Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.

BACKGROUND: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. METHODS: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. RESULTS: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. CONCLUSIONS: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

Self-perceived oral health and orofacial appearance in an adult population, 60 years of age.

OBJECTIVE: The study aimed to compare self-perceived oral health and orofacial appearance in three different cohorts of 60-year-old individuals. METHOD: A cross-sectional design, based on data obtained from a questionnaire used in the Swedish National Study of Aging and Care. The sample comprised 478 individuals, from baseline, 2001-2003 (n = 191), 2007-2009 (n = 218) and 2014-2015 (n = 69). Comparisons were made within and between the cohorts, with bivariate analysis and Fisher's exact test. Statistical significance was considered at p < 0.05. RESULTS: The result showed that a low number of the participants reported self-perceived problems with oral health. Of the problems reported, a higher proportion in cohort 2014-2015 (39.3%) experienced problems with bleeding gums. The experience of bleeding gums increased between the cohorts 2001-2003 and 2014-2015 (p = 0.040) and between 2007-2009 and 2014-2015 (p = 0.017). The prevalence of discomfort with sensitive teeth was experienced in 7%-32%. Twice as many women compared to men experienced discomfort in all cohorts (no significant differences between the cohorts). Satisfaction with dental appearance was experienced in 75%-84%. Twice as many women compared to men were dissatisfied with their dental appearance in 2001-2003 (p = 0.011) and with discoloured teeth (p = 0.020). No significant differences could be seen between the cohorts regarding discomfort with dental appearance or discoloured teeth. CONCLUSION: The 60-year-olds irrespective of birth cohort, perceived their oral health and orofacial appearance as satisfactory.

Antibodies against Porphyromonas gingivalis in serum and saliva and their association with rheumatoid arthritis and periodontitis. Data from two rheumatoid arthritis cohorts in Sweden.

Background: Periodontitis and oral pathogenic bacteria can contribute to the development of rheumatoid arthritis (RA). A connection between serum antibodies to Porphyromonas gingivalis (P. gingivalis) and RA has been established, but data on saliva antibodies to P. gingivalis in RA are lacking. We evaluated antibodies to P. gingivalis in serum and saliva in two Swedish RA studies as well as their association with RA, periodontitis, antibodies to citrullinated proteins (ACPA), and RA disease activity. Methods: The SARA (secretory antibodies in RA) study includes 196 patients with RA and 101 healthy controls. The Karlskrona RA study includes 132 patients with RA ≥ 61 years of age, who underwent dental examination. Serum Immunoglobulin G (IgG) and Immunoglobulin A (IgA) antibodies and saliva IgA antibodies to the P. gingivalis-specific Arg-specific gingipain B (RgpB) were measured in patients with RA and controls. Results: The level of saliva IgA anti-RgpB antibodies was significantly higher among patients with RA than among healthy controls in multivariate analysis adjusted for age, gender, smoking, and IgG ACPA (p = 0.022). Saliva IgA anti-RgpB antibodies were associated with RA disease activity in multivariate analysis (p = 0.036). Anti-RgpB antibodies were not associated with periodontitis or serum IgG ACPA. Conclusion: Patients with RA had higher levels of saliva IgA anti-RgpB antibodies than healthy controls. Saliva IgA anti-RgpB antibodies may be associated with RA disease activity but were not associated with periodontitis or serum IgG ACPA. Our results indicate a local production of IgA anti-RgpB in the salivary glands that is not accompanied by systemic antibody production.

Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification.

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew's correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.

Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.

Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.

Sleep disturbance predicts worse cognitive performance in subsequent years: A longitudinal population-based cohort study.

