The precision of gingival recession measurements is increased by an automated curvature analysis method

Background The extent of gingival recession represents one of the most important measures determining outcome of periodontal plastic surgery. The accurate measurements are, thus, critical for optimal treatment planning and outcome evaluation. Present study aimed to introduce automated curvature-based digital gingival recession measurements, evaluate the agreement and reliability of manual measurements, and identify sources of manual variability. Methods Measurement of gingival recessions was performed manually by three examiners and automatically using curvature analysis on representative cross-sections (n = 60). Cemento-enamel junction (CEJ) and gingival margin (GM) measurement points selection was the only variable. Agreement and reliability of measurements were analysed using intra- and inter-examiner correlations and Bland–Altman plots. Measurement point selection variability was evaluated with manual point distance deviation from an automatic point. The effect of curvature on manual point selection was evaluated with scatter plots. Results Bland–Altman plots revealed a high variability of examiner’s recession measurements indicated by high 95% limits of agreement range of approximately 1 mm and several outliers beyond the limits of agreement. CEJ point selection was the main source of examiner’s variability due to smaller curvature values than GM, i.e., median values of − 0.98 mm− 1 and − 4.39 mm− 1, respectively, indicating straighter profile for CEJ point. Scatter plots revealed inverse relationship between curvature and examiner deviation for CEJ point, indicating a threshold curvature value around 1 mm− 1. Conclusions Automated curvature-based approach increases the precision of recession measurements by reproducible measurement point selection. Proposed approach allows evaluation of teeth with indistinguishable CEJ that could be not be included in the previous studies.

The precision of gingival recession measurements is increased by an automated curvature analysis method

Background The extent of gingival recession represents one of the most important measures determining outcome of periodontal plastic surgery. The accurate measurements are, thus, critical for optimal treatment planning and outcome evaluation. Present study aimed to introduce automated curvature-based digital gingival recession measurements, evaluate the agreement and reliability of manual measurements, and identify sources of manual variability. Methods Measurement of gingival recessions was performed manually by three examiners and automatically using curvature analysis on representative cross-sections (n = 60). Cemento-enamel junction (CEJ) and gingival margin (GM) measurement points selection was the only variable. Agreement and reliability of measurements were analysed using intra- and inter-examiner correlations and Bland-Altman plots. Measurement point selection variability was evaluated with manual point distance deviation from an automatic point. The effect of curvature on manual point selection was evaluated with scatter plots. Results Bland-Altman plots revealed a high variability of examiner's recession measurements indicated by high 95% limits of agreement range of approximately 1mm and several outliers beyond the limits of agreement. CEJ point selection was the main source of examiner's variability due to smaller curvature values than GM, i.e., median values of -0.98mm− 1 and − 4.39mm− 1, respectively, indicating straighter profile for CEJ point. Scatter plots revealed inverse relationship between curvature and examiner deviation for CEJ point, indicating a threshold curvature value around 1mm− 1. Conclusions Automated curvature-based approach increases the precision of recession measurements by reproducible measurement point selection. Proposed approach allows evaluation of teeth with indistinguishable CEJ that could be not be included in the previous studies.

Sequential Diagnosis by Abstraction

When a system behaves abnormally, sequential diagnosis takes a sequence of measurements of the system until the faults causing the abnormality are identified, and the goal is to reduce the diagnostic cost, defined here as the number of measurements. To propose measurement points, previous work employs a heuristic based on reducing the entropy over a computed set of diagnoses. This approach generally has good performance in terms of diagnostic cost, but can fail to diagnose large systems when the set of diagnoses is too large. Focusing on a smaller set of probable diagnoses scales the approach but generally leads to increased average diagnostic costs. In this paper, we propose a new diagnostic framework employing four new techniques, which scales to much larger systems with good performance in terms of diagnostic cost. First, we propose a new heuristic for measurement point selection that can be computed efficiently, without requiring the set of diagnoses, once the system is modeled as a Bayesian network and compiled into a logical form known as d-DNNF. Second, we extend hierarchical diagnosis, a technique based on system abstraction from our previous work, to handle probabilities so that it can be applied to sequential diagnosis to allow larger systems to be diagnosed. Third, for the largest systems where even hierarchical diagnosis fails, we propose a novel method that converts the system into one that has a smaller abstraction and whose diagnoses form a superset of those of the original system; the new system can then be diagnosed and the result mapped back to the original system. Finally, we propose a novel cost estimation function which can be used to choose an abstraction of the system that is more likely to provide optimal average cost. Experiments with ISCAS-85 benchmark circuits indicate that our approach scales to all circuits in the suite except one that has a flat structure not susceptible to useful abstraction.

SELECTING MEASUREMENT LOCATIONS TO MINIMIZE RECONSTRUCTION ERROR USING THE INVERSE BOUNDARY ELEMENT METHOD

This paper details an approach to select measurement point locations for the inverse boundary element method. An accurate reconstruction of the vibration requires a well conditioned acoustic transfer matrix, which depends on measurement point selection. Matrix techniques can be used to regularize the solution though they often lead to poor reconstruction rank. A technique to determine the number of measurement points required, and their placement, prior to measurement has been developed using three criteria: uniqueness, completeness, and measurement point density. With this technique, the reconstruction error and the number of measurements can be minimized.