biostudies-literatureUnknown51(3)Pan YC<h4>Objectives</h4>Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sonogram, and automate soft tissue height (STH) measurement using deep learning.<h4>Methods</h4>In this retrospective study, 627 frames from 111 independent cine loops of mandibular and maxillary premolar and incisors collected from our porcine model (<i>N</i> = 8) were labeled by a reader. 274 premolar sonograms, including data augmentation, were used to train a multi class segmentation model. The model was evaluated against several test sets, including premolar of the same breed (<i>n</i> = 74, Yucatan) and premolar of a different breed (<i>n</i> = 120, Sinclair). We further proposed a rule-based algorithm to automate STH measurements using predicted segmentation masks.<h4>Results</h4>The model reached a Dice similarity coefficient of 90.7±4.39%, 89.4±4.63%, and 83.7±10.5% for soft tissue, bone, and crown segmentation, respectively on the first test set (<i>n</i> = 74), and 90.0±7.16%, 78.6±13.2%, and 62.6±17.7% on the second test set (<i>n</i> = 120). The automated STH measurements have a mean difference (95% confidence interval) of -0.22 mm (-1.4, 0.95), a limit of agreement of 1.2 mm, and a minimum ICC of 0.915 (0.857, 0.948) when compared to expert annotation.<h4>Conclusion</h4>This work demonstrates the potential use of deep learning in identifying periodontal structures on sonograms and obtaining diagnostic periodontal dimensions.Dento maxillo facial radiology20210363https://www.ebi.ac.uk/biostudies/studies/S-EPMC8925874biostudies-literatureMulti-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model.PMC8925874Pan YCChan HLHadjiiski LMKripfgans ODKong XfalseMulti-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model.<h4>Objectives</h4>Ultrasound emerges as a complement to cone-beam computed tomography in dentistry, but struggles with artifacts like reverberation and shadowing. This study seeks to help novice users recognize soft tissue, bone, and crown of a dental sonogram, and automate soft tissue height (STH) measurement using deep learning.<h4>Methods</h4>In this retrospective study, 627 frames from 111 independent cine loops of mandibular and maxillary premolar and incisors collected from our porcine model (<i>N</i> = 8) were labeled by a reader. 274 premolar sonograms, including data augmentation, were used to train a multi class segmentation model. The model was evaluated against several test sets, including premolar of the same breed (<i>n</i> = 74, Yucatan) and premolar of a different breed (<i>n</i> = 120, Sinclair). We further proposed a rule-based algorithm to automate STH measurements using predicted segmentation masks.<h4>Results</h4>The model reached a Dice similarity coefficient of 90.7±4.39%, 89.4±4.63%, and 83.7±10.5% for soft tissue, bone, and crown segmentation, respectively on the first test set (<i>n</i> = 74), and 90.0±7.16%, 78.6±13.2%, and 62.6±17.7% on the second test set (<i>n</i> = 120). The automated STH measurements have a mean difference (95% confidence interval) of -0.22 mm (-1.4, 0.95), a limit of agreement of 1.2 mm, and a minimum ICC of 0.915 (0.857, 0.948) when compared to expert annotation.<h4>Conclusion</h4>This work demonstrates the potential use of deep learning in identifying periodontal structures on sonograms and obtaining diagnostic periodontal dimensions.2022-01-01T00:00:00Z2022 Mar2025-04-04T21:43:45.93Z2025-04-04T21:43:45.93ZS-EPMC89258743476251210.1259/dmfr.20210363