BACKGROUND: Poor sleep is a potential modifiable risk factor for later life development cognitive impairment. The aim of this study is to examine if subjective measures of sleep duration and sleep disturbance predict future cognitive decline in a population-based cohort of 60, 66, 72 and 78-year-olds with a maximal follow up time of 18 years. METHODS: This study included participants from the Swedish National Study on Ageing and Care - Blekinge, with assessments 2001-2021. A cohort of 60 (n = 478), 66 (n = 623), 72 (n = 662) and 78 (n = 548) year-olds, were assessed at baseline and every 6 years until 78 years of age. Longitudinal associations between sleep disturbance (sleep scale), self-reported sleep duration and cognitive tests (Mini Mental State Examination and the Clock drawing test) were examined together with typical confounders (sex, education level, hypertension, hyperlipidemia, smoking status, physical inactivity and depression). RESULTS: There was an association between sleep disturbance at age 60 and worse cognitive function at ages 60, 66 and 72 years in fully adjusted models. The association was attenuated after bootstrap-analysis for the 72-year-olds. The items of the sleep scale most predictive of later life cognition regarded nightly awakenings, pain and itching and daytime naps. Long sleep was predictive of future worse cognitive function. CONCLUSION: Sleep disturbance was associated with worse future cognitive performance for the 60-year-olds, which suggests poor sleep being a risk factor for later life cognitive decline. Questions regarding long sleep, waking during the night, pain and itching and daytime naps should be further explored in future research and may be targets for intervention.

Magnetic resonance and diffusion tensor imaging of the adolescent rabbit growth plate of the knee.

PURPOSE: To assess the ability of MRI-DTI to evaluate growth plate morphology and activity compared with that of histomorphometry and micro-CT in rabbits. METHODS: The hind limbs of female rabbits aged 16, 20, and 24 wk (n = 4 per age group) were studied using a 9.4T MRI scanner with a multi-gradient echo 3D sequence and DTI in 14 directions (b-value = 984 s/mm2 ). After MRI, the right and left hind limb were processed for histological analysis and micro-CT, respectively. The Wilcoxon signed-rank test was used to evaluate the height and volume of the growth plate. Intraclass correlation and Pearson correlation coefficient were used to evaluate the association between DTI metrics and age. RESULTS: The growth plate height and volume were similar for all modalities at each time point and age. Age was correlated with all tractography and DTI metrics in both the femur and tibia. A correlation was also observed between all the metrics at both sites. Tract number and volume declined with age; however, tract length did not show any changes. The fractional anisotropy color map showed lateral diffusion centrally in the growth plate and perpendicular diffusion in the hypertrophic zone, as verified by histology and micro-CT. CONCLUSION: MRI-DTI may be useful for evaluating the growth plates.

Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia.

Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia. Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew's correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system's efficiency. Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535. Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.

An Intelligent Learning System for Unbiased Prediction of Dementia Based on Autoencoder and Adaboost Ensemble Learning.

Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%.

Oral health status among 60-year-old individuals born in 1941-1943 and 1954-1955 and 81-year-old individuals born in 1922-1924 and 1933-1934, respectively: a cross-sectional study.

OBJECTIVE: This study aimed to analyze the oral health status of four different birth cohorts: two cohorts of 60-year-olds born in 1941-1943 and 1954-1955 and 2 cohorts of 81-year-olds born in 1920-1922 and 1933-1934. MATERIAL AND METHODS: The study was based on data from an ongoing longitudinal population project, The Swedish National Study on Aging and Care (SNAC). Oral health status was repeatedly examined clinically and radiographically in 2001-2003 and 2014-2015, including 60- and 81-year-olds, in total 412 individuals. Statistical analyses were performed using independent-samples t test and Pearson's χ2 test. RESULTS: More individuals were dentate in 2014-2015 compared to 2001-2003 in the two age groups: 60 and 81 years (p < 0.001 for both). The mean number of teeth increased in the 60-year-olds from 24.2 to 27.0 and in the 81-year-olds from 14.3 to 20.2. The numbers of at least one intact tooth increased for both age groups (p < 0.001 and p < 0.004, respectively). In the age groups 81 years, there was an increase in having at least one PPD ≥ 6 mm (p < 0.016) and bone loss ≥ 5 mm (p < 0.029) between the two examinations. No such differences were found in the age groups of 60 years. CONCLUSION: Over 13 years, oral health improved for both 60- and 81-year-old age groups. The most significant changes were in the 81-year-olds where oral health had improved except for periodontal status. CLINICAL RELEVANCE: More natural teeth and impaired periodontal status potentially impact oral health and should increase focus on preventive and supportive dental care in older individuals.

