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  • 1
    Online Resource
    Online Resource
    Comparator Choice in Studies Testing Endodontic Instrument Fatigue Resistance: A Network Analysis
    Elsevier BV ; 2019
    In:  Journal of Endodontics Vol. 45, No. 6 ( 2019-06), p. 784-790
    Details
    In: Journal of Endodontics, Elsevier BV, Vol. 45, No. 6 ( 2019-06), p. 784-790
    Type of Medium: Online Resource
    ISSN: 0099-2399
    URL: Article
    DOI: 10.1016/j.joen.2019.02.015
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 2083582-6
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  • 2
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    Long-term costs of post-restorations: 7-year practice-based results from Germany
    Springer Science and Business Media LLC ; 2021
    In:  Clinical Oral Investigations Vol. 25, No. 4 ( 2021-04), p. 2175-2181
    Details
    In: Clinical Oral Investigations, Springer Science and Business Media LLC, Vol. 25, No. 4 ( 2021-04), p. 2175-2181
    Abstract: We evaluated the initial and follow-up treatment costs of different post-restorations in a practice-based German healthcare setting. Methods A total of 139 incisors, canines, or premolars received post-restorations placed by eight general dental practitioners in Germany, and were followed over a mean ± SD 7.1 ± 4.5 years. Preformed metal (MP, n = 68), glass-fiber (GF, n = 28), or cast post-and-core buildups (MC, n = 23) had been used to retain crowns or bridge anchors. Preformed metal and glass-fiber had also been used to retain directly built up post-retained composite restorations (PC, n = 20). Material and treatment costs for the initial post-restorations as well as restorative, endodontic, or surgical re-treatments were estimated from a public-payer-perspective in Germany. Associations between total and annualized total costs and covariates were assessed using generalized linear modeling. The study was registered in the German Clinical Trials Register (DRKS-ID: DRKS00012938). Results MC showed highest initial treatment costs (557.23 Euro), but the least re-treatments (6/23, 26%), while PC showed lowest initial costs (203.52 Euro) but the most re-treatments (11/20, 55%). Costs for MP/GF post-crowns were initially similarly costly (496.47/496.87 Euro), and both also showed similar re-treatments (35%/36%). The overall annual failure rate was 5.2% (MC: 3.5%, MP: 4.6%, GF: 5.3%, PC: 10.3%). Including costs for the resulting re-treatments, mean total costs were 591.66 Euro (MC), 548.31 Euro (MP), 526.37 Euro (GF), and 361.81 Euro (PC). Annualized total costs were 171.36 Euro (MC), 141.75 Euro (MP), 146.12 Euro (GF), and 135.65 Euro (PC). Total and annualized total costs were highest for MC, with PC being the significantly less costly option ( p 〈 0.001). Conclusions Within German healthcare, both initial and follow-up costs for post-restorations were considerable. Saving costs initially may, at least partially, be offset by more complications long-term. Clinical relevance Dentists need to be aware that the placement of posts is not only initially costly but also comes with significant long-term costs for treating occurring complications. This should be communicated with patients and considered during treatment planning.
