The Detection of Dental Pathologies on Periapical Radiographs—Results from a Reliability Study

Meusburger, Theresa; Wülk, Annika; Keßler, Andreas; Heck, Katrin; Hickel, Reinhard; Dujic, Helena; Kühnisch, Jan

doi:10.3390/jcm12062224

Cited by 4 publications

(3 citation statements)

References 51 publications

(100 reference statements)

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“…The corresponding Kappa values showed substantial reliability, ranging from 0.454 to 0.482 (inter-examiner). The intra-examiner reliability in terms of Cohen's Kappa amounted to 0.739 [36].…”

Section: Categorisation Of Periodontal Bone Loss (Reference Standard)mentioning

confidence: 97%

Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks

Dujic,

Meyer,

Hoss

et al. 2023

Diagnostics

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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.

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Section: Categorisation Of Periodontal Bone Loss (Reference Standard)mentioning

confidence: 97%

Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks

Dujic,

Meyer,

Hoss

et al. 2023

Diagnostics

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“…In addition, the efficiency of the training was determined during a calibration course. Reproducibility of PBL within and between investigators was assessed using 150 periapical radiographs, and the corresponding inter-and intra-examiner reliability showed substantial kappa values [17]. The detailed kappa values are specified in Table 1.…”

Section: Categorization Of Periodontal Bone Loss (Reference Standard)mentioning

confidence: 99%

“…The measurement of clinical attachment loss by periodontal probing varies due to individual probing force, probe angulation, and varying probe tip diameter [15,16]. In addition, radiographic PBL evaluation represents a challenging task for a clinician due to possible variations in contrast and exposure angle as well as structural overlap, so that the interpretation of dental radiographs may lead to inconsistencies among dentists [17][18][19]. Here, the use of artificial intelligence (AI)-based diagnostics could reduce these diagnostic discrepancies.…”

Section: Introductionmentioning

confidence: 99%

Detection of Periodontal Bone Loss on Periapical Radiographs—A Diagnostic Study Using Different Convolutional Neural Networks

Hoss,

Meyer,

Wölfle

et al. 2023

JCM

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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.

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Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model

Latke,

Narawade

2024

Image and Vision Computing

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The Detection of Dental Pathologies on Periapical Radiographs—Results from a Reliability Study

Cited by 4 publications

References 51 publications

Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks

Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks

Detection of Periodontal Bone Loss on Periapical Radiographs—A Diagnostic Study Using Different Convolutional Neural Networks

Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model

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