PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10551728

Computer-aided periodontal disease diagnosis using computer vision.

M C Juan·M Alcañiz·C Monserrat·V Grau·C Knoll
View original source ↗Export metadata ↓

Abstract

Periodontal diseases are the major cause of tooth loss. The study of the evolution of these diseases is crucial to achieve adequate planning and treatment. Depth probing is essential to know the periodontal disease stage. In this paper we present a new system for Computer-Aided Periodontal Disease Diagnosis using computer vision. The system automates the depth probing and incorporates a colour camera fitted together with a plastic probe that automatically and exactly obtains the depth probing measure. The system has been tested by several periodontists and with 125 teeth of different patients. The differences between the values taken by the system and two periodontists have not been significant.

Explore related subjects

Absorptiometry, PhotonAlgorithmsAngiography, Digital SubtractionDiagnosis, Computer-AssistedDisease ProgressionEquipment DesignHumansImage Interpretation, Computer-AssistedPeriodontal DiseasesReproducibility of ResultsSoftwareJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M C Juan, M Alcañiz, C Monserrat, V Grau, C Knoll. Computer-aided periodontal disease diagnosis using computer vision.. https://doi.org/10.1016/s0895-6111(99)00012-9

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

A repeated measures concordance correlation coefficient.

The concordance correlation coefficient is commonly used to assess agreement between two raters or two methods of measuring a response when the data are measured on a continuous scale. However, the situation may arise in which repeated measurements are taken for each rater or method, e.g. longitudinal studies in clinical trials or bioassay data with subsamples. This paper proposes a coefficient for measuring agreement between two raters or two methods of measuring a response in the presence of repeated measurements. We illustrate the methodology with examples comparing (1) 1-hr versus 2-hr blood draws for measuring cortisol in an asthma clinical trial and (2) two measurements of percentage body fat, from skinfold calipers and dual energy X-ray absorptiometry.

Absorptiometry, Photon↗
PubMed2007

Crystallization and preliminary X-ray analysis of a domain in the Runx2 transcription factor that interacts with the 1alpha,25 dihydroxy vitamin D3 receptor.

The Runx2 transcription factor is a key regulator of osteoblast differentiation. In response to 1alpha,25 dihydroxy vitamin D3, Runx2 may interact with the 1alpha,25 dihydroxy vitamin D3 receptor (VDR) in the promoter of target genes, producing a synergic activation of their transcription. Previous studies have suggested that the motifs responsible for the VDR-Runx2 interaction are contained within the 230-361 domain of Runx2. In this work, we confirmed by GST-pull down that Runx2(I(209-361)) is sufficient to interact with the VDR. To obtain structural information, GST-Runx2(I(209-361)) protein was overexpressed in Escherichia coli, purified and crystallized using the hanging-drop vapor-diffusion method and polyethyleneglycol as a precipitant. The crystals were found to diffract to a maximum resolution of 2.7 A and a complete data set to a 3.3 A resolution was collected and analyzed. The crystals belong to the tetragonal system, with a space group P4 and unit-cell parameters of a = b = 90.8, and c = 57.2 A. The presence of a monomer of the recombinant GST-Runx2(I(209-361)) in the asymmetric unit gives a V(M) of 2.7 A(3) Da(-1) and a solvent content of 54.8%.

Absorptiometry, Photon↗
PubMed2007

Quantifying the treatment effect explained by markers in the presence of measurement error.

Surrogate markers or intermediate markers are important in identifying subjects with high risk of a serious disease or for monitoring disease progression of a subject on treatment. Quantifying the proportion of treatment effect (PTE) explained by markers has been studied extensively. Due to reasons such as biological variation, limited machine precision, etc. markers are generally measured with error. The estimated PTE ignoring the measurement error could be biased, which may lead to incorrect conclusions. In this article, we adjust for the measurement error using regression calibration to construct a less biased estimator of excess relative odds, a quantity to measure the treatment effect explained by markers. The method is applied to data from a clinical study in osteoporosis.

Absorptiometry, Photon↗