TY  - JOUR
A1  - Herm, Lukas-Valentin
A1  - Steinbach, Theresa
A1  - Wanner, Jonas
A1  - Janiesch, Christian
T1  - A nascent design theory for explainable intelligent systems
JF  - Electronic Markets
N2  - Due to computational advances in the past decades, so-called intelligent systems can learn from increasingly complex data, analyze situations, and support users in their decision-making to address them. However, in practice, the complexity of these intelligent systems renders the user hardly able to comprehend the inherent decision logic of the underlying machine learning model. As a result, the adoption of this technology, especially for high-stake scenarios, is hampered. In this context, explainable artificial intelligence offers numerous starting points for making the inherent logic explainable to people. While research manifests the necessity for incorporating explainable artificial intelligence into intelligent systems, there is still a lack of knowledge about how to socio-technically design these systems to address acceptance barriers among different user groups. In response, we have derived and evaluated a nascent design theory for explainable intelligent systems based on a structured literature review, two qualitative expert studies, a real-world use case application, and quantitative research. Our design theory includes design requirements, design principles, and design features covering the topics of global explainability, local explainability, personalized interface design, as well as psychological/emotional factors.
KW  - artificial intelligence
KW  - explainable artificial intelligence
KW  - XAI
KW  - design science research
KW  - design theory
KW  - intelligent systems
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323809
SN  - 1019-6781
VL  - 32
IS  - 4
ER  - 
TY  - JOUR
A1  - Kunz, Felix
A1  - Stellzig-Eisenhauer, Angelika
A1  - Boldt, Julian
T1  - Applications of artificial intelligence in orthodontics — an overview and perspective based on the current state of the art
JF  - Applied Sciences
N2  - Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care.
KW  - orthodontics
KW  - artificial intelligence
KW  - machine learning
KW  - deep learning
KW  - cephalometry
KW  - age determination by skeleton
KW  - tooth extraction
KW  - orthognathic surgery
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-310940
SN  - 2076-3417
VL  - 13
IS  - 6
ER  - 
TY  - JOUR
A1  - Vollmer, Andreas
A1  - Saravi, Babak
A1  - Vollmer, Michael
A1  - Lang, Gernot Michael
A1  - Straub, Anton
A1  - Brands, Roman C.
A1  - Kübler, Alexander
A1  - Gubik, Sebastian
A1  - Hartmann, Stefan
T1  - Artificial intelligence-based prediction of oroantral communication after tooth extraction utilizing preoperative panoramic radiography
JF  - Diagnostics
N2  - 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.
KW  - artificial intelligence
KW  - deep learning
KW  - X-ray
KW  - tooth extraction
KW  - oroantral fistula
KW  - operative planning
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-278814
SN  - 2075-4418
VL  - 12
IS  - 6
ER  - 
TY  - JOUR
A1  - Vollmer, Andreas
A1  - Vollmer, Michael
A1  - Lang, Gernot
A1  - Straub, Anton
A1  - Shavlokhova, Veronika
A1  - Kübler, Alexander
A1  - Gubik, Sebastian
A1  - Brands, Roman
A1  - Hartmann, Stefan
A1  - Saravi, Babak
T1  - Associations between periodontitis and COPD: An artificial intelligence-based analysis of NHANES III
JF  - Journal of Clinical Medicine
N2  - 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.
KW  - COPD
KW  - periodontitis
KW  - bone loss
KW  - machine learning
KW  - prediction
KW  - artificial intelligence
KW  - model
KW  - gingivitis
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-312713
SN  - 2077-0383
VL  - 11
IS  - 23
ER  - 
TY  - JOUR
A1  - Henckert, David
A1  - Malorgio, Amos
A1  - Schweiger, Giovanna
A1  - Raimann, Florian J.
A1  - Piekarski, Florian
A1  - Zacharowski, Kai
A1  - Hottenrott, Sebastian
A1  - Meybohm, Patrick
A1  - Tscholl, David W.
A1  - Spahn, Donat R.
A1  - Roche, Tadzio R.
