首页|Findings on Artificial Intelligence Reported by Investigators at Near East Unive rsity (Artificial Intelligence-based Algorithm for Cervical Vertebrae Maturation Stage Assessment)
Findings on Artificial Intelligence Reported by Investigators at Near East Unive rsity (Artificial Intelligence-based Algorithm for Cervical Vertebrae Maturation Stage Assessment)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on Artificial Intelligence have bee n presented. According to news reporting originating from Mersin, Turkey, by New sRx correspondents, research stated, "The aim of this study was to develop an ar tificial intelligence (AI) algorithm to automatically and accurately determine t he stage of cervical vertebra maturation (CVM) with the main purpose being to el iminate the human error factor. Setting and Sample Population Archives of the ce phalometric images were reviewed and the data of 1501 subjects with fully visibl e cervical vertebras were included in this retrospective study." Our news editors obtained a quote from the research from Near East University, " Lateral cephalometric (LC) that met the inclusion criteria were used in the trai ning process, labeling was carried out using a computer vision annotation tool ( CVAT), tracing was done by an experienced orthodontist as a gold standard and, i n order to limit the effect of the uneven distribution of the training data set, maturation stage was classified with a modified Bachetti method by the operator who labelled them. The labelled data were split randomly into a training set (8 0%), a testing set (10%) and an validation set (10% ), to measure intra-observer, inter-observer reliability, intraclass correlation coefficient (ICC) and weighted Cohen's kappa test was carried out. The ICC was valued at 0.973, weighted Cohen's kappa standard error was 0.870 +/- 0.027 which shows high reliability of the observers and excellent level of agreement betwee n them, the segmentation network achieved a global accuracy of 0.99 and the aver age dice score overall images was 0.93. The classification network achieved an a ccuracy of 0.802, class sensitivity of (pre-pubertal 0.78; pubertal 0.45; post-p ubertal 0.98), respectively, per class specificity of (pre-pubertal 0.94; pubert al 0.94; post-pubertal 0.75), respectively. The developed algorithm showed the a bility to determine the cervical vertebrae maturation stage which might aid in a faster diagnosis process by eliminating human intervention, which might lead to wrong decision-making procedures that might affect the outcome of the treatment plan."
MersinTurkeyEurasiaAlgorithmsArt ificial IntelligenceEmerging TechnologiesMachine LearningNear East Univers ity
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