Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Effect of nano-hydroxyapatite incorporation on fluoride-releasing ability, penetration, and adaptation of a pit and fissure sealant(John Wiley and Sons Inc, 2022) Netalkar, P.P.; Maithreye, S.R.; Karuna, Y.M.; Srikant, S.; Gadipelly, T.; Bhat Panemangalore, B.P.; Dasgupta, A.; Lewis, A.Background: Dental caries is one of the most common multifactorial oral diseases and can be prevented using pit and fissure sealants. Aim: To evaluate the effect of nano-hydroxyapatite (nanoHAP) incorporation on fluoride-releasing ability, penetration, and adaptation of a pit and fissure sealant. Design: This was an in vitro study with two groups: conventional sealant and nanoHAP-incorporated sealant. Sealant penetration and adaptation were assessed using stereomicroscope and scanning electron microscope (SEM) (15 and 10 samples per group, respectively). Fluoride release was analyzed using ion-selective electrode (15 samples per group). The chi-square test was used to compare penetration and adaptation between the 2 groups, and an independent Student t test was used to compare fluoride release. Results: The nanoHAP group showed significantly more samples with no bubbles (P =.001) and no debris (P <.001). SEM analysis showed a significantly greater percentage of adequate fissures in the test group (P =.007). The fluoride release was significantly higher in test samples with p values of.001 and.016 on day 1 and day 60, respectively. Conclusion: The incorporation of nanoHAP into the conventional pit and fissure sealant improved its penetration and adaptation properties along with fluoride release. © 2021 BSPD, IAPD and John Wiley & Sons Ltd.Item LCSNet: Lightweight Caries Segmentation Network for the segmentation of dental caries using smartphone photographs(Elsevier B.V., 2025) Radha, R.C.; Raghavendra, B.S.; Hota, R.K.; Vijayalakshmi, K.R.; Patil, S.; Narasimhadhan, A.V.Dental caries is one of the major dental issues that is common among many individuals. It leads to tooth loss and affects the tooth root, creating a need to automatically detect dental caries to reduce treatment costs and prevent its consequences. The Lightweight Caries Segmentation Network (LCSNet) proposed in this study detects the location of dental caries by applying pixel-wise segmentation to dental photographs taken with various Android phones. LCSNet utilizes a Dual Multiscale Residual (DMR) block in both the encoder and decoder, adapts transfer learning through a pre-trained InceptionV3 model at the bottleneck layer, and incorporates a Squeeze and Excitation block in the skip connection, effectively extracting spatial information even from images where 95 % of the background and only 5 % represent the area of interest. A new dataset was developed by gathering oral photographs of dental caries from two hospitals, with advanced augmentation techniques applied. The LCSNet architecture demonstrated an accuracy of 97.36 %, precision of 73.1 %, recall of 70.2 %, an F1-Score of 71.14 %, and an Intersection-over-Union (IoU) of 56.8 %. Expert dentists confirmed that the LCSNet model proposed in this in vivo study accurately segments the position and texture of dental caries. Both qualitative and quantitative performance analyses, along with comparative analyses of efficiency and computational requirements, were conducted with other deep learning models. The proposed model outperforms existing deep learning models and shows significant potential for integration into a smartphone application-based oral disease detection system, potentially replacing some conventional clinically adapted methods. © 2025 The Authors.