Presentation and primary care management of temporomandibular disorders: A survey of primary care dentists in Santiago and Newcastle.

BACKGROUND: Primary care dentists are often the first point of call for people with temporomandibular disorders (TMD) but it is not known how many people present to their dentist with TMD or the forms of first-line management that are routinely offered. OBJECTIVES: To report rates of presentation of TMD and management offered in primary care by general dental practitioners in two similarly urban areas, Santiago, Chile and North-East England. METHODS: An online survey was developed and distributed to primary care dentists in both regions. Descriptive data were presented to quantify presentation rates and forms of management offered. RESULTS: Responses were received from 215 dentists practising in Chile and 46 in Newcastle. The majority reported seeing 1-2 patients weekly with TMD and less than one new presentation each week. Symptoms were most often treated conservatively and with self-management according to international guidelines. The form of self-management varied however and verbal instructions were often not backed up by written information. CONCLUSIONS: This research provides a useful starting point in understanding the presentation to, and initial treatment of TMD in primary care internationally. Limitations included the method of recruitment and potentially non-representative samples. Further research could build on this work by including more countries and using more structured sampling methods. The work will be useful in understanding and planning early care pathways for people experiencing TMD.

Intervención interdisciplinaria para la prevención primaria de la caries en lactantes de 0 a 18 meses / Interdisciplinary intervention for the primary prevention of dental caries in children under 18 months

Introducción: la caries es la enfermedad crónica más frecuente en la infancia. La presencia de caries en la dentición temporal es el principal factor de riesgo para desarrollar caries en la dentición definitiva. La mayoría de los factores de riesgo de la caries son modificables y pueden convertirse en elementos para la prevención y control de la enfermedad. Con el objetivo de reducir la incidencia de caries a la edad de 18 meses se diseña una intervención interdisciplinaria de prevención primaria dirigida a familias con niños que se visitan siguiendo el Protocol d’activitats preventives i de promoció de la salut a l’edat pediátrica (PAPPS). Material y métodos: ensayo clínico no aleatorizado, realizado en dos centros de asistencia primaria de Catalunya desde enero de 2019 hasta junio de 2022. En uno de los centros se diseñó e implementó una intervención educativa de prevención primaria de la caries con consejos y habilidades para las familias. En el otro centro se mantuvo el protocolo habitual de recomendaciones. Se evaluó y comparó la incidencia de caries en ambos grupos a la edad de 18 meses con un modelo de regresión logística estimado con el programa R. Resultados: la incidencia de caries a los 18 meses fue superior en los niños del grupo control (OR = 6,0; IC 95% 1,8-20,2), a pesar de que la valoración del riesgo de caries basada en el sistema llamado Caries Management by Risk Assessment (CAMBRA) indicó mayor riesgo de desarrollo de caries en los lactantes del grupo intervención. Conclusión: la intervención interdisciplinaria de prevención primaria de la caries incorporada en los programas de salud infantil reduce la incidencia de caries en los primeros años de vida. (AU)

Introduction: caries is the most common chronic disease in childhood. The presence of caries in the primary dentition is the main risk factor for developing caries in the permanent dentition. Most of the risk factors for caries are modifiable and can become elements for the prevention and control of the disease. With the goal of reducing the incidence of caries in children at age 18 months, we designed an interdisciplinary primary prevention intervention aimed at families with children who attended routine preventive visits within the PAPPS (“Protocol d’activitats preventives i de promoció de la salut a l’edat pediàtrica”) child health programme. Methodology: non-randomized clinical trial carried out in two primary care centres in Catalonia between January 2019 and June 2022. In one of the centres, an educational intervention for the primary prevention of caries was designed and implemented to provide families with guidance and skills. In the other centre, patients received standard care. The incidence of caries was assessed and compared in both groups at age 18 months by means of a logistic regression model fitted with the R software. Results: the incidence of caries at 18 months was higher in children in the control group (OR=6.0; 95% CI: 1.8-20.2), despite the fact that the caries risk assessment by means of the “Caries Management by Risk Assessment” (CAMBRA) protocol indicated a higher risk of caries in infants in the intervention group. Conclusion: the interdisciplinary primary caries prevention intervention integrated into the child health prevention and promotion programme achieved a reduction in the incidence of caries in early childhood. (AU)

Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach.

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)-YNet model for the segmentation step. This architecture includes networks to model each radiologist's noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose "for presentation" mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist's label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.

A deep learning framework to classify breast density with noisy labels regularization.

BACKGROUND AND OBJECTIVE: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. METHODS: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. RESULTS: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. CONCLUSIONS: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation.

BACKGROUND AND OBJECTIVE: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. METHODS: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. RESULTS: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. CONCLUSIONS: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.

Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.

BACKGROUND: The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. METHODS: A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. RESULTS: The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. CONCLUSION: Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction.

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC=0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC=0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.

Effectiveness of problem-solving technique in caring for family caregivers: a clinical trial study in an urban area of Catalonia (Spain).

