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  -