Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images

Almalki, Yassir Edrees; Din, Amsa Imam; Ramzan, Muhammad; Irfan, Muhammad; Aamir, Khalid Mahmood; Almalki, Abdullah; Alotaibi, Saud S.; Alaglan, Ghada; Alshamrani, Hassan A.; Rahman, Saifur

doi:10.3390/s22197370

Cited by 22 publications

(12 citation statements)

References 33 publications

(33 reference statements)

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“…14 Our study shows that the majority of participants were fully aware that Orthopantomogram (OPG) is used for the diagnosis of full-mouth dental problems and pathologies of mixed dentition. This follows a previous study conducted by Almalki YE at el., 15 and Jadhav P 16 . The question regarding radiograph used for diagnosing sinus pathologies was rightly answered by most of the participants as Paranasal Sinus view (PNS).…”

Section: Discussionmentioning

confidence: 99%

Awareness and knowledge regarding dental radiology: A cross-sectional study

Akbar,

Butt,

Waris

et al. 2024

J REHMAN MED INST

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Introduction: Dental radiographs are an important tool for the diagnostic workup of the patient. Both basic and specialized radiographs are used in dentistry and knowledge regarding its indications and techniques is required both for undergraduate and postgraduate students, general dentists, and consultants. Objective: Our study aimed to explore the knowledge and awareness of dental radiographs among dentists and dental students. Materials and method: The cross-sectional descriptive study was conducted at Rehman College of Dentistry, Peshawar Pakistan from October 2022 to March 2023. The questionnaire was prepared after a literature review, validation by experts, and pilot testing. Reliability was observed to be more than 0.9 and the questionnaire was disseminated through online Google forms using convenience sampling. SPSS Version 22.0 was used for data analysis. Results: 154 Participants were included in the study. Out of which 58 percent were females and 42 percent were males. Generally, regarding most of the questions, specialists had the highest knowledge, while postgraduate and general dentists had a neutral ratio, and undergraduates were the least aware. Conclusion: Despite the utmost significance of dental radiology in the diagnosis and treatment planning of the patient, it is not given its due importance. Generally, the dental fraternity lags in knowledge and awareness about the field, especially at the undergraduate level.

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Section: Discussionmentioning

confidence: 99%

Awareness and knowledge regarding dental radiology: A cross-sectional study

Akbar,

Butt,

Waris

et al. 2024

J REHMAN MED INST

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“…Furthermore, in 2022, panoramic X-rays (OPGs) obtained from clinics were incorporated into the study in [24]. A DSLR was used for some of the OPGs, while clinics supplied digital copies for others.…”

Section: Datasets Used For Ai-based Systems For Dentistry E-healthmentioning

confidence: 99%

“…A leading model for object detection and classification tasks. Implemented with the darknet framework, it features 53 layers, including residual blocks from Darknet-53, which enhances its ability to capture intricate image features effectively [24]. Figure 6 shows the YOLOv3 network architecture.…”

Section: Yolov3mentioning

confidence: 99%

Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry

Musleh,

Almossaeed,

Balhareth

et al. 2024

BDCC

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The rise of artificial intelligence has created and facilitated numerous everyday tasks in a variety of industries, including dentistry. Dentists have utilized X-rays for diagnosing patients’ ailments for many years. However, the procedure is typically performed manually, which can be challenging and time-consuming for non-specialized specialists and carries a significant risk of error. As a result, researchers have turned to machine and deep learning modeling approaches to precisely identify dental disorders using X-ray pictures. This review is motivated by the need to address these challenges and to explore the potential of AI to enhance diagnostic accuracy, efficiency, and reliability in dental practice. Although artificial intelligence is frequently employed in dentistry, the approaches’ outcomes are still influenced by aspects such as dataset availability and quantity, chapter balance, and data interpretation capability. Consequently, it is critical to work with the research community to address these issues in order to identify the most effective approaches for use in ongoing investigations. This article, which is based on a literature review, provides a concise summary of the diagnosis process using X-ray imaging systems, offers a thorough understanding of the difficulties that dental researchers face, and presents an amalgamative evaluation of the performances and methodologies assessed using publicly available benchmarks.

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“…Even though the ensemble model is made up of several base models, it works like a single model. [32]…”

Section: Ensemble Modelingmentioning

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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Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images

Cited by 22 publications

References 33 publications

Awareness and knowledge regarding dental radiology: A cross-sectional study

Awareness and knowledge regarding dental radiology: A cross-sectional study

Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry

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

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