data analysisto new diagnostic methods, novel interference waysand totally different treatment choices. Oral health contributes to various diseases and conditions like Endocarditis, Cardio vascular diseases, diabetes, osteoporosis, pregnancy and birth and many more. Bad breathe, tooth decay, periodontitis, oral abscess, tooth erosion, dentinal sensitivity and many more can be even trickier to detect in plain dental radiography. The most prevalent disease periodontitis is a gum disease when left untreated, leads to tooth loss and more hazardous complications. Early Prediction and Proper diagnosis in time will protect our health from the mentioned diseases which can be implemented by making use of emerging technologies to assist and support dentists in predictions and decision making. Hence focusing more on oral health, In the current paper, the most contributing risk factors and parameters like Pocket Depth, Black Triangles, Alveolar Bone Loss, Furcation, Periodontal Abscess, Smoking, Gingivitis, Clinical Attachment Loss, Mobility Etc. that progresses the disease were taken in to consideration and a Python code was implemented which can be used as a Decision making aid to check whether person suffers or likely to suffer in future or not suffering from the disease.In this paper, literature reviews on the various automated computerized methods used to detect and diagnose the disease were discussed and an attempt was made to clearly identify and describe both the clinical and radiological parameters that a dentist/Periodontist use as a metric to grade/assess the periodontitis. The present strategy can be enhanced as a tool and can be used as a decision making aid by dentists’ in the prediction of periodontitis and can also be used for demonstrating fresher’s or upcoming dentists the progress of gum disease, grading the severity of the disease and the associated risk factors considering clinical, radiological findings and adverse habits thereby improving overall time period taken for manual predictions.

scholarly journals Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making

2020 ◽  
Vol 175 ◽  
pp. 104860 ◽  
Author(s):  
M. Pilar Romero ◽  
Yu-Mei Chang ◽  
Lucy A. Brunton ◽  
Jessica Parry ◽  
Alison Prosser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Decision Making ◽  
Decision Tree ◽  
Disease Control ◽  
Bovine Tuberculosis ◽  
Control Decision ◽  
Tuberculosis Risk

scholarly journals The Use of Antimicrobial Photodynamic Therapy to Maintain a Hopeless Tooth With a PeriodonticEndodontic Lesion: A Case Report

2020 ◽  
Vol 11 (3) ◽  
pp. 355-360
Author(s):  
Nahid Derikvand ◽  
Masoud Hatami ◽  
Nasim Chiniforush ◽  
Seyedeh Sara Ghasemi
Keyword(s):  
Photodynamic Therapy ◽  
Case Report ◽  
Root Canal ◽  
Alveolar Bone ◽  
Antimicrobial Photodynamic Therapy ◽  
Pocket Depth ◽  
Clinical Attachment Loss ◽  
Tooth Number ◽  
End Stage ◽  
Grade Iii

Background: In spite of some advances in periodontal generative methods, it is impossible to stop progressive Loss of supporting alveolar bone in some end-stage periodontitis. The aim of this study is to report a kind of treatment modality which was seemed to be successes full in maintaining teeth. In this case-report, a hopeless tooth was saved by combined non-surgical periodontalendodontal treatment and antimicrobial photodynamic therapy (aPDT). Case Report: A 58-year-old male presented with a chief complaint of pain and mobility of tooth number 38. Clinical examinations revealed a periodontic-endodontic lesion with clinical attachment loss exceeding 10 mm and grade III mobility. To preserve the tooth, we operated nonsurgical periodontal treatment including scaling and root planning (SRP) plus root canal therapy (RCT) combined with intra-root canal non-aPDT laser decontamination. Then we applied laser pocket therapy with and without aPDT. Following 6 months of the aPDT treatment, the mobility and pocket depth of the tooth improved from grade III to I and from 10 to 3 millimeters respectively. Conclusion: aPDT is a novel adjunctive therapy that can be used for various conditions with microbial etiology. This case report demonstrated that aPDT might be effective in the treatment of periodontic-endodontic lesions in a hopeless tooth.

