A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs

Bayrakdar, İ̇brahim Şevki; Orhan, Kaan; Çelik, Özer; Bilgir, Elif; Sağlam, Hande; Kaplan, Fatma Akkoca; Görür, Sinem Atay; Odabaş, Alper; Aslan, Ahmet Faruk; Różyło‐Kalinowska, Ingrid

doi:10.1155/2022/7035367

Cited by 34 publications

(37 citation statements)

References 38 publications

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“…In the field of endodontics, the potential of AI to detect periapical lesions has been tested through various imaging techniques. This includes cone-beam computed tomography (CBCT) scans [36][37][38][39], panoramic images [40][41][42][43][44], as well as periapical X-rays (Table 1). The current standards for diagnosing periapical lesions involve a clinical evaluation alongside taking a periapical radiograph, which dentists and specialists routinely perform.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review

et al. 2023

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This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting apical pathosis on periapical radiographs. A total of twenty anonymized periapical radiographs were retrieved from the database of Poznan University of Medical Sciences. These radiographs displayed a sequence of 60 visible teeth. The evaluation of the radiographs was conducted using two methods (manual and automatic), and the results obtained from each technique were afterward compared. For the ground-truth method, one oral and maxillofacial radiology expert with more than ten years of experience and one trainee in oral and maxillofacial radiology evaluated the radiographs by classifying teeth as healthy and unhealthy. A tooth was considered unhealthy when periapical periodontitis related to this tooth had been detected on the radiograph. At the same time, a tooth was classified as healthy when no periapical radiolucency was detected on the periapical radiographs. Then, the same radiographs were evaluated by artificial intelligence, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA). Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) correctly identified periapical lesions on periapical radiographs with a sensitivity of 92.30% and identified healthy teeth with a specificity of 97.87%. The recorded accuracy and F1 score were 96.66% and 0.92, respectively. The artificial intelligence algorithm misdiagnosed one unhealthy tooth (false negative) and over-diagnosed one healthy tooth (false positive) compared to the ground-truth results. Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) showed an optimum accuracy for detecting periapical periodontitis on periapical radiographs. However, more research is needed to assess the diagnostic accuracy of artificial intelligence-based algorithms in dentistry.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review

et al. 2023

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“…U-Net architecture was used to reveal this. U-Net is a convolutional neural network developed at the Freiburg University Computer Science Department for segmentation in image processing studies in biomedical fields [ 10 , 11 , 12 , 17 , 20 , 21 , 26 , 30 , 31 , 34 , 38 , 50 , 64 , 66 , 67 , 70 , 71 , 82 , 83 , 84 , 85 , 86 ].…”

Section: Discussionmentioning

confidence: 99%

“…Common applications of AI in oral diagnosis and dentomaxillofacial radiology are as follows: Oral cancer prognosis and assessment of oral cancer risk [ 45 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]; Determination of temporomandibular joint disorder progression and temporomandibular internal derangements [ 27 , 30 , 34 , 38 , 63 ]; Interpretation of conventional 2D imaging [ 31 , 64 , 65 , 66 , 67 , 68 ]; Interpretation of cone beam computed tomography and other 3D imaging methods [ 1 , 10 , 12 , 17 , 18 , 19 , 21 , 23 , 27 , 69 , 70 , 71 ]. …”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Teeth, Crown–Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

et al. 2023

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Background: The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs). Methods: 8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool’s database. All teeth, crown–bridge restorations, dental implants, composite–amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique. Results: The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown–bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively. Conclusions: Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.

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“…In this biomedical study [19], the U-Net approach to apical lesion segmentation on panoramic radiographs is discussed. The objective of this study was to extract apical lesions from 470 dental panoramic radiographs.…”

Section: Related Workmentioning

confidence: 99%

Multi Oral Disease Classification from Panoramic Radiograph using Transfer Learning and XGBoost

Jaiswal¹,

Katkar²,

Bhirud³

2022

IJACSA

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The subject of oral healthcare is a crucial research field with significant technological development. This research examines the field of oral health care known as dentistry, a branch of medicine concerned with the anatomy, development, and disorders of the teeth. Good oral health is essential for speaking, smiling, testing, touching, digesting food, swallowing, and many other aspects, such as expressing a variety of emotions through facial expressions. Comfort in doing all these activities contributes to a person's self-confidence. For diagnosing multiple oral diseases at a time panoramic radiograph is used. Oral healthcare experts are important to appropriately detect and classify disorders. This automated approach was developed to eliminate the overhead of experts and the time required for diagnosis. This research is based on a self-created dataset of 500 images representing six distinct diseases in 46 possible combinations. Tooth wear, periapical, periodontitis, tooth decay, missing tooth, and impacted tooth are all examples of diseases. This system is developed using the concept of transfer learning with the use of a different pre-trained network such as "ResNet50V2", "ResNet101V2", "MobileNetV3Large", "MobileNetV3Small", "MobileNet", "EfficientNetB0", "EfficientNetB1", and "EfficientNetB2" with XGBoost and to get the final prediction The images in the dataset were divided into 80% training and 20% images for testing. To assess the performance of this system, various measuring metrics are used. Experiments revealed that the proposed model detected Tooth wear, periapical, periodontitis, tooth decay, missing tooth, and impacted tooth with an accuracy of 91.8%, 92.2%, 92.4%, 93.2%, 91.6%, and 90.8%, respectively.

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A U-Net Approach to Apical Lesion Segmentation on Panoramic Radiographs

Cited by 34 publications

References 38 publications

Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review

Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review

Automatic Segmentation of Teeth, Crown–Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls

Multi Oral Disease Classification from Panoramic Radiograph using Transfer Learning and XGBoost

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