BACKGROUND: The focal infection theory has been used to explain several chronic systemic diseases in the past. Systemic diseases were thought to be caused by focal infections, such as caries and periodontal diseases, and dentists were held responsible for these diseases due to the spread of oral infections. As knowledge of the interrelationship between oral microorganisms and the host immune response has evolved over the last few decades, the focal infection theory has been modified in various ways. The relationship between oral and systemic health appears to be more complex than that suggested by the classical theory of focal infections. Indeed, the contribution of the oral microbiota to some systemic diseases is gaining acceptance, as there are strong associations between periodontal disease and atherosclerotic vascular disease, diabetes, and hospital-associated pneumonia, amongst others. As many jurisdictions have various protocols for managing this oral-systemic axis of disease, we sought to provide a consensus on this notion with the help of a multidisciplinary team from the Czech Republic. METHODS: A multidisciplinary team comprising physicians/surgeons in the specialities of dentistry, ear-nose and throat (ENT), cardiology, orthopaedics, oncology, and diabetology were quetioned with regard to their conceptual understanding of the focal infection theory particularly in relation to the oral-systemic axis. The team also established a protocol to determine the strength of these associations and to plan the therapeutic steps needed to treat focal odontogenic infections whenever possible. RESULTS: Scoring algorithms were devised for odontogenic inflammatory diseases and systemic risks, and standardised procedures were developed for general use. CONCLUSIONS: The designed algorithm of the oral-systemic axis will be helpful for all health care workers in guiding their patient management protocol.
- MeSH
- fokální infekce zubní * komplikace terapie MeSH
- konsensus MeSH
- lidé MeSH
- nemoci parodontu terapie MeSH
- týmová péče o pacienty MeSH
- zubní kaz terapie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- konsensus - konference MeSH
- Geografické názvy
- Česká republika MeSH
- MeSH
- kaz zubního kořene * prevence a kontrola terapie MeSH
- lidé středního věku MeSH
- lidé MeSH
- orální hygiena metody MeSH
- primární prevence metody MeSH
- rizikové faktory MeSH
- senioři MeSH
- stárnutí MeSH
- stravovací zvyklosti MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
OBJECTIVE: The objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth. MATERIALS AND METHODS: Four experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method. RESULTS: The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors. CONCLUSIONS: The automatic method consistently outperformed novices and performed as well as highly experienced dentists. CLINICAL SIGNIFICANCE: The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.
- MeSH
- interproximální technika MeSH
- lidé MeSH
- náchylnost k zubnímu kazu * MeSH
- zubní kaz * diagnostické zobrazování MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVES: Dental caries is a widespread multifactorial disease, caused by the demineralization of hard dental tissues. Susceptibility to dental caries is partially genetically conditioned; this study was aimed at finding an association of selected single nucleotide polymorphisms (SNPs) in genes encoding proteins involved in amelogenesis with this disease in children. MATERIALS AND METHODS: In this case-control study, 15 SNPs in ALOX15, AMBN, AMELX, KLK4, TFIP11, and TUFT1 genes were analyzed in 150 children with primary dentition and 611 children with permanent teeth with/without dental caries from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) cohort. RESULTS: Dental caries in primary dentition was associated with SNPs in AMELX (rs17878486) and KLK4 (rs198968, rs2242670), and dental caries in permanent dentition with SNPs in AMELX (rs17878486) and KLK4 (rs2235091, rs2242670, rs2978642), (p ≤ 0.05). No significant differences between cases and controls were observed in the allele or genotype frequencies of any of the selected SNPs in ALOX15, AMBN, TFIP11, and TUFT1 genes (p > 0.05). Some KLK4 haplotypes were associated with dental caries in permanent dentition (p ≤ 0.05). CONCLUSIONS: Based on this study, we found that although the SNPs in AMELX and KLK4 are localized in intronic regions and their functional significance has not yet been determined, they are associated with susceptibility to dental caries in children. CLINICAL RELEVANCE: AMELX and KLK4 variants could be considered in the risk assessment of dental caries, especially in permanent dentition, in the European Caucasian population.
- MeSH
- amelogeneze * genetika MeSH
- amelogenin genetika MeSH
- dítě MeSH
- lidé MeSH
- longitudinální studie MeSH
- studie případů a kontrol MeSH
- zubní kaz * genetika epidemiologie MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVE: The aim of this work was to assemble a large annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance. MATERIALS AND METHODS: A dataset of 3989 bitewing radiographs was created, and 7257 carious lesions were annotated using minimal bounding boxes. The dataset was then divided into 3 parts for the training (70%), validation (15%), and testing (15%) of multiple object detection convolutional neural networks (CNN). The tested CNN architectures included YOLOv5, Faster R-CNN, RetinaNet, and EfficientDet. To further improve the detection performance, model ensembling was used, and nested predictions were removed during post-processing. The models were compared in terms of the [Formula: see text] score and average precision (AP) with various thresholds of the intersection over union (IoU). RESULTS: The twelve tested architectures had [Formula: see text] scores of 0.72-0.76. Their performance was improved by ensembling which increased the [Formula: see text] score to 0.79-0.80. The best-performing ensemble detected caries with the precision of 0.83, recall of 0.77, [Formula: see text], and AP of 0.86 at IoU=0.5. Small carious lesions were predicted with slightly lower accuracy (AP 0.82) than medium or large lesions (AP 0.88). CONCLUSIONS: The trained ensemble of object detection CNNs detected caries with satisfactory accuracy and performed at least as well as experienced dentists (see companion paper, Part II). The performance on small lesions was likely limited by inconsistencies in the training dataset. CLINICAL SIGNIFICANCE: Caries can be automatically detected using convolutional neural networks. However, detecting incipient carious lesions remains challenging.
- MeSH
- deep learning * MeSH
- lidé MeSH
- náchylnost k zubnímu kazu MeSH
- neuronové sítě (počítačové) MeSH
- zubní kaz * diagnostické zobrazování MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Klíčová slova
- pulpální diagnostika,
- MeSH
- biologické markery MeSH
- diagnóza stomatologická metody MeSH
- dospělí MeSH
- lidé MeSH
- odontoblasty imunologie patologie MeSH
- pulpotomie metody MeSH
- zubní dřeň patologie MeSH
- zubní kaz * diagnóza imunologie prevence a kontrola MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- kazuistiky MeSH
- MeSH
- antiinfekční látky lokální farmakologie klasifikace terapeutické užití MeSH
- biofilmy MeSH
- chlorhexidin farmakologie terapeutické užití MeSH
- ionty farmakologie terapeutické užití MeSH
- lidé MeSH
- xylitol farmakologie metabolismus terapeutické užití MeSH
- zubní kaz * etiologie patofyziologie prevence a kontrola MeSH
- zubní plak * patofyziologie prevence a kontrola terapie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
- Klíčová slova
- matricová technika,
- MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- rentgendiagnostika zubní MeSH
- záchovná stomatologie * metody přístrojové vybavení MeSH
- zubní cementy terapeutické užití MeSH
- zubní kaz terapie MeSH
- zuby diagnostické zobrazování patologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- Publikační typ
- kazuistiky MeSH
x
x
- MeSH
- infekce močového ústrojí komplikace MeSH
- infekce spojené s protézou * prevence a kontrola MeSH
- komplikace diabetu MeSH
- kouření MeSH
- lidé MeSH
- náhrada kyčelního kloubu metody normy MeSH
- obezita komplikace MeSH
- podvýživa komplikace MeSH
- revmatoidní artritida farmakoterapie komplikace MeSH
- rizikové faktory * MeSH
- zubní kaz komplikace MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
