MATERIALS AND METHODS: One hundred and fifty mammography patients above 40 years and undergoing EIT were chosen using convenient sampling. Visual interpretation of the images was carried out by a radiologist with minimum of three years experience using the breast imaging - electrical impedance (BI-EIM) classification for detection of abnormalities. A set of thirty blinded EIT images were reinterpreted to determine the intra-rater reliability using kappa. Quantitative assessment was by comparison of the breast average electric conductivity with the norm and correlations with visual interpretation of the images were determined using Chi-square. One-way ANOVA was used to compare the mean electrical conductivity between groups and t-test was used for comparisons with pre-existing Caucasians statistics. Independent t-tests were applied to compare the mean electrical conductivity of women with factors like exogenous hormone use and family history of breast cancer.
RESULTS: The mean electrical conductivity of Malaysian women was significantly lower than that of Caucasians (p<0.05). Quantitative assessment of electrical impedance tomography was significantly related with visual interpretation of images of the breast (p<0.05).
CONCLUSIONS: Quantitative assessment of electrical impedance tomography images was significantly related with visual interpretation.
METHODS: Fifty digital models were scanned from the same plaster models. Arch and tooth size measurements were made by 2 operators, twice. Calibration was done on 10 sets of models and checked using the Pearson correlation coefficient. Data were analyzed by error variances, repeatability coefficient, repeated-measures analysis of variance, and Bland-Altman plots.
RESULTS: Error variances ranged between 0.001 and 0.044 mm for the digital caliper method, and between 0.002 and 0.054 mm for the 3D software method. Repeated-measures analysis of variance showed small but statistically significant differences (P <0.05) between the repeated measurements in the arch and buccolingual planes (0.011 and 0.008 mm, respectively). There were no statistically significant differences between methods and between operators. Bland-Altman plots showed that the mean biases were close to zero, and the 95% limits of agreement were within ±0.50 mm. Repeatability coefficients for all measurements were similar.
CONCLUSIONS: Measurements made on models scanned by the 3D structured-light scanner were in good agreement with those made on conventional plaster models and were, therefore, clinically acceptable.
RESULT: An automated 3D modeling pipeline empowered by an Artificial Neural Network (ANN) was developed. This automated 3D modelling pipeline enables automated deformation of a generic 3D model of monogenean anchor into another target 3D anchor. The 3D modelling pipeline empowered by ANN has managed to automate the generation of the 8 target 3D models (representing 8 species: Dactylogyrus primaries, Pellucidhaptor merus, Dactylogyrus falcatus, Dactylogyrus vastator, Dactylogyrus pterocleidus, Dactylogyrus falciunguis, Chauhanellus auriculatum and Chauhanellus caelatus) of monogenean anchor from the respective 2D illustrations input without repeating the tedious modelling procedure.
CONCLUSIONS: Despite some constraints and limitation, the automated 3D modelling pipeline developed in this study has demonstrated a working idea of application of machine learning approach in a 3D modelling work. This study has not only developed an automated 3D modelling pipeline but also has demonstrated a cross-disciplinary research design that integrates machine learning into a specific domain of study such as 3D modelling of the biological structures.