@article{Jekauc:2024:RecognizingAffectiveStates,

title = {Recognizing affective states from the expressive behavior of tennis players using Convolutional Neural Networks},

author = {Darko Jekauc and Diana Burkart and Julian Fritsch and Marc Hesenius and Ole Meyer and Saquib Sarfraz and Rainer Stiefelhagen},

doi = {10.1016/j.knosys.2024.111856},

issn = {0950-7051},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Knowledge-Based Systems},

volume = {295},

abstract = {This study describes an AI model by leveraging advanced Convolutional Neural Networks (CNNs) to recognize affective states in real-world sports settings, particularly tennis matches. In contrast to prior studies that primarily utilized data acquired from actors and rudimentary statistical methods, the present research emphasizes the analysis of bodily expressions in real-life contexts, aiming for a more naturalistic representation of human emotions. Our CNN-based models demonstrate an accuracy rate of up to 68.9%, outperforming or matching human observers in many instances. Intriguingly, both the machine learning models and human observers exhibited a shared propensity to more effectively identify negative affective states, which may be attributed to the more intense and straightforward expression of these states. These results not only advance the state of the art in affective state recognition but also pave the way for broader applications, including in healthcare and automotive safety sectors, thereby constituting a significant advancement in the development of sophisticated and universally applicable emotional recognition systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Felsch:2023:CariesVisionTransformer,

title = {Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model},

author = {Marco Felsch and Ole Meyer and Anne Schlickenrieder and Paula Engels and Jule Schönewolf and Felicitas Zöllner and Roswitha Heinrich-Weltzien and Marc Hesenius and Reinhard Hickel and Volker Gruhn and Jan Kühnisch},

url = {https://www.nature.com/articles/s41746-023-00944-2},

doi = {10.1038/s41746-023-00944-2},

isbn = {2398-6352},

year  = {2023},

date = {2023-10-25},

journal = {npj Digital Medicine},

volume = {6},

issue = {1},

pages = {198},

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{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{Schoenwolf:2022:MIHDetection,

title = {Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs},

author = {Jule Schönewolf and Ole Meyer and Paula Engels and Anne Schlickenrieder and Reinhard Hickel and Volker Gruhn and Marc Hesenius and Jan Kühnisch},

doi = {10.1007/s00784-022-04552-4},

year  = {2022},

date = {2022-05-24},

urldate = {2022-01-01},

journal = {Clinical Oral Investigations},

abstract = {The aim of this study was to develop and validate a deep learning--based convolutional neural network (CNN) for the automated detection and categorization of teeth affected by molar-incisor-hypomineralization (MIH) on intraoral photographs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Engels:2022:PosteriorRestorationsAI,

title = {Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs},

author = {Paula Engels and Ole Meyer and Jule Schönewolf and Anne Schlickenrieder and Reinhard Hickel and Marc Hesenius and Volker Gruhn and Jan Kühnisch},

url = {https://www.sciencedirect.com/science/article/pii/S0300571222001737},

doi = {https://doi.org/10.1016/j.jdent.2022.104124},

issn = {0300-5712},

year  = {2022},

date = {2022-04-05},

urldate = {2022-01-02},

journal = {Journal of Dentistry},

volume = {121},

pages = {104124},

abstract = {Objectives 

Intraoral photographs might be considered the machine-readable equivalent of a clinical-based visual examination and can potentially be used to detect and categorize dental restorations. The first objective of this study was to develop a deep learning-based convolutional neural network (CNN) for automated detection and categorization of posterior composite, cement, amalgam, gold and ceramic restorations on clinical photographs. Second, this study aimed to determine the diagnostic accuracy for the developed CNN (test method) compared to that of an expert evaluation (reference standard). 

Methods The whole image set of 1761 images (483 of unrestored teeth, 570 of composite restorations, 213 of cements, 278 of amalgam restorations, 125 of gold restorations and 92 of ceramic restorations) was divided into a training set (N = 1407, 401, 447, 66, 231, 93, and 169, respectively) and a test set (N = 354, 82, 123, 26, 47, 32, and 44). The expert diagnoses served as a reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101--32 × 8d), which was trained by using image augmentation and transfer learning. Statistical analysis included the calculation of contingency tables, areas under the receiver operating characteristic curve and saliency maps. 

Results 

After training was complete, the CNN was able to categorize restorations correctly with the following diagnostic accuracy values: 94.9% for unrestored teeth, 92.9% for composites, 98.3% for cements, 99.2% for amalgam restorations, 99.4% for gold restorations and 97.8% for ceramic restorations. 

Conclusions 

It was possible to categorize different types of posterior restorations on intraoral photographs automatically with a good diagnostic accuracy. 