Epidemiological risk factors for periodontal pockets and clinical attachment loss among Greek adults

2018 ◽  
Author(s):  
Nikolaos Andreas Chrysanthakopoulos
Keyword(s):  
Risk Factors ◽  
Multinomial Logistic Regression ◽  
Economic Status ◽  
Pocket Depth ◽  
Clinical Attachment Loss ◽  
Attachment Loss ◽  
Probing Pocket Depth ◽  
Epidemiological Risk ◽  
Potential Risk Factors ◽  
Greek Adults

Aim: The aim of this study was to identify variables related to deep periodontal pockets and clinical attachment loss. Materials and methods: The study population consisted of 575 Greek adults, 259 males and 316 females aged 35 to 69 years who referred in a private practice for periodontal treatment. Participants completed a self-administered questionnaire which included several epidemiological variables and underwent an oral clinical examination. The analyses performed by multinomial logistic regression model to estimate the possible associations among the variables examined. Results: 31.3% of the participants showed a mean probing pocket depth of >6.00 mm, and 67.1% showed a mean clinical attachment loss of ≥5.0 mm. Male gender, lower socio-economic status, smoking; irregular dental follow-up and a diabetes mellitus history were consistent statistically significant potential risk factors for probing pocket depth of ≥4.00 mm and clinical attachment loss of ≥3.00 mm. Conclusion: These results confirm previous findings regarding the principal role of cigarette smoking and various epidemiological variables in the etiology of deep periodontal pockets and periodontal loss of attachment.

scholarly journals Periodontal and chronic kidney diseases: A modifiable association

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Foujan Jabbarzadehkhoei ◽  
Soheila Bakhshandeh ◽  
Mahshid Namdari ◽  
Mina Pakkhesal ◽  
Mohammad Hossein Khoshnevisan
Keyword(s):  
Health Status ◽  
Oral Health ◽  
Kidney Diseases ◽  
Periodontal Diseases ◽  
Treatment Protocol ◽  
Oral Health Status ◽  
Pocket Depth ◽  
Clinical Attachment Loss ◽  
Cross Sectional ◽  
Periodontal Index

Objectives: The purpose of this study was to assess the relationship between periodontal diseases and CKD duration. Methods: This descriptive cross-sectional study was conducted on referral CKD patients to a teaching hospital in 2017. Two instruments were used for data collection. The first one was a self-reported questionnaire regarding oral health status and patients’ behaviors. The second questionnaire was used for the clinical assessment of oral health status. Results: Out of 192 patients, 46.9% were male and 53.1% female with a mean (SD) age of 51.9 (±15.1) years. The mean duration of CKD was 7.70 (±7.34) years. About 67.7% of patients experienced toothache in the past year. Also, 67.7% had gingival bleeding (BOP), 34.4% had Clinical Attachment Loss (CAL) > 4 mm, and over 50% of patients had a pocket depth (PD) > 4 mm. By controlling the patient’s age, a direct correlation was detected between the duration of CKD and DMFT index (r=0.64, P<0.001). Moreover, the prolongation of the disease period was detected in patients with CAL>4 mm (P=0.02). Likewise, a direct correlation was detected between the duration of CKD and the periodontal index (r=0.48, P<0.001). Conclusion: Given the direct correlation between the periodontal conditions and duration of CKD, regular biannual dental visits are essential for CKD patients. All physicians are encouraged to include regular oral health checkups in the treatment protocol for CKD patients.

scholarly journals Fine-Tuning Multilevel Modeling of Risk Factors Associated with Nonsurgical Periodontal Treatment Outcome

2019 ◽  
Author(s):  
João Botelho ◽  
Vanessa Machado ◽  
Paulo Mascarenhas ◽  
Ricardo Alves ◽  
Maria Alzira Cavacas ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fine Tuning ◽  
Periodontal Treatment ◽  
Pocket Depth ◽  
Clinical Attachment Loss ◽  
Probing Depth ◽  
Nonsurgical Periodontal Therapy ◽  
Hierarchical Levels ◽  
Variance Component Models

