Detection and classification of 32 tooth types in dental panoramic radiographs using single CNN model and post-processing

Morishita, Takumi; Muramatsu, Chisako; Seino, Yuta; Takahashi, Ryo; Hayashi, Tetsutaro; Nishiyama, Wataru; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

doi:10.1117/12.2625961

Cited by 1 publication

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cases with supernumerary teeth and deciduous teeth were not included in this study. Fifty-two images that were found to be duplicate in our previous study [6] were excluded. Experienced dentists provided approximate outlines of tooth crowns and roots and dental prostheses and their tooth numbers and conditions as shown in Fig.…”

Section: Databasementioning

confidence: 99%

“…To solve these problems, we developed a model using images obtained from 10 different facilities including a university hospital and dental clinics with various image sizes and characteristics. In our previous studies, we proposed methods to recognize 32 tooth types [5] and 4 attributes, including tooth, root stump, implant, and pontic [6]. However, for more complete files, recognition of restoration states is desirable.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Recognition of tooth numbers and conditions on dental panoramic radiographs for assisting dental chart filing

Muramatsu¹,

Sakai²,

Seino³

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

View full text Add to dashboard Cite

Dental panoramic radiographs are often obtained at dental clinic visits for diagnosis and recording purposes. Automated filing of dental charts can help dentists in reducing their workload and improving diagnostic efficiency. The purpose of this study is to develop a system that prerecords a dental chart by recognizing teeth with their numbers and restoration history on dental panoramic radiographs. The proposed system uses YOLO which detects 16 types of teeth and restoration conditions simultaneously. Based on the detected tooth types, they were further classified into 32 types and combined with the tooth conditions by post-processing. We tested our method on 870 panoramic images obtained at 10 different facilities by 5-fold cross validation. The proposed method obtained 0.99 recall and precision for recognition of 32 tooth types and 0.90 recall and 0.90 precision on determining the tooth condition. It has the potential to assist prefiling the dental charts for efficient dental care.

show abstract

Section: Databasementioning

confidence: 99%

mentioning

confidence: 99%