@article{VollmerVollmerLangetal.2022,
  author    = {Vollmer, Andreas and Vollmer, Michael and Lang, Gernot and Straub, Anton and Shavlokhova, Veronika and K{\"u}bler, Alexander and Gubik, Sebastian and Brands, Roman and Hartmann, Stefan and Saravi, Babak},
  title     = {Associations between periodontitis and COPD: An artificial intelligence-based analysis of NHANES III},
  series = {Journal of Clinical Medicine},
  volume    = {11},
  journal   = {Journal of Clinical Medicine},
  number    = {23},
  issn      = {2077-0383},
  doi       = {10.3390/jcm11237210},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312713},
  year      = {2022},
  abstract  = {A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70\% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953\% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.},
  language  = {en}
}
@article{WirtzGraviusAscherletal.2014,
  author    = {Wirtz, Dieter C. and Gravius, Sascha and Ascherl, Rudolf and Thorweihe, Miguel and Forst, Raimund and Noeth, Ulrich and Maus, Uwe M. and Wimmer, Matthias D. and Zeiler, Guenther and Deml, Moritz C.},
  title     = {Uncemented femoral revision arthroplasty using a modular tapered, fluted titanium stem 5-to 16-year results of 163 cases},
  series = {Acta Orthopaedica},
  volume    = {85},
  journal   = {Acta Orthopaedica},
  number    = {6},
  issn      = {1745-3674},
  doi       = {10.3109/17453674.2014.958809},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-114555},
  pages     = {562 - 569},
  year      = {2014},
  abstract  = {Background and purpose - Due to the relative lack of reports on the medium- to long-term clinical and radiographic results of modular femoral cementless revision, we conducted this study to evaluate the medium- to long-term results of uncemented femoral stem revisions using the modular MRP-TITAN stem with distal diaphyseal fixation in a consecutive patient series. Patients and methods - We retrospectively analyzed 163 femoral stem revisions performed between 1993 and 2001 with a mean follow-up of 10 (5-16) years. Clinical assessment included the Harris hip score (HHS) with reference to comorbidities and femoral defect sizes classified by Charnley and Paprosky. Intraoperative and postoperative complications were analyzed and the failure rate of the MRP stem for any reason was examined. Results - Mean HHS improved up to the last follow-up (37 (SD 24) vs. 79 (SD 19); p < 0.001). 99 cases (61\%) had extensive bone defects (Paprosky IIB-III). Radiographic evaluation showed stable stem anchorage in 151 cases (93\%) at the last follow-up. 10 implants (6\%) failed for various reasons. Neither a breakage of a stem nor loosening of the morse taper junction was recorded. Kaplan-Meier survival analysis revealed a 10-year survival probability of 97\% (95\% CI: 95-100). Interpretation - This is one of the largest medium- to longterm analyses of cementless modular revision stems with distal diaphyseal anchorage. The modular MRP-TITAN was reliable, with a Kaplan-Meier survival probability of 97\% at 10 years.},
  language  = {en}
}