Dental calculus-a reservoir for detection of past SARS-CoV-2 infection.
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ABSTRACT:
SUBMITTER: Berton F
PROVIDER: S-EPMC8152705 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
Dental calculus-a reservoir for detection of past SARS-CoV-2 infection.
Berton Federico F Rupel Katia K Florian Fiorella F Biasotto Matteo M Pallavicini Alberto A Di Lenarda Roberto R
Clinical oral investigations 20210526 8
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Project description:The most grievous threat to human health has been witnessed worldwide with the recent outbreak of Corona virus disease 2019 (COVID-19). There is mounting evidence available regarding theconnect of COVID -19 and oral cavity, particularly periodontal disease. The current review provides an update on the diagnostic potential of dental calculus and how this bio resource may help in providing us huge amount of diagnostic regarding the causative virus. Contemporary standard method of diagnosis via nasopharyngeal swabs (NPS) is tedious, may enhance the risk of aerosol contamination by inducing sneezing and detects the presence of active infection only.However,dental calculus being a mineralized deposit serves as a reservoir for biomoleculesand provides detection of past SARS CoV2 infection. Further, the abundance of information that can be obtained from this remarkable mineralized deposit on teeth regarding the viral genome, its evolution and interactions with the oral microflora shall enhance the understanding of the viral disease process and its connection with the periodontal disease. Additional diagnostic information, which may be obtained from this simple bio reservoir can complement the contemporary diagnostic strategies adopted in the management of COVID-19pandemic and enhance our existing knowledge for developing improvised novel approaches to mitigate the effects of mutated variants of the infectious agent.
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Project description:Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently (e.g., too large) with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing radiographs. On 2584 bitewings calculus was accurately labeled using bounding boxes (BBs) and artificially increased and decreased stepwise, resulting in 30 consistently and 9 inconsistently noisy datasets. An object detection network (YOLOv5) was trained on each dataset and evaluated on noisy and accurate test data. Training on accurately labeled data yielded an mAP50: 0.77 (SD: 0.01). When trained on consistently too small BBs model performance significantly decreased on accurate and noisy test data. Model performance trained on consistently too large BBs decreased immediately on accurate test data (e.g., 200% BBs: mAP50: 0.24; SD: 0.05; p < 0.05), but only after drastically increasing BBs on noisy test data (e.g., 70,000%: mAP50: 0.75; SD: 0.01; p < 0.05). Models trained on inconsistent BB sizes showed a significant decrease of performance when deviating 20% or more from the original when tested on noisy data (mAP50: 0.74; SD: 0.02; p < 0.05), or 30% or more when tested on accurate data (mAP50: 0.76; SD: 0.01; p < 0.05). In conclusion, accurate predictions need accurate labeled data in the training process. Testing on noisy data may disguise the effects of noisy training data. Researchers should be aware of the relevance of accurately annotated data, especially when testing model performances.
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