Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study.

BACKGROUND: Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos. METHODS: A total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test. RESULTS: At the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880-0.958) for caries detection and 0.902 (95% CI, 0.853-0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection. CONCLUSIONS: The proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.

Effects of different methods of demineralized dentin matrix preservation on the proliferation and differentiation of human periodontal ligament stem cells.

Background/purpose: Demineralized dentin matrix (DDM) is used as a tissue regeneration scaffold. Effective preservation of DDM benefits clinical applications. Cryopreservation and freeze-drying may be effective methods to retain DDM mechanical properties and biological activity. Materials and methods: Human periodontal ligament stem cells (hPDLSCs) isolated using enzymatic dissociation were identified by multidirectional differentiation and flow cytometry. DDM was prepared with EDTA and divided into four groups: fresh DDM (fDDM), room temperature-preserved DDM (rtDDM), cryopreserved DDM (cDDM) and freeze-dried DDM (fdDDM). The DDM surface morphology was observed, and microhardness was detected. Transforming growth factor-ß1 (TGF-ß1), fibroblast growth factor (FGF) and collagen-Ⅰ (COL-Ⅰ) concentrations in DDM liquid extracts were detected by enzyme-linked immunosorbent assay (ELISA). The hPDLSCs were cultured with DDM liquid extracts. The effect of DDM on cells proliferation was examined by CCK-8 assay. The effect of DDM on hPDLSC secreted phosphoprotein-1 (SPP1), periostin (POSTN) and COL-Ⅰ gene expression was examined by real-time qPCR. Results: cDDM dentinal tubules were larger than those of the other groups. The three storage conditions had no significant effect on DDM microhardness and COL-Ⅰ concentration. However, TGF-ß1 and FGF concentrations decreased after storage, with the greatest change in rtDDM, followed by fdDDM and cDDM. The liquid extracts of fDDM, cDDM and fdDDM slightly inhibited hPDLSCs proliferation, but those of rtDDM had no significant effect. The hPDLSCs cultured with fDDM, cDDM and fdDDM liquid extracts showed increased SPP1, POSTN and COL-Ⅰ gene expression. Conclusion: Cryopreservation and freeze-drying better maintain the mechanical properties and biological activity of DDM.