@article{Ruiner:2023:JobDemandsTechnology,

title = {Job demands and resources when using technologies at work – development of a digital work typology},

author = {Caroline Ruiner and Christina Elisabeth Debbing and Vera Hagemann and Martina Schaper and Matthias Klumpp and Marc Hesenius},

url = {https://doi.org/10.1108/ER-11-2021-0468},

doi = {10.1108/ER-11-2021-0468},

isbn = {0142-5455},

year  = {2023},

date = {2023-01-01},

journal = {Employee Relations: The International Journal},

volume = {45},

number = {1},

pages = {190–208},

publisher = {Emerald Publishing Limited},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{diagnostics13233562,

title = {Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks},

author = {Helena Dujic and Ole Meyer and Patrick Hoss and Uta Christine Wölfle and Annika Wülk and Theresa Meusburger and Leon Meier and Volker Gruhn and Marc Hesenius and Reinhard Hickel and Jan Kühnisch},

url = {https://www.mdpi.com/2075-4418/13/23/3562},

doi = {10.3390/diagnostics13233562},

issn = {2075-4418},

year  = {2023},

date = {2023-01-01},

journal = {Diagnostics},

volume = {13},

number = {23},

abstract = {Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hoss:2023:PeriodontalBoneLoss,

title = {Detection of Periodontal Bone Loss on Periapical Radiographs - A Diagnostic Study Using Different Convolutional Neural Networks},

author = {Patrick Hoss and Ole Meyer and Uta Christine Wölfle and Annika Wülk and Theresa Meusburger and Leon Meier and Reinhard Hickel and Volker Gruhn and Marc Hesenius and Jan Kühnisch and Helena Dujic},

doi = {10.3390/jcm12227189},

issn = {2077-0383},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Clinical Medicine},

volume = {12},

number = {22},

abstract = {Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{saeed_power_2023,

title = {The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach},

author = {Mudassir Saeed and Asma Naseer and Hassan Masood and Shafiq Ur Rehman and Volker Gruhn},

url = {https://ieeexplore.ieee.org/document/10318035/},

doi = {10.1109/ACCESS.2023.3332628},

issn = {2169-3536},

year  = {2023},

date = {2023-01-01},

urldate = {2023-12-20},

journal = {IEEE Access},

volume = {11},

pages = {130330–130344},

keywords = {},

pubstate = {published},

tppubtype = {article}

}