    Type of Medium: Online Resource
    ISSN: 1432-6981 , 1436-3771
    URL: Article
    DOI: 10.1007/s00784-020-03529-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 1472578-2
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  • 3
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    Generalizability of deep learning models for dental image analysis
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-03-17)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-03-17)
    Abstract: We assessed the generalizability of deep learning models and how to improve it. Our exemplary use-case was the detection of apical lesions on panoramic radiographs. We employed two datasets of panoramic radiographs from two centers, one in Germany (Charité, Berlin, n = 650) and one in India (KGMU, Lucknow, n = 650): First, U-Net type models were trained on images from Charité (n = 500) and assessed on test sets from Charité and KGMU (each n = 150). Second, the relevance of image characteristics was explored using pixel-value transformations, aligning the image characteristics in the datasets. Third, cross-center training effects on generalizability were evaluated by stepwise replacing Charite with KGMU images. Last, we assessed the impact of the dental status (presence of root-canal fillings or restorations). Models trained only on Charité images showed a (mean ± SD) F1-score of 54.1 ± 0.8% on Charité and 32.7 ± 0.8% on KGMU data ( p   〈  0.001/t-test). Alignment of image data characteristics between the centers did not improve generalizability. However, by gradually increasing the fraction of KGMU images in the training set (from 0 to 100%) the F1-score on KGMU images improved (46.1 ± 0.9%) at a moderate decrease on Charité images (50.9 ± 0.9%, p   〈  0.01). Model performance was good on KGMU images showing root-canal fillings and/or restorations, but much lower on KGMU images without root-canal fillings and/or restorations. Our deep learning models were not generalizable across centers. Cross-center training improved generalizability. Noteworthy, the dental status, but not image characteristics were relevant. Understanding the reasons behind limits in generalizability helps to mitigate generalizability problems.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-021-85454-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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  • 4
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    Patients’ Perspectives on Artificial Intelligence in Dentistry: A Controlled Study
    MDPI AG ; 2022
    In:  Journal of Clinical Medicine Vol. 11, No. 8 ( 2022-04-12), p. 2143-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 11, No. 8 ( 2022-04-12), p. 2143-
    Abstract: Background: As artificial intelligence (AI) becomes increasingly important in modern dentistry, we aimed to assess patients’ perspectives on AI in dentistry specifically for radiographic caries detection and the impact of AI-based diagnosis on patients’ trust. Methods: Validated questionnaires with Likert-scale batteries (1: “strongly disagree” to 5: “strongly agree”) were used to query participants’ experiences with dental radiographs and their knowledge/attitudes towards AI as well as to assess how AI-based communication of a diagnosis impacted their trust, belief, and understanding. Analyses of variance and ordinal logistic regression (OLR) were used (p 〈 0.05). Results: Patients were convinced that “AI is useful” (mean Likert ± standard deviation 4.2 ± 0.8) and did not fear AI in general (2.2 ± 1.0) nor in dentistry (1.6 ± 0.8). Age, education, and employment status were significantly associated with patients’ attitudes towards AI for dental diagnostics. When shown a radiograph with a caries lesion highlighted by an arrow, patients recognized the lesion significantly less often than when using AI-generated coloured overlays highlighting the lesion (p 〈 0.0005). AI-based communication did not significantly affect patients’ trust in dentists’ diagnosis (p = 0.44; OLR). Conclusions: Patients showed a positive attitude towards AI in dentistry. AI-supported diagnostics may assist communicating radiographic findings by increasing patients’ ability to recognize caries lesions on dental radiographs.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm11082143
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662592-1
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  • 5
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    Hyperparameter Tuning and Automatic Image Augmentation for Deep Learning-Based Angle Classification on Intraoral Photographs—A Retrospective Study
    MDPI AG ; 2022
    In:  Diagnostics Vol. 12, No. 7 ( 2022-06-23), p. 1526-
    Details
    In: Diagnostics, MDPI AG, Vol. 12, No. 7 ( 2022-06-23), p. 1526-
