@article{VollmerNaglerHoerneretal.2023,
  author    = {Vollmer, Andreas and Nagler, Simon and H{\"o}rner, Marius and Hartmann, Stefan and Brands, Roman C. and Breitenb{\"u}cher, Niko and Straub, Anton and K{\"u}bler, Alexander and Vollmer, Michael and Gubik, Sebastian and Lang, Gernot and Wollborn, Jakob and Saravi, Babak},
  title     = {Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery},
  series = {Heliyon},
  volume    = {9},
  journal   = {Heliyon},
  number    = {11},
  issn      = {2405-8440},
  doi       = {10.1016/j.heliyon.2023.e20752},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-350416},
  year      = {2023},
  abstract  = {Background Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.},
  language  = {en}
}
@article{VollmerVollmerLangetal.2023,
  author    = {Vollmer, Andreas and Vollmer, Michael and Lang, Gernot and Straub, Anton and K{\"u}bler, Alexander and Gubik, Sebastian and Brands, Roman C. and Hartmann, Stefan and Saravi, Babak},
  title     = {Automated assessment of radiographic bone loss in the posterior maxilla utilizing a multi-object detection artificial intelligence algorithm},
  series = {Applied Sciences},
  volume    = {13},
  journal   = {Applied Sciences},
  number    = {3},
  issn      = {2076-3417},
  doi       = {10.3390/app13031858},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-305050},
  year      = {2023},
  abstract  = {Periodontitis is one of the most prevalent diseases worldwide. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease. Since automated bone loss detection has many benefits, our goal was to develop a multi-object detection algorithm based on artificial intelligence that would be able to detect and quantify radiographic bone loss using standard two-dimensional radiographic images in the maxillary posterior region. This study was conducted by combining three recent online databases and validating the results using an external validation dataset from our organization. There were 1414 images for training and testing and 341 for external validation in the final dataset. We applied a Keypoint RCNN with a ResNet-50-FPN backbone network for both boundary box and keypoint detection. The intersection over union (IoU) and the object keypoint similarity (OKS) were used for model evaluation. The evaluation of the boundary box metrics showed a moderate overlapping with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all five folds were 0.694 and 0.611, respectively. Mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Despite only using a small and heterogeneous set of images for training, our results indicate that the algorithm is able to learn the objects of interest, although without sufficient accuracy due to the limited number of images and a large amount of information available in panoramic radiographs. Considering the widespread availability of panoramic radiographs as well as the increasing use of online databases, the presented model can be further improved in the future to facilitate its implementation in clinics.},
  language  = {en}
}
@article{VollmerSaraviVollmeretal.2022,
  author    = {Vollmer, Andreas and Saravi, Babak and Vollmer, Michael and Lang, Gernot Michael and Straub, Anton and Brands, Roman C. and K{\"u}bler, Alexander and Gubik, Sebastian and Hartmann, Stefan},
  title     = {Artificial intelligence-based prediction of oroantral communication after tooth extraction utilizing preoperative panoramic radiography},
  series = {Diagnostics},
  volume    = {12},
  journal   = {Diagnostics},
  number    = {6},
  issn      = {2075-4418},
  doi       = {10.3390/diagnostics12061406},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-278814},
  year      = {2022},
  abstract  = {Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen's kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100\% for InceptionV3) but low sensitivity (highest sensitivity 42.86\% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1-4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74\% to 95.02\%, whereas sensitivity ranged from 14.14\% to 59.60\%. Cohen's kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen's kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14\% to 95.24\%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction.},
  language  = {en}
}