This retrospective study aimed to investigate the effect of known risk factors on nonsurgical periodontal treatment (NSPT) response using a pocket depth fine-tuning multilevel linear model (MLM). Thirty-seven patients (24 males and 13 females) with moderate to severe chronic periodontitis were treated with nonsurgical periodontal therapy. Follow-up visits at 3, 6, and 12 months included measurement of several clinical periodontal parameters. Data were extracted from a database system. Probing depth (PD) and Clinical Attachment Loss (CAL) reductions after NSPT in an overall of 1416 initially affected sites (baseline PD &ge; 4 mm), distributed on 536 teeth, were analyzed against known risk factors at three hierarchical levels (patient, tooth and site). The variance component models fitted to assess the three-level variance of PD and CAL decrease for each post-treatment follow-up showed that all levels contributed significantly to the overall variance (P &lt; 0.001). Patients that underwent NSPT and were continually monitored had very curative results. All three hierarchical levels included risk factors who had impact on the to influence the magnitude of PD and CAL reduction. Specifically, the tooth&rsquo;s type, surfaces involved and teeth mobility site-level risk factors showed the highest influence on these reductions, being highly relevant factors for the NSPT success.

scholarly journals Correlation of periodontal diseases with intracranial aneurysm formation: novel predictive indicators

2021 ◽  
Author(s):  
Keyun Liu ◽  
Jia Sun ◽  
Lingling Shao ◽  
Hongwei He ◽  
Qinglin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Alveolar Bone ◽  
Periodontal Diseases ◽  
Plaque Index ◽  
Radiographic Examination ◽  
Gingival Index ◽  
Periodontal Health ◽  
Clinical Attachment Loss ◽  
Aneurysm Formation

Abstract Background and purpose: We investigated whether periodontal diseases, specifically, periodontitis and gingivitis, could be risk factors of the incidence of intracranial aneurysms (IAs).Methods: We performed a case–control study to compare the differences in the periodontal disease parameters of 281 cases that were divided into the IAs group and non-IAs group. All cases underwent complete radiographic examination for IAs and examination for periodontal health. Results: Comparing with those in the non-IAs group, the cases in the IAs group were older (53.95 ± 8.56 vs 47.79 ± 12.33, p < 0.001) and had a higher incidence of hypertension (76 vs 34, p = 0.006). Univariate logistic regression analysis revealed that age (>50 years) and hypertension were predictive risk factors of aneurysm formation (odds ratio [OR] 1.047, 95% confidence interval [95%CI] 1.022–1.073, p < 0.001 and OR 2.047, 95%CI 1.232–3.401, p = 0.006). In addition, univariate and multivariate logistic regression analyses showed that the parameters of periodontal diseases, including gingival index, plaque index, clinical attachment loss, and alveolar bone loss, were significantly associated with the occurrence of IAs (all p < 0.05). For further statistical investigation, the parameters of periodontal diseases were divided into four layers based on the quartered data. Poorer periodontal health condition (especially gingival index＞1.1 and plaque index＞1.5) had the correlation with IAs formation (p＝0.007 and p＜0.001). Conclusion: Severe gingivitis or periodontitis combining with hypertension, are significantly associated with the incidence of IAs.

scholarly journals Smoking a Dangerous Addiction: A Systematic Review on an Underrated Risk Factor for Oral Diseases

2021 ◽  
Vol 18 (21) ◽  
pp. 11003
Author(s):  
Naveed Ahmed ◽  
Sohaib Arshad ◽  
Syed Nahid Basheer ◽  
Mohmed Isaqali Karobari ◽  
Anand Marya ◽  
...  
Keyword(s):  
Systematic Review ◽  
Cigarette Smoking ◽  
Alveolar Bone ◽  
Periodontal Diseases ◽  
Research Articles ◽  
Oral Diseases ◽  
Web Database ◽  
Pocket Depth ◽  
Clinical Attachment Loss ◽  
Evidence Type

Despite growing knowledge of the adverse effects of cigarette smoking on general health, smoking is one of the most widely prevalent addictions around the world. Globally, about 1.1 billion smokers and over 8 million people die each year because of cigarette smoking. Smoking acts as a source for a variety of oral and systemic diseases. Various periodontal issues such as increased pocket depth, loss of alveolar bone, tooth mobility, oral lesions, ulcerations, halitosis, and stained teeth are more common among smokers. This systematic review was conducted according to the guidelines from PRISMA, and research articles were retrieved from the Web database sources on 31 May 2021. The quality of research articles was ensured by the type of evidence from combined schema incorporating as schema-13 evidence type description, Cochrane health promotion and public health field (CHPPHF), and the health gains notation framework-14 screening question for quality assessment of qualitative and quantitative studies. Smokers have been found to have bleeding on probing, periodontal pockets, and clinical attachment loss compared to nonsmokers. Oral and respiratory cancers are among the most lethal known diseases caused by cigarette smoking and other commonly occurring sequelae such as stained teeth, periodontal diseases, etc.

