Tooth detection for each tooth type by application of faster R-CNNs to divided analysis areas of dental panoramic X-ray images

Mima, Yuichi; Nakayama, Ryohei; Hizukuri, Akiyoshi; Murata, Kan

doi:10.1007/s12194-022-00659-1

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Vertical root fracture [72,73] Deep learning Periapical pathosis [21], dental tumors [74], tooth numbering [75][76][77][78], tooth detection and identification [79][80][81], periodontal bone loss [32,82,83] Disease classification Classical image analysis approaches Tooth detection [84,85], osteoporosis assessment [86], dental caries [87] Machine learning Dental caries [88], proximal dental caries [14], molar and pre-molar teeth [89], osteoporosis [90], dental caries [15], periapical lesions [16,17], dental restorations [22], periapical roots [91], teeth with root [92], sagittal patterns [93] Deep learning Tooth numbering [94][95][96][97][98][99], dental implant stages [100], implant fixture [101], bone loss [18], periapical periodontitis [102][103][104][105], dental decay [106], approximal dental caries [19] Disease segmentation C...…”

Section: Disease Detection Machine Learningmentioning

confidence: 99%

“…For automatic tooth region detection, Mima et al investigated Faster R-CNN, using four cross-validations on panoramic X-ray images. The model could classify 32 tooth types with an accuracy of 91.7% and a mean IoU of 0.748 [ 80 ]. Another study explored a single shot multibox detector (SSD) network with a side branch; the model achieved a detection rate of 99.03% and a classification rate of 96.79% on panoramic X-ray images [ 81 ].…”

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

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A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

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Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.

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Section: Disease Detection Machine Learningmentioning

confidence: 99%

Section: Approaches To Dental Disease Diagnosis Using X-ray Imagingmentioning

confidence: 99%

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

et al. 2023

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“…A DL algorithm for automatically detecting teeth in panoramic radiography is considered a breakthrough in dental practice [19] , [20] , [21] . Each detected tooth is classified according to its pathological and treatment status, such as presence of tooth decay, eruption disorder, restorative or prosthetic or endodontic treatments, and dental implants.…”

Section: The Region (Object) Detection Taskmentioning

confidence: 99%

Deep learning and artificial intelligence in dental diagnostic imaging

Katsumata

2023

Japanese Dental Science Review

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“…Application of AI in dentistry has been investigated as early as 1992. [ 3 ] The use of AI systems in dental care includes image enhancement of radiographs,[ 4 5 6 ] disease detection like Vertical root fracture,[ 7 ] dental tumors,[ 8 ] tooth numbering and identification,[ 9 10 ] periodontal bone loss,[ 11 ] osteoporosis assessment,[ 12 ] dental caries,[ 13 ] periapical lesions,[ 14 ] dental restorations,[ 15 ] dental implant stages,[ 16 ] localization of cephalometric landmarks, classification of skeletal malocclusion, planning orthognathic surgery, temporomandibular joint disorders, detection of mandibular fracture. [ 17 ] The extent to which AI solutions are adapted in dentistry will mainly depend upon the attitudes of clinicians and patients.…”

Section: Introductionmentioning

confidence: 99%

Perceptiveness and Attitude on the use of Artificial Intelligence (AI) in Dentistry among Dentists and Non-Dentists - A Regional Survey

Pringle,

Kumaran,

Missier

et al. 2024

Journal of Pharmacy and Bioallied Sciences

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Artificial intelligence (AI) is an emerging tool in modern medicine and the digital world. AI can help dentists diagnose oral diseases, design treatment plans, monitor patient progress and automate administrative tasks. The aim of this study is to evaluate the perception and attitude on use of artificial intelligence in dentistry for diagnosis and treatment planning among dentists and non-dentists’ population of south Tamil Nadu region in India. Materials and Methods: A cross sectional online survey conducted using 20 close ended questionnaire google forms which were circulated among the dentists and non -dentists population of south Tamil Nadu region in India. The data collected from 264 participants (dentists -158, non-dentists -106) within a limited time frame were subjected to descriptive statistical analysis. Results: 70.9% of dentists are aware of artificial intelligence in dentistry. 40.5% participants were not aware of AI in caries detection but aware of its use in interpretation of radiographs (43.9%) and in planning of orthognathic surgery (42.4%) which are statistically significant P < 0.05.44.7% support clinical experience of a human doctor better than AI diagnosis. Dentists of 54.4% agree to support AI use in dentistry. Conclusion: The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non -dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning.

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Tooth detection for each tooth type by application of faster R-CNNs to divided analysis areas of dental panoramic X-ray images

Cited by 9 publications

References 16 publications

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health

Deep learning and artificial intelligence in dental diagnostic imaging

Perceptiveness and Attitude on the use of Artificial Intelligence (AI) in Dentistry among Dentists and Non-Dentists - A Regional Survey

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