Clinical significance 

Dental diagnostics might be supported by artificial intelligence-based algorithms in the future. However, further improvements are needed to increase accuracy and practicability.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kühnisch2022,

title = {Caries Detection on Intraoral Images Using Artificial Intelligence},

author = {Jan Kühnisch and Ole Meyer and Marc Hesenius and Volker Gruhn},

url = {https://doi.org/10.1177/00220345211032524},

doi = {10.1177/00220345211032524},

year  = {2022},

date = {2022-01-02},

urldate = {2022-01-01},

journal = {Journal of Dental Research},

volume = {101},

number = {2},

pages = {158-165},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kuehnisch:2022:AutomatMIH,

title = {Automatisierte Erkennung von Molaren-Inzisiven- Hypomineralisationen (MIH) mithilfe künstlicher Intelligenz},

author = {Jan Kühnisch and Marc Hesenius and Ole Meyer},

year  = {2022},

date = {2022-01-01},

journal = {Quintessenz Zahnmedizin},

volume = {73},

number = {12},

pages = {1094-1096},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{diagnostics11091608,

title = {Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence},

author = {Anne Schlickenrieder and Ole Meyer and Jule Schönewolf and Paula Engels and Reinhard Hickel and Volker Gruhn and Marc Hesenius and Jan Kühnisch},

url = {https://www.mdpi.com/2075-4418/11/9/1608},

doi = {10.3390/diagnostics11091608},

issn = {2075-4418},

year  = {2021},

date = {2021-01-01},

journal = {Diagnostics},

volume = {11},

number = {9},

abstract = {The aim of the present study was to investigate the diagnostic performance of a trained convolutional neural network (CNN) for detecting and categorizing fissure sealants from intraoral photographs using the expert standard as reference. An image set consisting of 2352 digital photographs from permanent posterior teeth (461 unsealed tooth surfaces/1891 sealed surfaces) was divided into a training set (n = 1881/364/1517) and a test set (n = 471/97/374). All the images were scored according to the following categories: unsealed molar, intact, sufficient and insufficient sealant. Expert diagnoses served as the reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. A statistical analysis was performed, including the calculation of contingency tables and areas under the receiver operating characteristic curve (AUC). The results showed that the CNN accurately detected sealants in 98.7% of all the test images, corresponding to an AUC of 0.996. The diagnostic accuracy and AUC were 89.6% and 0.951, respectively, for intact sealant; 83.2% and 0.888, respectively, for sufficient sealant; 92.4 and 0.942, respectively, for insufficient sealant. On the basis of the documented results, it was concluded that good agreement with the reference standard could be achieved for automatized sealant detection by using artificial intelligence methods. Nevertheless, further research is necessary to improve the model performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Klumpp:2019:ProdLogisticsAndHCI,

title = {Production Logistics and Human-Computer Interaction---State-of-the-Art, Challenges and Requirements for the Future},

author = {Matthias Klumpp and Marc Hesenius and Ole Meyer and Caroline Ruiner and Volker Gruhn},

url = {https://doi.org/10.1007/s00170-019-03785-0},

doi = {10.1007/s00170-019-03785-0},

issn = {1433-3015},

year  = {2019},

date = {2019-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {105},

number = {9},

pages = {3691--3709},

abstract = {Current concept, development, and testing applications in production concerning Cyber-Physical Systems (CPS), Industry 4.0 (I40), and Internet of Things (IoT) are mainly addressing fully autonomous systems, fostered by an increase in available technologies regarding distributed decision-making, sensors, and actuators for robotics systems. This is applied also to production logistics settings with a multitude of transport tasks, e.g., between warehousing or material supply stations and production locations within larger production sites as for example in the automotive industry. In most cases, mixed environments where automated systems and humans collaborate (e.g., cobots) are not in the center of analysis and development endeavors although the worker's adoption and acceptance of new technologies are of crucial relevance. From an interdisciplinary research perspective, this constitutes an important research gap, as the future challenges for successful automated systems will rely mainly on human-computer interaction (HCI) in connection with an efficient collaboration between motivated workers, automated robotics, and transportation systems. We develop a HCI efficiency description in production logistics based on an interdisciplinary analysis consisting of three interdependent parts: (i) a production logistics literature review and process study, (ii) a computer science literature review and simulation study for an existing autonomous traffic control algorithm applicable to production logistics settings with the specific inclusion of human actors, and (iii) a work science analysis for automation settings referring to theoretical foundations and empirical findings regarding the management of workers in digital work settings. We conclude with practical implications and discuss avenues for future research and business applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}