Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

Welch, Mattea; McIntosh, Chris; Purdie, Thomas G.; Wee, Leonard; Traverso, Alberto; Dekker, André; Haibe‐Kains, Benjamin; Jaffray, David A.

doi:10.1088/1361-6560/ab5427

Cited by 10 publications

(15 citation statements)

References 44 publications

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“…These 2,319 images are a subset of the full 3,211 image volume set that are independent from the training of the published CNN. When used on its own to make binary classifications (DA positive or DA negative) of entire patient CT volumes, the CNN yielded an MCC of 0.82 (p-value=0.0002; Figure 4B ) and an AUC of 0.97 (p-value=0.0002; Supplementary Figure 3 ), in line with the performance of the CNN found in the original study (an AUC of 0.92 ± 0.03 ) [8] .…”

Section: Convolutional Neural Network Detectionsupporting

confidence: 80%

“…A five-layer convolutional neural network (CNN) [8] was used as a secondary classification step in our method. This CNN is a binary DA classifier that takes 3D patient CT volumes as inputs ( Figure 2C ).…”

Section: Convolutional Neural Network Detectionmentioning

confidence: 99%

“…The published CNN [8] was tested on 2,319 CT image volumes (945 strong, 606 weak, and 768 without artifacts). These 2,319 images are a subset of the full 3,211 image volume set that are independent from the training of the published CNN.…”

Section: Convolutional Neural Network Detectionmentioning

confidence: 99%

“…Several studies have tried to address this data processing challenge by removing slices affected by DAs [3] or by using metal artifact reduction (MAR) algorithms [7] . Recently, a convolutional neural network (CNN) [8] and hand-crafted radiomic feature-based model [9] have been developed to detect the presence of DAs in CT volumes. However, to the best of our knowledge, no studies differentiated between DAs of different magnitudes or quantified how the location of these artifacts could affect quantitative imaging features used to train radiomic models.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid algorithm for dental artifact detection in large computed tomography datasets

Arrowsmith

Reiazi

Welch

et al. 2020

Preprint

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Computed tomography (CT) is one of the most common medical imaging modalities and the main technology used in radiomics research, the computational voxel-level analysis of medical images. Analysis of CT images is vulnerable to the effects of dental artifacts (DA) caused by metal implants or fillings. Running automated analysis pipelines with uncurated datasets can reduce performance and hamper future reproducibility on new datasets. This work introduces a new tool to detect the location and magnitude of DAs in CT images based on a combination of deep learning and conventional image processing algorithms. We show the utility of this new DA detector through an analysis of the correlations between radiomic features and the location of DAs in 2,319 CT axial volumes. We were able to predict the correct DA magnitude (no, weak or strong artifacts) yielding a Matthews correlation coefficient of 0.73 (p-value=0.0002), achieving the same level of agreement as human labellers. The algorithm was able to identify the location of the DAs in the CT volumes with performance on par with human labellers. Finally, our analysis of radiomic features showed that only when strong DAs were present, the proximity of the tumour to the mouth was highly correlated with specific radiomic features. Our results suggest that removing these features, or removing CT slices containing the DAs, could reduce these unwanted correlations.

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Section: Convolutional Neural Network Detectionsupporting

confidence: 80%

Section: Convolutional Neural Network Detectionmentioning

confidence: 99%

Section: Convolutional Neural Network Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid algorithm for dental artifact detection in large computed tomography datasets

Arrowsmith

Reiazi

Welch

et al. 2020

Preprint

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“…As automated treatment planning algorithms such as the RPA continue to advance, it is important to consider how to develop an automated method to mirror the management of dental artifacts in a typical clinical workflow. Recent work by Welch et al ( Welch et al, 2020 ) detects the presence or absence of dental artifact within a patient’s entire CT scan. The tool achieved a PR-AUC of 0.92 ± 0.03.…”

Section: Introductionmentioning

confidence: 99%

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow

Hernandez

Sjogreen

Nguyen

et al. 2021

Computerized Medical Imaging and Graphics

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Purpose: We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process. Methods: We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified. Results: Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans. Conclusion: The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.

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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review

Chen,

Dhawan,

Liu

et al. 2024

Clinical & Exp Dental Res

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ObjectivesArtificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision‐making in clinical dentistry and identify trends and research gaps in the current literature.Material and MethodsThis review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses Extension for Scoping Reviews (PRISMA‐ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full‐text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles.ResultsOf the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry ‐ particularly oral medicine, oral surgery, and orthodontics ‐ for direct clinical inference and segmentation. AI‐based image analysis was use in several components of the clinical decision‐making process, including diagnosis, detection or classification, prediction, and management.ConclusionsA variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.

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Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

Cited by 10 publications

References 44 publications

A hybrid algorithm for dental artifact detection in large computed tomography datasets

A hybrid algorithm for dental artifact detection in large computed tomography datasets

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow

Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review

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