scholarly journals Correlation of periodontal diseases with intracranial aneurysm formation: novel predictive indicators

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Keyun Liu ◽  
Jia Sun ◽  
Lingling Shao ◽  
Hongwei He ◽  
Qinglin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Alveolar Bone ◽  
Periodontal Diseases ◽  
Plaque Index ◽  
Radiographic Examination ◽  
Gingival Index ◽  
Periodontal Health ◽  
Clinical Attachment Loss ◽  
Aneurysm Formation

Abstract Background We investigated whether periodontal diseases, specifically, periodontitis and gingivitis, could be risk factors of the incidence of intracranial aneurysms (IAs). Methods We performed a case–control study to compare the differences in the periodontal disease parameters of 281 cases that were divided into the IAs group and non-IAs group. All cases underwent complete radiographic examination for IAs and examination for periodontal health. Results Comparing with those in the non-IAs group, the cases in the IAs group were older (53.95 ± 8.56 vs 47.79 ± 12.33, p < 0.001) and had a higher incidence of hypertension (76 vs 34, p = 0.006). Univariate logistic regression analysis revealed that age (> 50 years) and hypertension were predictive risk factors of aneurysm formation (odds ratio [OR] 1.047, 95% confidence interval [95% CI] 1.022–1.073, p < 0.001 and OR 2.047, 95% CI 1.232–3.401, p = 0.006). In addition, univariate and multivariate logistic regression analyses showed that the parameters of periodontal diseases, including gingival index, plaque index, clinical attachment loss, and alveolar bone loss, were significantly associated with the occurrence of IAs (all p < 0.05). For further statistical investigation, the parameters of periodontal diseases were divided into four layers based on the quartered data. Poorer periodontal health condition (especially gingival index > 1.1 and plaque index > 1.5) had the correlation with IAs formation (p = 0.007 and p < 0.001). Conclusion Severe gingivitis or periodontitis, combining with hypertension, is significantly associated with the incidence of IAs.

scholarly journals Machine Learning to Monitor Diagnostic Safety Risks in Emergency Departments: A Study Protocol (Preprint)

2020 ◽  
Author(s):  
Moein Enayati ◽  
Mustafa Sir ◽  
Xingyu Zhang ◽  
Sarah Parker ◽  
Elizabeth Duffy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Decision Making ◽  
Emergency Departments ◽  
Diagnostic Errors ◽  
Care Team ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Diagnostic Decision ◽  
Diagnostic Decision Making

BACKGROUND Diagnostic decision-making, especially in emergency departments (EDs), is a highly complex cognitive process involving uncertainty and susceptibility to error. A combination of parameters including patient factors (e.g. history, behaviors, complexity, and comorbidity), provider/care-team factors (e.g. cognitive load, information gathering, and synthesis), and system factors (e.g. health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Records with potential diagnostic errors have been identified using electronic triggers that flag certain patterns of care (i.e., triggers), such as the escalation of care or death after ED discharge. Sophisticated data analytics and machine learning techniques that can be applied to existing electronic health record (EHR) datasets could shed light on potential risk factors influencing diagnostic decision-making. OBJECTIVE To identify variables contributing to potential diagnostic errors in the ED using large scale EHR data. METHODS We will apply trigger algorithms to EHR data repositories to generate a large dataset of trigger-positive and trigger-negative encounters. Samples from both sets will be validated using medical record reviews where we expect to find a higher number of diagnostic safety problems in the trigger positive subset. Advanced data mining and machine learning techniques will be used to evaluate relationships between certain patient, provider/care-team, and system risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. RESULTS This study received funding in February 2019, and is approved by the Institutional Review Board at two health systems. Trigger queries are being developed at both organizations and sample cohorts are being labeled using the triggers. Once completed, study data can inform important parameters for future clinical decision support systems to help identify risks that contribute to diagnostic errors. CONCLUSIONS Using large datasets to investigate risk factors (patient, provider/care team, and system-level) in the diagnostic process can provide mechanisms for future monitoring of diagnostic safety.

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