CoGNIT Automated Tablet Computer Cognitive Testing in Patients With Mild Cognitive Impairment: Feasibility Study.

BACKGROUND: Early diagnosis of cognitive disorders is becoming increasingly important. Limited resources for specialist assessment and an increasing demographical challenge warrants the need for efficient methods of evaluation. In response, CoGNIT, a tablet app for automatic, standardized, and efficient assessment of cognitive function, was developed. Included tests span the cognitive domains regarded as important for assessment in a general memory clinic (memory, language, psychomotor speed, executive function, attention, visuospatial ability, manual dexterity, and symptoms of depression). OBJECTIVE: The aim of this study was to assess the feasibility of automatic cognitive testing with CoGNIT in older patients with symptoms of mild cognitive impairment (MCI). METHODS: Patients older than 55 years with symptoms of MCI (n=36) were recruited at the research clinic at the Blekinge Institute of Technology (BTH), Karlskrona, Sweden. A research nurse administered the Mini-Mental State Exam (MMSE) and the CoGNIT app on a tablet computer. Technical and testing issues were documented. RESULTS: The test battery was completed by all 36 patients. One test, the four-finger-tapping test, was performed incorrectly by 42% of the patients. Issues regarding clarity of instructions were found in 2 tests (block design test and the one finger-tapping test). Minor software bugs were identified. CONCLUSIONS: The overall feasibility of automatic cognitive testing with the CoGNIT app in patients with symptoms of MCI was good. The study highlighted tests that did not function optimally. The four-finger-tapping test will be discarded, and minor improvements to the software will be added before further studies and deployment in the clinic.

Psychological Health and Digital Social Participation of the Older Adults during the COVID-19 Pandemic in Blekinge, Sweden-An Exploratory Study.

COVID-19 has affected the psychological health of older adults directly and indirectly through recommendations of social distancing and isolation. Using the internet or digital tools to participate in society, one might mitigate the effects of COVID-19 on psychological health. This study explores the social participation of older adults through internet use as a social platform during COVID-19 and its relationship with various psychological health aspects. In this study, we used the survey as a research method, and we collected data through telephonic interviews; and online and paper-based questionnaires. The results showed an association of digital social participation with age and feeling lack of company. Furthermore, in addition, to the increase in internet use in older adults in Sweden during COVID-19, we conclude that digital social participation is essential to maintain psychological health in older adults.

Short Term Economic Evaluation of the Digital Platform "Support, Monitoring and Reminder Technology for Mild Dementia" (SMART4MD) for People with Mild Cognitive Impairment and their Informal Caregivers.

BACKGROUND: A randomized controlled trial of the SMART4MD tablet application was conducted for persons with mild cognitive impairment (PwMCI) and their informal caregivers to improve or maintain quality of life. OBJECTIVE: The objective was to conduct economic evaluation of SMART4MD compared to standard care in Sweden from a healthcare provider perspective based on a 6-month follow-up period. METHODS: Three hundred forty-five dyads were enrolled: 173 dyads in the intervention group and 172 in standard care. The primary outcome measures for PwMCI and informal caregivers were quality-adjusted life years (QALY). The results are presented as incremental cost-effectiveness ratios, and confidence intervals are calculated using non-parametric bootstrap procedure. RESULTS: For PwMCI, the mean difference in total costs between intervention and standard care was € 12 (95% CI: -2090 to 2115) (US$ = € 1.19) and the mean QALY change was -0.004 (95% CI: -0.009 to 0.002). For informal caregivers, the cost difference was - € 539 (95% CI: -2624 to 1545) and 0.003 (95% CI: -0.002 to 0.008) for QALY. The difference in cost and QALY for PwMCI and informal caregivers combined was -€ 527 (95% CI: -3621 to 2568) and -0.001 (95% CI: -0.008 to 0.006). Although generally insignificant differences, this indicates that SMART4MD, compared to standard care was: 1) more costly and less effective for PwMCI, 2) less costly and more effective for informal caregivers, and 3) less costly and less effective for PwMCI and informal caregivers combined. CONCLUSION: The cost-effectiveness of SMART4MD over 6 months is inconclusive, although the intervention might be more beneficial for informal caregivers than PwMCI.

