Depression of the maxillary sinus anterior wall and its influence on panoramic radiography appearance

Yoshida, Kazuhito; Fukuda, Motoki; Gotoh, Keinosuke; Ariji, Eiichiro

doi:10.1259/dmfr.20170126

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The insertion of dental implants into the maxillary sinus is the most common complication that occurs in the maxilla, and various studies have been conducted on the shape of the maxillary sinus associated with implant placement. In addition, various studies have been conducted on the factors affecting the size and shape of the maxillary sinus and their influence on panoramic features [ 6 , 17 , 18 ].…”

Section: Discussionmentioning

confidence: 99%

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Nasal cavity perforation by implant fixtures: case series with emphasis on panoramic imaging of nasal cavity extending posteriorly

Yeom,

Huh,

et al. 2023

Head Face Med

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The nasal cavity is an important landmark when considering implant insertion into the anterior region of the maxillary arch. The perforation of implants into the nasal cavity may cause complications, such as implant migration, inflammation, or changes in nasal airflow; thus, precise assessment of the nasal cavity is mandatory.Three cases of nasal cavity perforation by dental implants are presented, including one case of implant fixture migration into the nasal cavity. On panoramic radiographs of the patients, the following common features were observed: the horizontal radiopaque line of the hard palate was observed to be inferior to or similar to that of the antral floor and the bone between the lateral wall of the nasal cavity and the medial wall of the maxillary sinus was emphasized in a triangular shape.When the maxillary sinus is small and alveolar bone resorption is severe, panoramic evaluation may cause overestimation of the available residual bone, particularly in the maxillary canine/premolar region. Therefore, the residual bone should be reevaluated three-dimensionally to measure the exact bony shape and volume.

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Section: Discussionmentioning

confidence: 99%

“…Yoshida et al revealed the influence of depression of the maxillary sinus anterior wall on panoramic radiographic appearance [ 17 ]. They showed that the diagonal line on a panoramic image was related to the depression of the anterior wall of the maxillary sinus.…”

Section: Discussionmentioning

confidence: 99%

Nasal cavity perforation by implant fixtures: case series with emphasis on panoramic imaging of nasal cavity extending posteriorly

Yeom,

Huh,

et al. 2023

Head Face Med

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“…Four videos wrongly identified the posterior wall of the maxillary sinus, with half of them pointed at the zygomatic process of the maxilla (abovementioned) and the other half of them pointed at the diagonal line related to the depression of the anterior wall of the maxillary sinus. This depression is seldom covered by the literature, and readers can refer to [ 25 ] for its radiographic manifestation on panoramic radiography. It might be constructive for students to learn from errors, only if the errors could be identified and rectified [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Content Analysis of YouTube Videos on Radiographic Anatomy on Dental Panoramic Images

Yeung¹

2022

Healthcare

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The radiographic anatomy on dental panoramic images is essential knowledge for proper diagnosis and treatment planning purposes. No prior study has examined the content of YouTube videos with regard to radiographic anatomy on panoramic radiography. The objective of this study was to provide a content analysis on these videos. The initial search string was: (panoramic anatomy). An additional search was performed with the search string: (OPG landmarks). By screening the resultant videos and their related videos (recommended by YouTube as a list on the right of the screen), a total of 62 videos were screened. Videos were excluded if they were irrelevant (e.g., focusing on radiographic errors without covering the anatomy), elaborating mainly with drawings without showing the landmarks on panoramic images, duplicate videos, and non-English speaking. Finally, 38 videos were included and analyzed. Most of them showed clear panoramic images and had clear tracing or delineation of the anatomical landmarks. On average, each video described 26 landmarks, including 12.3 from the midfacial region, 8.2 from the mandible, and 5.2 from soft tissue/air space/others. The videos were of good quality in general, with some frequent shortcomings being lack of visual aid with skull and schematic diagrams, and lack of discussion on clinical relevance. The maxillary sinus was the structure mostly involved in wrong information, particularly the wrong delineation of its posterior wall.

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“…3 However, interpreting the detected conditions and lesions may be challenging because of superimpositions caused by the complexity of the 3-dimensional anatomic structures on the panoramic image. 4 Computer-aided diagnosis systems have been developed to prevent clinicians from overlooking dental diseases and to increase the accuracy of radiological diagnoses. 5 In recent years, computer-aided detection or diagnosis systems created by conventional methods have been replaced by artificial intelligence (AI)-based systems aiming to enhance radiological diagnoses.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs

et al. 2022

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Purpose The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort.

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Depression of the maxillary sinus anterior wall and its influence on panoramic radiography appearance

Abstract: The diagonal line on a panoramic image was verified to be related to depression of the maxillary sinus anterior wall, and its panoramic image appearance can be altered depending on the position of the tomographic image layer.

Cited by 6 publications

References 13 publications

Nasal cavity perforation by implant fixtures: case series with emphasis on panoramic imaging of nasal cavity extending posteriorly

Nasal cavity perforation by implant fixtures: case series with emphasis on panoramic imaging of nasal cavity extending posteriorly

Content Analysis of YouTube Videos on Radiographic Anatomy on Dental Panoramic Images

A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs

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