Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.

Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector-based convolutional neural network (V-CNN) with respect to extracted features of the induced pluripotent stem cell (iPSC) colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies was compared with that of the competitive support vector machine (SVM) classifier based on morphological, textural, and combined features. Additionally, five-fold cross-validation was used to investigate the performance of the V-CNN model. The precision, recall, and F-measure values of the V-CNN model were comparatively higher than those of the SVM classifier, with a range of 87-93%, indicating fewer false positives and false negative rates. Furthermore, for determining the quality of colonies, the V-CNN model showed higher accuracy values based on morphological (95.5%), textural (91.0%), and combined (93.2%) features than those estimated with the SVM classifier (86.7, 83.3, and 83.4%, respectively). Similarly, the accuracy of the feature sets using five-fold cross-validation was above 90% for the V-CNN model, whereas that yielded by the SVM model was in the range of 75-77%. We thus concluded that the proposed V-CNN model outperforms the conventional SVM classifier, which strongly suggests that it as a reliable framework for robust colony classification of iPSCs. It can also serve as a cost-effective quality recognition tool during culture and other experimental procedures.

Distributional Variations in the Quantitative Cortical and Trabecular Bone Radiographic Measurements of Mandible, between Male and Female Populations of Korea, and its Utilization.

It is important to investigate the irregularities in aging-associated changes in bone, between men and women for bone strength and osteoporosis. The purpose of this study was to characterize the changes and associations of mandibular cortical and trabecular bone measures of men and women based on age and to the evaluation of cortical shape categories, in a large Korean population. Panoramic radiographs of 1047 subjects (603 women and 444 men) aged between 15 to 90 years were used. Mandibular cortical width (MCW), mandibular cortical index (MCI), and fractal dimensions (FD) of the molar, premolar, and anterior regions of the mandibular trabecular bone were measured. Study subjects were grouped into six 10-years age groups. A local linear regression smoothing with bootstrap resampling for robust fitting of data was used to estimate the relationship between radiographic mandibular variables and age groups as well as genders. The mean age of women (49.56 ± 19.5 years) was significantly higher than that of men (45.57 ± 19.6 years). The MCW of men and women (3.17mm and 2.91mm, respectively, p < 0.0001) was strongly associated with age and MCI. Indeed, trabecular measures also correlated with age in men (r > -0.140, p = 0.003), though not as strongly as in women (r > -0.210, p < 0.0001). In men aged over 55 years, only MCW was significantly associated (r = -0.412, p < 0.0001). Furthermore, by comparison of mandibular variables from different age groups and MCI categories, the results suggest that MCW was detected to be strongly associated in both men and women for the detection of bone strength and osteoporosis. The FD measures revealed relatively higher association with age among women than men, but not as strong as MCW.

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.

OBJECTIVES: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. METHODS: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. RESULTS: Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. CONCLUSIONS: The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.