AIMS AND OBJECTIVES: To evaluate the effectiveness of the problem-solving technique in reducing symptoms of anxiety and depression among primary caregivers and to describe and evaluate the process carried out by nurses to find strengths and areas of improvement. BACKGROUND: In Spain, home care for the chronically ill patients and their family caregivers should be a priority in health and social policies due to the increase in ageing population and the progressive increase in dependent individuals. One of the areas involved is home-based nursing and counselling for family caregivers. DESIGN: This is a clinical trial study (during 2007-2011) with a mixed analysis methodology. METHODS: Quantitative analysis was used to evaluate the effectiveness of the problem-solving technique in reducing symptoms of anxiety and depression. The clinical trial involved a control and experimental group and pre-post intervention measurements, using the Goldberg Scale. The practical application of the technique was evaluated by qualitative analysis. RESULTS: There was a statistically significant improvement in symptoms of anxiety and depression in the intervention group after application of the technique. Positive aspects and resistance factors in its implementation were noted. CONCLUSIONS: The problem-solving technique is a cost-effective intervention for reducing symptoms of anxiety and depression in family caregivers of the chronically ill patients. Positive aspects of the technique were satisfaction of the caregiver and nurse, and work done together based on reflection. Resistance factors identified were difficulty in maintaining written records and subjective perception of a lack of time in everyday practice for its consistent application. RELEVANCE TO CLINICAL PRACTICE: The problem-solving technique is an important tool to reduce the suffering of family caregivers of chronically ill patients and a prevention element of family claudication.

Validation of DM-Scan, a computer-assisted tool to assess mammographic density in full-field digital mammograms.

We developed a semi-automated tool to assess mammographic density (MD), a phenotype risk marker for breast cancer (BC), in full-field digital images and evaluated its performance testing its reproducibility, comparing our MD estimates with those obtained by visual inspection and using Cumulus, verifying their association with factors that influence MD, and studying the association between MD measures and subsequent BC risk. Three radiologists assessed MD using DM-Scan, the new tool, on 655 processed images (craniocaudal view) obtained in two screening centers. Reproducibility was explored computing pair-wise concordance correlation coefficients (CCC). The agreement between DM-Scan estimates and visual assessment (semi-quantitative scale, 6 categories) was quantified computing weighted kappa statistics (quadratic weights). DM-Scan and Cumulus readings were compared using CCC. Variation of DM-Scan measures by age, body mass index (BMI) and other MD modifiers was tested in regression mixed models with mammographic device as a random-effect term. The association between DM-Scan measures and subsequent BC was estimated in a case-control study. All BC cases in screening attendants (2007-2010) at a center with full-field digital mammography were matched by age and screening year with healthy controls (127 pairs). DM-Scan was used to blindly assess MD in available mammograms (112 cases/119 controls). Unconditional logistic models were fitted, including age, menopausal status and BMI as confounders. DM-Scan estimates were very reliable (pairwise CCC: 0.921, 0.928 and 0.916). They showed a reasonable agreement with visual MD assessment (weighted kappa ranging 0.79-0.81). DM-Scan and Cumulus measures were highly concordant (CCC ranging 0.80-0.84), but ours tended to be higher (4%-5% on average). As expected, DM-Scan estimates varied with age, BMI, parity and family history of BC. Finally, DM-Scan measures were significantly associated with BC (p-trend=0.005). Taking MD<7% as reference, OR per categories of MD were: OR7%-17%=1.32 (95% CI=0.59-2.99), OR17%-28%=2.28 (95% CI=1.03-5.04) and OR>=29%=3.10 (95% CI=1.35-7.14). Our results confirm that DM-Scan is a reliable tool to assess MD in full-field digital mammograms.

Women's features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN).

Measurement of mammographic density (MD), one of the leading risk factors for breast cancer, still relies on subjective assessment. However, the consistency of MD measurement in full-digital mammograms has yet to be evaluated. We studied inter- and intra-rater agreement with respect to estimation of breast density in full-digital mammograms, and tested whether any of the women's characteristics might have some influence on them. After an initial training period, three experienced radiologists estimated MD using Boyd scale in a left breast cranio-caudal mammogram of 1,431 women, recruited at three Spanish screening centres. A subgroup of 50 randomly selected images was read twice to estimate short-term intra-rater agreement. In addition, a reading of 1,428 of the images, performed 2 years before by one rater, was used to estimate long-term intra-rater agreement. Pair-wise weighted kappas with 95% bootstrap confidence intervals were calculated. Dichotomous variables were defined to identify mammograms in which any rater disagreed with other raters or with his/her own assessment, respectively. The association between disagreement and women's characteristics was tested using multivariate mixed logistic models, including centre as a random-effects term, and taking into account repeated measures when required. All quadratic-weighted kappa values for inter- and intra-rater agreement were excellent (higher than 0.80). None of the studied women's features, i.e. body mass index, brassiere size, menopause, nulliparity, lactation or current hormonal therapy, was associated with higher risk of inter- or intra-rater disagreement. However, raters differed significantly more in images that were classified in the higher-density MD categories, and disagreement in intra-rater assessment was also lower in low-density mammograms. The reliability of MD assessment in full-field digital mammograms is comparable to that for original or digitised images. The reassuring lack of association between subjects' MD-related characteristics and agreement suggests that bias from this source is unlikely.