An Instrument for Measuring Social Participation to Examine Older Adults' Use of the Internet as a Social Platform: Development and Validation Study.

BACKGROUND: Older people's use of the internet is increasingly coming into focus with the demographic changes of a growing older population. Research reports several benefits of older people's internet use and highlights problems such as various forms of inequality in use within the group. There is a need for consistent measurements to follow the development and use of the internet in this group and to be able to compare groups both within and between countries, as well as follow the changes over time. OBJECTIVE: The aim of this study was to create an instrument to measure an older person's perception of the benefits of their online social participation, unconnected to specific applications and services. The instrument to measure internet social participation proposed in this paper builds on social participation factors and is a multidimensional construct incorporating both social relations and societal connectedness. METHODS: A short instrument for measuring social participation over the internet was created. An exploratory factor analysis (EFA) was conducted in a random selection of persons aged 65 years or older (n=193) on 10 initial items. Further validation was made by confirmatory factor analysis (CFA) in the remaining group (n=193). RESULTS: A 1-factor solution for the social internet score was decided upon after exploratory factor analysis (EFA; based on a random sample of half the data set). None of the questionnaire items were excluded based on the EFA, as they all had high loadings, the lowest being 0.61. The Cronbach α coefficient was .92. The 1-factor solution explained 55% of the variance. CFA was performed and included all 10 questionnaire items in a 1-factor solution. Indices of goodness of fit of the model showed room for improvement. Removal of 4 questions in a stepwise procedure resulted in a 6-item model (χ26=13.985; χ2/degrees of freedom=1.554; comparative fit index=0.992; root mean square error of approximation=0.054; standardized root mean square residual=0.025). CONCLUSIONS: The proposed instrument can be used to measure digital social participation and coherence with society. The factor analysis is based on a sufficient sample of the general population of older adults in Sweden, and overall the instrument performed as expected.

Cross-Sectional Study of Sexual Activity and Satisfaction Among Older Adult's ≥60 Years of Age.

INTRODUCTION: Despite the rapidly increasing population of older adults, little is currently known about sexual activity and sexual satisfaction among the oldest people. AIM: The present study aimed to investigate sexual activity and sexual satisfaction among people of ≥60 years of age. We also examined whether sexual activity and sexual satisfaction were influenced by age, gender, cohabiting, socioeconomic factors, education, functional ability, and self-reported health. METHODS: We performed a descriptive analysis of self-stated sexual activity and sexual satisfaction among 1680 participants who were 60 years and older from the Swedish National Study on Aging and Care. Chi-square tests and logistic regression were used to analyze relationships between factors. MAIN OUTCOME MEASURE: Sexual activity and sexual satisfaction. RESULTS: Among participants aged ≥90 years, about 10% were sexually active. Within the total study population, 46% (654/1680) were sexually active. Overall, sexually activity was more commonly reported by men (55%) than women (40%). However, men in all age cohorts reported sexual dissatisfaction more commonly than women. In the total sample, 24% (246/1680) reported dissatisfaction with their sex life. Sexual activity and sexual satisfaction were positively associated with self-reported health and cohabitation. CONCLUSION: The present results suggest that sexual activity is present throughout life. For persons older than 90 years, about 10% of participants were sexually active, regardless of gender. Every third man reported dissatisfaction with his sex life. Women were more satisfied with their sex lives than men, and this difference varies more widely among age cohorts. These findings confirm that it is important that health professional take sexuality into account during caring encounters with older persons. M Stentagg, L Skär, JS Berglund, et al. Cross-Sectional Study of Sexual Activity and Satisfaction Among Older Adult's ≥60 Years of Age. Sex Med 2021;9:100316.