    Abstract: We aimed to assess the effects of hyperparameter tuning and automatic image augmentation for deep learning-based classification of orthodontic photographs along the Angle classes. Our dataset consisted of 605 images of Angle class I, 1038 images of class II, and 408 images of class III. We trained ResNet architectures for classification of different combinations of learning rate and batch size. For the best combination, we compared the performance of models trained with and without automatic augmentation using 10-fold cross-validation. We used GradCAM to increase explainability, which can provide heat maps containing the salient areas relevant for the classification. The best combination of hyperparameters yielded a model with an accuracy of 0.63–0.64, F1-score 0.61–0.62, sensitivity 0.59–0.65, and specificity 0.80–0.81. For all metrics, it was apparent that there was an ideal corridor of batch size and learning rate combinations; smaller learning rates were associated with higher classification performance. Overall, the performance was highest for learning rates of around 1–3 × 10−6 and a batch size of eight, respectively. Additional automatic augmentation improved all metrics by 5–10% for all metrics. Misclassifications were most common between Angle classes I and II. GradCAM showed that the models employed features relevant for human classification, too. The choice of hyperparameters drastically affected the performance of deep learning models in orthodontics, and automatic image augmentation resulted in further improvements. Our models managed to classify the dental sagittal occlusion along Angle classes based on digital intraoral photos.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics12071526
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662336-5
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  • 6
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    Predictors for tooth loss in periodontitis patients: Systematic review and meta‐analysis
    Wiley ; 2019
    In:  Journal of Clinical Periodontology Vol. 46, No. 7 ( 2019-07), p. 699-712
    Details
    In: Journal of Clinical Periodontology, Wiley, Vol. 46, No. 7 ( 2019-07), p. 699-712
    Abstract: A range of predictors for tooth loss in periodontitis patients have been reported. We performed a systematic review and meta‐analysis to assess the consistency and magnitude of any association between a total of 12 predictors and tooth loss. Materials and Methods Medline/Embase/Central were searched for longitudinal studies investigating the association between predictors and tooth loss in periodontitis patients. Random‐effects meta‐analysis was performed, and study quality assessed. Results Twenty studies (15,422 patients, mean follow‐up: 12 years) were included. The mean annual tooth loss/patient was 0.12 (min./max: 0.01/0.36). Older patients ( n  = 8 studies; OR : 1.05, 95% CI : 1.03–1.08/year), non‐compliant ones ( n  = 11; 1.51, 1.06–2.16), diabetics ( n  = 7; 1.80, 1.26–2.57), those with IL ‐1‐polymorphism ( n  = 3; 1.80; 1.29–2.52) and smokers ( n  = 15; 1.98, 1.58–2.48) had a significantly higher risk of tooth loss. Teeth with bone loss ( n  = 3; 1.04, 1.03–1.05/%), high probing pocket depth ( n  = 6; 3.19, 1.70–5.98), mobility ( n  = 4; 3.71, 1.65–8.38) and molars ( n  = 4; 4.22, 2.12–8.39), especially with furcation involvement ( n  = 5; 2.68, 1.75–4.08) also showed higher risks. Gender ( n  = 16; 0.95, 0.86–1.05) and endodontic affection ( n  = 3; 3.62, 0.99–13.2) were not significantly associated with tooth loss. Conclusions Older, non‐compliant, smoking or diabetic patients, and teeth with bone loss, high probing pocket depth, mobility, or molars, especially with furcation involvement showed higher risks of tooth loss.
    Type of Medium: Online Resource
    ISSN: 0303-6979 , 1600-051X
    URL: Issue
    URL: Article
    DOI: 10.1111/jcpe.2019.46.issue-7
    DOI: 10.1111/jcpe.13118
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2026349-1
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  • 7
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    Online Resource
    More teeth in more elderly: Periodontal treatment needs in Germany 1997–2030
    Wiley ; 2018
    In:  Journal of Clinical Periodontology Vol. 45, No. 12 ( 2018-12), p. 1400-1407
    Details
    In: Journal of Clinical Periodontology, Wiley, Vol. 45, No. 12 ( 2018-12), p. 1400-1407
    Abstract: With more teeth retained for longer in an ageing population, population‐wide periodontal treatment needs may increase. We assessed and projected periodontal treatment needs from 1997 to 2030 in Germany. Methods Partial‐mouth probing‐pocket depths ( PPD s) from repeated waves (1997, 2005, 2014) of the nationally representative German Oral Health Studies were transformed into full‐mouth PPD s via decision‐tree‐based ensemble‐modelling. In line with German healthcare‐regulations, teeth with PPD  ≥ 4 mm were regarded as needing periodontal treatment. Weighted means were interpolated cross‐sectionally by fitting spline‐curves and then regressed longitudinally 1997–2030. Results In 1997, younger adults (35–44 years old) had a mean of 7.4 teeth needing treatment (overall 93.8 million teeth); this decreased to 4.8 teeth (47.3 million teeth) in 2014. For 2030, we project 3.2 teeth (33.7 million teeth). In seniors, an increase was recorded (1997: 4.5 teeth, 33.5 million teeth; 2014: 7.5 teeth, 63.4 million teeth); this is expected to continue until 2030 (to 12.2 teeth, 140.8 million teeth). The cumulative number of teeth needing treatment increased from 2000 (355 million) to 2015 (365 million), and will increase further to 2030 (464 million). Conclusions Population‐wide periodontal treatment needs may increase until 2030, mainly in the elderly. Concepts for addressing, these growing needs are required.
