Abstract
PurposePeriodontitis is the sixth most prevalent disease worldwide and periodontal bone loss (PBL) detection is crucial for its early recognition and establishment of the correct diagnosis and prognosis. Current radiographic assessment by clinicians exhibits substantial interobserver variation. Computer-assisted radiographic assessment can calculate bone loss objectively and aid in early bone loss detection. Understanding the rate of disease progression can guide the choice of treatment and lead to early initiation of periodontal therapy.MethodologyWe propose an end-to-end system that includes a deep neural network with hourglass architecture to predict dental landmarks in single, double and triple rooted teeth using periapical radiographs. We then estimate the PBL and disease severity stage using the predicted landmarks. We also introduce a novel adaptation of MixUp data augmentation that improves the landmark localisation.ResultsWe evaluate the proposed system using cross-validation on 340 radiographs from 63 patient cases containing 463, 115 and 56 single, double and triple rooted teeth. The landmark localisation achieved Percentage Correct Keypoints (PCK) of 88.9%, 73.9% and 74.4%, respectively, and a combined PCK of 83.3% across all root morphologies, outperforming the next best architecture by 1.7%. When compared to clinicians’ visual evaluations of full radiographs, the average PBL error was 10.69%, with a severity stage accuracy of 58%. This simulates current interobserver variation, implying that diverse data could improve accuracy.ConclusionsThe system showed a promising capability to localise landmarks and estimate periodontal bone loss on periapical radiographs. An agreement was found with other literature that non-CEJ (Cemento-Enamel Junction) landmarks are the hardest to localise. Honing the system’s clinical pipeline will allow for its use in intervention applications.
Highlights
Periodontitis remains a major public health problem with a high cost to society [26], affecting 45% of UKSciences (WEISS) and Department of Computer Science, University College London, London, UK 2 Unit of Periodontology, University College London Eastman Dental Institute, London, UK adults1,with 11.2% of the world population experiencing severe periodontitis conditions [13]
Both panoramic and periapical radiographs have been utilised for automatic periodontal bone loss (PBL) detection and disease progression analysis [4,14,16]
The adjusted symmetric hourglass with proposed Interstitial Spatial MixUp (ISM) model additions is compared with a baseline ResNet-based regression model without the proposed ISM model additions, a symmetric hourglass without additions, a network with an asymmetric hourglass architecture from [24] with and without model additions and a stacked hourglass network, adapted from [21,24], with model additions
Summary
We evaluate the proposed system using cross-validation on 340 radiographs from 63 patient cases containing 463, 115 and 56 single, double and triple rooted teeth. The landmark localisation achieved Percentage Correct Keypoints (PCK) of 88.9%, 73.9% and 74.4%, respectively, and a combined PCK of 83.3% across all root morphologies, outperforming the best architecture by 1.7%. When compared to clinicians’ visual evaluations of full radiographs, the average PBL error was 10.69%, with a severity stage accuracy of 58%. This simulates current interobserver variation, implying that diverse data could improve accuracy
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