Federated Learning in Dentistry: Chances and Challenges

Rischke, Roman; Schneider, Lisa; Müller, K-R; Samek, Wojciech; Schwendicke, Falk; Krois, Joachim

doi:10.1177/00220345221108953

Cited by 28 publications

(15 citation statements)

References 36 publications

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“…The second goal was to create a web or cloud-based environment where datasets can be freely stored, trained, and validated in real time. Achieving these goals requires the proactive development of standard protocols to facilitate data sharing and integration, secure transmission and storage of large datasets, and enable federated learning 33 , 34 .…”

Section: Discussionmentioning

confidence: 99%

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset

Park

Huh

Lee

2023

Sci Rep

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This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.

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Section: Discussionmentioning

confidence: 99%

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset

Park

Huh

Lee

2023

Sci Rep

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“…In their conclusions, the authors encouraged larger sample sizes, accurate predictors, and external validation first before considering the use of these models in the clinical practice (Bashir et al, 2022). The present study utilized these recommendations in an international collaboration with the aim of aggregating different cohorts and increasing the sample size for model development and validation (Rischke et al, 2022). These cohorts had previously been used for other prognostic studies published in periodontology (Shi et al, 2020; Petsos et al, 2021; Saleh et al, 2021; Saydzai et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Development and international validation of logistic regression and machine‐learning models for the prediction of 10‐year molar loss

Troiano

Nibali

Petsos

et al. 2022

J Clinic Periodontology

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Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients.Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine-learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K-nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results:The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis-related molar loss. In addition, the ensembled model showed the best calibration performance.Conclusions: Through a multi-centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.

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“…Precision medicine is data hungry, so conventional orthodontic data sets could be expanded by genetic, epigenetic, proteomic, metabolic, and other "omic-based" data (Schwendicke and Krois 2022;Schwendicke F, Krois J. 2022b).…”

Section: Precision Orthodonticsmentioning

confidence: 99%

Artificial Intelligence in Orthodontics: Critical Review

Nordblom,

Büttner,

Schwendicke

2024

J Dent Res

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With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist’s task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

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Federated Learning in Dentistry: Chances and Challenges

Cited by 28 publications

References 36 publications

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset

Automated deep learning for classification of dental implant radiographs using a large multi-center dataset

Development and international validation of logistic regression and machine‐learning models for the prediction of 10‐year molar loss

Artificial Intelligence in Orthodontics: Critical Review

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