    Type of Medium: Online Resource
    ISSN: 0303-6979 , 1600-051X
    URL: Issue
    URL: Article
    DOI: 10.1111/jcpe.2018.45.issue-12
    DOI: 10.1111/jcpe.13020
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2026349-1
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  • 8
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    Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification
    MDPI AG ; 2022
    In:  Diagnostics Vol. 12, No. 5 ( 2022-05-16), p. 1237-
    Details
    In: Diagnostics, MDPI AG, Vol. 12, No. 5 ( 2022-05-16), p. 1237-
    Abstract: High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ≥45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics12051237
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662336-5
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  • 9
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    Segmentation of Dental Restorations on Panoramic Radiographs Using Deep Learning
    MDPI AG ; 2022
    In:  Diagnostics Vol. 12, No. 6 ( 2022-05-25), p. 1316-
    Details
    In: Diagnostics, MDPI AG, Vol. 12, No. 6 ( 2022-05-25), p. 1316-
    Abstract: Convolutional Neural Networks (CNNs) such as U-Net have been widely used for medical image segmentation. Dental restorations are prominent features of dental radiographs. Applying U-Net on the panoramic image is challenging, as the shape, size and frequency of different restoration types vary. We hypothesized that models trained on smaller, equally spaced rectangular image crops (tiles) of the panoramic would outperform models trained on the full image. A total of 1781 panoramic radiographs were annotated pixelwise for fillings, crowns, and root canal fillings by dental experts. We used different numbers of tiles for our experiments. Five-times-repeated three-fold cross-validation was used for model evaluation. Training with more tiles improved model performance and accelerated convergence. The F1-score for the full panoramic image was 0.7, compared to 0.83, 0.92 and 0.95 for 6, 10 and 20 tiles, respectively. For root canals fillings, which are small, cone-shaped features that appear less frequently on the radiographs, the performance improvement was even higher (+294%). Training on tiles and pooling the results thereafter improved pixelwise classification performance and reduced the time to model convergence for segmenting dental restorations. Segmentation of panoramic radiographs is biased towards more frequent and extended classes. Tiling may help to overcome this bias and increase accuracy.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics12061316
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662336-5
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  • 10
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    Patients’ perspectives on the use of artificial intelligence in dentistry: a regional survey
    Springer Science and Business Media LLC ; 2023
    In:  Head & Face Medicine Vol. 19, No. 1 ( 2023-06-22)
    Details
    In: Head & Face Medicine, Springer Science and Business Media LLC, Vol. 19, No. 1 ( 2023-06-22)
    Abstract: The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients’ perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher’s exact tests with Monte Carlo approximation. Patients’ perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor–patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1–5 (42.3%) or 5–10 (46.8%) years. Older patients ( 〉  35 years) expected higher AI performance standards than younger patients (18–35 years) ( p   〈  0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients’ perceptions may allow professionals to shape AI-driven dentistry in the future.
    Type of Medium: Online Resource
    ISSN: 1746-160X
    URL: Article
    DOI: 10.1186/s13005-023-00368-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2202219-3
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