T1  - Attitudes of anesthesiologists toward artificial intelligence in anesthesia: a multicenter, mixed qualitative–quantitative study
JF  - Journal of Clinical Medicine
N2  - Artificial intelligence (AI) is predicted to play an increasingly important role in perioperative medicine in the very near future. However, little is known about what anesthesiologists know and think about AI in this context. This is important because the successful introduction of new technologies depends on the understanding and cooperation of end users. We sought to investigate how much anesthesiologists know about AI and what they think about the introduction of AI-based technologies into the clinical setting. In order to better understand what anesthesiologists think of AI, we recruited 21 anesthesiologists from 2 university hospitals for face-to-face structured interviews. The interview transcripts were subdivided sentence-by-sentence into discrete statements, and statements were then grouped into key themes. Subsequently, a survey of closed questions based on these themes was sent to 70 anesthesiologists from 3 university hospitals for rating. In the interviews, the base level of knowledge of AI was good at 86 of 90 statements (96%), although awareness of the potential applications of AI in anesthesia was poor at only 7 of 42 statements (17%). Regarding the implementation of AI in anesthesia, statements were split roughly evenly between pros (46 of 105, 44%) and cons (59 of 105, 56%). Interviewees considered that AI could usefully be used in diverse tasks such as risk stratification, the prediction of vital sign changes, or as a treatment guide. The validity of these themes was probed in a follow-up survey of 70 anesthesiologists with a response rate of 70%, which confirmed an overall positive view of AI in this group. Anesthesiologists hold a range of opinions, both positive and negative, regarding the application of AI in their field of work. Survey-based studies do not always uncover the full breadth of nuance of opinion amongst clinicians. Engagement with specific concerns, both technical and ethical, will prove important as this technology moves from research to the clinic.
KW  - artificial intelligence
KW  - machine learning
KW  - anesthesia
KW  - anesthesiology
KW  - qualitative research
KW  - clinical decision support
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311189
SN  - 2077-0383
VL  - 12
IS  - 6
ER  - 
TY  - JOUR
A1  - Vollmer, Andreas
A1  - Vollmer, Michael
A1  - Lang, Gernot
A1  - Straub, Anton
A1  - Kübler, Alexander
A1  - Gubik, Sebastian
A1  - Brands, Roman C.
A1  - Hartmann, Stefan
A1  - Saravi, Babak
T1  - Automated assessment of radiographic bone loss in the posterior maxilla utilizing a multi-object detection artificial intelligence algorithm
JF  - Applied Sciences
N2  - 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.
KW  - radiographic bone loss
KW  - alveolar bone loss
KW  - maxillofacial surgery
KW  - deep learning
KW  - classification
KW  - artificial intelligence
KW  - object detection
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305050
SN  - 2076-3417
VL  - 13
IS  - 3
ER  - 
TY  - JOUR
A1  - Herm, Lukas-Valentin
A1  - Janiesch, Christian
A1  - Fuchs, Patrick
T1  - Der Einfluss von menschlichen Denkmustern auf künstliche Intelligenz – eine strukturierte Untersuchung von kognitiven Verzerrungen
JF  - HMD Praxis der Wirtschaftsinformatik
N2  - Künstliche Intelligenz (KI) dringt vermehrt in sensible Bereiche des alltäglichen menschlichen Lebens ein. Es werden nicht mehr nur noch einfache Entscheidungen durch intelligente Systeme getroffen, sondern zunehmend auch komplexe Entscheidungen. So entscheiden z. B. intelligente Systeme, ob Bewerber in ein Unternehmen eingestellt werden sollen oder nicht. Oftmals kann die zugrundeliegende Entscheidungsfindung nur schwer nachvollzogen werden und ungerechtfertigte Entscheidungen können dadurch unerkannt bleiben, weshalb die Implementierung einer solchen KI auch häufig als sogenannte Blackbox bezeichnet wird. Folglich steigt die Bedrohung, durch unfaire und diskriminierende Entscheidungen einer KI benachteiligt behandelt zu werden. Resultieren diese Verzerrungen aus menschlichen Handlungen und Denkmustern spricht man von einer kognitiven Verzerrung oder einem kognitiven Bias. Aufgrund der Neuigkeit dieser Thematik ist jedoch bisher nicht ersichtlich, welche verschiedenen kognitiven Bias innerhalb eines KI-Projektes auftreten können. Ziel dieses Beitrages ist es, anhand einer strukturierten Literaturanalyse, eine gesamtheitliche Darstellung zu ermöglichen. Die gewonnenen Erkenntnisse werden anhand des in der Praxis weit verbreiten Cross-Industry Standard Process for Data Mining (CRISP-DM) Modell aufgearbeitet und klassifiziert. Diese Betrachtung zeigt, dass der menschliche Einfluss auf eine KI in jeder Entwicklungsphase des Modells gegeben ist und es daher wichtig ist „mensch-ähnlichen“ Bias in einer KI explizit zu untersuchen.
