METHODS: The pre- and post-operative CT images of 55 patients undergoing DC surgery were analyzed. The ICV was measured by segmenting every slice of the CT images, and compared with estimated ICV calculated using the 1-in-10 sampling strategy and processed using the SBI method. An independent t test was conducted to compare the ICV measurements between the two different methods. The calculation using this method was repeated three times for reliability analysis using the intraclass correlations coefficient (ICC). The Bland-Altman plot was used to measure agreement between the methods for both pre- and post-operative ICV measurements.
RESULTS: The mean ICV (±SD) were 1341.1±122.1ml (manual) and 1344.11±122.6ml (SBI) for the preoperative CT data. The mean ICV (±SD) were 1396.4±132.4ml (manual) and 1400.53±132.1ml (SBI) for the post-operative CT data. No significant difference was found in ICV measurements using the manual and the SBI methods (p=.983 for pre-op, and p=.960 for post-op). The intrarater ICC showed a significant correlation; ICC=1.00. The Bland-Altman plot showed good agreement between the manual and the SBI method.
CONCLUSION: The shape-based interpolation method with 1-in-10 sampling strategy gave comparable results in estimating ICV compared to manual segmentation. Thus, this method could be used in clinical settings for rapid, reliable and repeatable ICV estimations.
Materials and methods: A total of 40 patients were recruited, (mean age = 23 years) and were assigned to low and moderate caries risk groups (n = 20). Eighty occlusal surfaces of posterior teeth were examined for early caries lesion visually and using SoproLife® at baseline and at a recall visit six months later. At baseline visit, patients were given oral hygiene education, fluoridated toothpaste for homecare and topical fluoride application. SoproLife® images acquired were analysed using Image J software version 1.50. Difference in the mean value of intensity of the red wavelength spectrum between baseline and recall visits, (ΔI), were analysed for both risk groups. ΔI for upper and lower first molar teeth were also analysed.
Results: Results show no statistical difference for ΔI between low and moderate risk groups (p = 0.13). There is no statistical difference in ΔI within the low caries risk group (p = 0.42) but there is significant difference in the moderate risk group (p = 0.02). No statistically significant difference in ΔI value between upper first molars (UFM) (p = 0.80) and lower first molars (LFM) (p = 0.07) were detected. There was also no statistically significant difference in ΔI value within the upper and lower first molars (UFM: p = 0.31, LFM: p = 0.27).
Conclusion: SoproLife® generated images did not show significant differences in remineralisation of early caries between low and moderate caries risk patients and between the upper first and lower first permanent molars in these patients.
METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.
RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.
CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.