Periodontitis related to cardiovascular events and mortality: a long-time longitudinal study.

OBJECTIVE: The present study assessed if individuals ≥ 60 years of age with periodontitis are more likely to develop stroke or ischemic heart diseases, or at a higher risk of death for 17 years. MATERIAL AND METHODS: At baseline individuals ≥ 60 received a dental examination including a panoramic radiograph. Periodontitis was defined as having ≥ 30% sites with ≥ 5-mm distance from the cementoenamel junction to the marginal bone level. Medical records were annually reviewed from 2001 to 2018. Findings from the medical records identifying an ICD-10 code of stroke and ischemic heart diseases or death were registered. RESULTS: Associations between periodontitis and incidence of ischemic heart disease were found in this 17-year follow-up study in all individuals 60-93 years (HR: 1.5, CI: 1.1-2.1, p = 0.017), in women (HR: 2.1, CI: 1.3-3.4, p = 0.002), and in individuals 78-96 years (HR: 1.7, CI: 1.0-2.6, p = 0.033). Periodontitis was associated with mortality in all individuals (HR: 1.4, CI: 1.2-1.8, p = 0.002), specifically in men (HR: 1.5, CI: 1.1-1.9, p = 0.006) or in ages 60-72 years (HR: 2.2, CI: 1.5-3.2, p = 0.000). Periodontitis was more prevalent among men (OR: 1.8, CI: 1.3-2.4, p = 0.000). CONCLUSIONS: Individuals with periodontitis have an increased risk for future events of ischemic heart diseases and death. CLINICAL RELEVANCE: Improving periodontal health in older individuals may reduce overall mortality and ischemic heart diseases. Both dental and medical professionals should be aware of the associations and ultimately cooperate.

Older People's Use and Nonuse of the Internet in Sweden.

The use of the internet has considerably increased over recent years, and the importance of internet use has also grown as services have gone online. Sweden is largely an information society like other countries with high reported use amongst European countries. In line with digitalization development, society is also changing, and many activities and services today take place on the internet. This development could potentially lead to those older persons who do not use the internet or do not follow the development of services on the internet finding it difficult to take part in information and activities that no longer occur in the physical world. This has led to a digital divide between groups, where the older generations (60+), in particular, have been affected. In a large study of Sweden's adult population in 2019, 95 percent of the overall population was said to be internet users, and the corresponding number for users over 66 years of age was 84%. This study shows that the numbers reported about older peoples' internet use, most likely, are vastly overestimated and that real use is significantly lower, especially among the oldest age groups. We report that 62.4% of the study subjects are internet users and that this number most likely also is an overestimation. When looking at nonresponders to the questionnaire, we find that they display characteristics generally attributed to non-use, such as lower education, lower household economy, and lower cognitive functioning.

Comparison of reliability of magnetic resonance imaging using cartilage and T1-weighted sequences in the assessment of the closure of the growth plates at the knee.