N2  - Artificial intelligence (AI) is increasingly penetrating sensitive areas of everyday human life, resulting in the ability to support humans in complex and difficult tasks. The result is that intelligent systems are capable of handling not only simple but also complex tasks. For example, this includes deciding whether an applicant should be hired or not. Oftentimes, this decision-making can be difficult to comprehend, and consequently incorrect decisions may remain undetected, which is why these implementations are often referred to as a so-called black box. Consequently, there is the threat of unfair and discriminatory decisions by an intelligent system. If these distortions result from human actions and thought patterns, it is referred to as a cognitive bias. However, due to the novelty of this subject, it is not yet apparent which different cognitive biases can occur within an AI project. The aim of this paper is to provide a holistic view through a structured literature review. Our insights are processed and classified according to the Cross-Industry Standard Process for Data Mining (CRISP-DM) model, which is widely used in practice. This review reveals that human influence on an AI is present in every stage of the model’s development process and that “human-like” biases in an AI must be examined explicitly.
T2  - The impact of human thinking on artificial intelligence – a structured investigation of cognitive biases
KW  - Menschliche Denkmuster
KW  - Maschinelles Lernen
KW  - Künstliche Intelligenz
KW  - Literaturanalyse
KW  - cognitive biases
KW  - machine learning
KW  - artificial intelligence
KW  - literature review
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323787
SN  - 1436-3011
VL  - 59
IS  - 2
ER  - 
TY  - JOUR
A1  - Loda, Sophia
A1  - Krebs, Jonathan
A1  - Danhof, Sophia
A1  - Schreder, Martin
A1  - Solimando, Antonio G.
A1  - Strifler, Susanne
A1  - Rasche, Leo
A1  - Kortüm, Martin
A1  - Kerscher, Alexander
A1  - Knop, Stefan
A1  - Puppe, Frank
A1  - Einsele, Hermann
A1  - Bittrich, Max
T1  - Exploration of artificial intelligence use with ARIES in multiple myeloma research
JF  - Journal of Clinical Medicine
N2  - Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.
KW  - natural language processing
KW  - ontology
KW  - artificial intelligence
KW  - multiple myeloma
KW  - real world evidence
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197231
SN  - 2077-0383
VL  - 8
IS  - 7
ER  - 
TY  - JOUR
A1  - Kazuhino, Koshino
A1  - Werner, Rudolf A.
A1  - Toriumi, Fuijo
A1  - Javadi, Mehrbod S.
A1  - Pomper, Martin G.
A1  - Solnes, Lilja B.
A1  - Verde, Franco
A1  - Higuchi, Takahiro
A1  - Rowe, Steven P.
T1  - Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images
JF  - Tomography
N2  - Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%-60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.
KW  - AI
KW  - Magnetresonanztomografie
KW  - artificial intelligence
KW  - magnetic resonance imaging
KW  - MRI
KW  - DCGAN
KW  - GAN
KW  - stroke
KW  - machine learning
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172185
VL  - 4
IS  - 4
ER  - 
TY  - JOUR
A1  - Janiesch, Christian
A1  - Zschech, Patrick
A1  - Heinrich, Kai
T1  - Machine learning and deep learning
JF  - Electronic Markets
N2  - Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.
KW  - analytical model building
KW  - machine learning
KW  - deep learning
KW  - artificial intelligence
KW  - artificial neural networks
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270155
SN  - 1422-8890
VL  - 31
IS  - 3
ER  -