BACKGROUND: Growth development is traditionally evaluated with plain radiographs of the hand and wrist to visualize bone structures using ionizing radiation. Meanwhile, MRI visualizes bone and cartilaginous tissue without radiation exposure. PURPOSE: To determine the state of growth plate closure of the knee in healthy adolescents and young adults and compare the reliability of staging using cartilage sequences and T1-weighted (T1W) sequence between pediatric and general radiologists. MATERIAL AND METHODS: A prospective, cross-sectional study of MRI of the knee with both cartilage and T1W sequences was performed in 395 male and female healthy subjects aged between 14.0 and 21.5 years old. The growth plate of the femur and the tibia were graded using a modified staging scale by two pediatric and two general radiologists. Femur and tibia were graded separately with both sequences. RESULTS: The intraclass correlation was overall excellent. The inter- and intra-observer agreement for pediatric radiologists on T1W was 82% (κ = 0.73) and 77% (κ = 0.65) for the femur and 90% (κ = 0.82) and 87% (κ = 0.75) for the tibia. The inter-observer agreement for general radiologists on T1W was 69% (κ = 0.56) for the femur and 56% (κ = 0.34) for the tibia. Cohen's kappa coefficient showed a higher inter- and intra-observer agreement for cartilage sequences than for T1W: 93% (κ = 0.86) and 89% (κ = 0.79) for the femur and 95% (κ = 0.90) and 91% (κ = 0.81) for the tibia. CONCLUSION: Cartilage sequences are more reliable than T1W sequence in the assessment of the growth plate in adolescents and young adults. Pediatric radiology experience is preferable.

Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach.

BACKGROUND: Bone age assessment (BAA) is used in numerous pediatric clinical settings as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical as the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods have drawbacks such as exposure of minors to radiation, they do not consider factors that might affect the bone age, and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals, it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA. OBJECTIVE: This study aims to investigate CA estimation through BAA in young individuals aged 14-21 years with machine learning methods, addressing the drawbacks of research using magnetic resonance imaging (MRI), assessment of multiple regions of interest, and other factors that may affect the bone age. METHODS: MRI examinations of the radius, distal tibia, proximal tibia, distal femur, and calcaneus were performed on 465 men and 473 women (aged 14-21 years). Measures of weight and height were taken from the subjects, and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, and type of residence during upbringing). Two pediatric radiologists independently assessed the MRI images to evaluate their stage of bone development (blinded to age, gender, and each other). All the gathered information was used in training machine learning models for CA estimation and minor versus adult classification (threshold of 18 years). Different machine learning methods were investigated. RESULTS: The minor versus adult classification produced accuracies of 0.90 and 0.84 for male and female subjects, respectively, with high recalls for the classification of minors. The CA estimation for the 8 age groups (aged 14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter, a lower error occurred only for the ages of 14 and 15 years. CONCLUSIONS: This study investigates CA estimation through BAA using machine learning methods in 2 ways: minor versus adult classification and CA estimation in 8 age groups (aged 14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results; however, for the second case, the BAA was not precise enough for the classification.

The association between rheumatoid arthritis and periodontal disease in a population-based cross-sectional case-control study.

BACKGROUND: The association between rheumatoid arthritis (RA) and periodontitis remains unclear. METHODS: We studied oral health and periodontitis in a population-based case-control study of individuals with ≥10 remaining teeth ≥61 years of age and either with, or without a diagnosis of RA. 126 dentate individuals with RA were recruited together with age-matched control individuals without RA. The control individuals were recruited from the general population from the same city (n = 249). A dental examination including a panoramic radiograph was performed on all participants. All individuals with RA were examined and medical records were reviewed by a rheumatologist. In the control group, none of the participants presented with symptoms of RA and their medical records were also negative. RESULTS: The RA group included more women (66.7% vs. 55.8%) (p < 0.01). Individuals in the RA group had a higher body mass index (BMI) (p < 0.001). A diagnosis of periodontitis was more common in the RA group (61.1%) than in the control group (33.7%) (p = 0.001). Binary logistic regression analysis identified that a BMI > 25 (OR 6.2, 95% CI 3.6, 10.5, p = 0.000), periodontitis (OR 2.5 95% CI 1.5, 4.2 p = 0.000), and female gender (OR 2.3, 95% CI 1.3-4.0, p = 0.003) were associated with RA. CONCLUSION: RA was associated a diagnosis of periodontitis.