Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence

The aim of the present study was to investigate the diagnostic performance of a trained convolutional neural network (CNN) for detecting and categorizing fissure sealants from intraoral photographs using the expert standard as reference. An image set consisting of 2352 digital photographs from permanent posterior teeth (461 unsealed tooth surfaces/1891 sealed surfaces) was divided into a training set (n = 1881/364/1517) and a test set (n = 471/97/374). All the images were scored according to the following categories: unsealed molar, intact, sufficient and insufficient sealant. Expert diagnoses served as the reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. A statistical analysis was performed, including the calculation of contingency tables and areas under the receiver operating characteristic curve (AUC). The results showed that the CNN accurately detected sealants in 98.7% of all the test images, corresponding to an AUC of 0.996. The diagnostic accuracy and AUC were 89.6% and 0.951, respectively, for intact sealant; 83.2% and 0.888, respectively, for sufficient sealant; 92.4 and 0.942, respectively, for insufficient sealant. On the basis of the documented results, it was concluded that good agreement with the reference standard could be achieved for automatized sealant detection by using artificial intelligence methods. Nevertheless, further research is necessary to improve the model performance.

Theoretical Calculations of Economic Life of Firefighting Appliances Based on Chassis Tatra in the South Moravian Region

This paper is focused on the evaluation of economic data obtained from operational records of firefighting equipment with a focus on firefighting and rescue appliances, especially on exit vehicles based on the chassis CAS 20 – TATRA T815-231R55 18 325 4x4.2. These vehicles have been operated by professional units of the Fire and Rescue Service in the South Moravian Region since September 2013. The producer of firefighting superstructures WISS GROUP, Bielsko-Biala, Poland, was a supplier of all these vehicles. The paper’s aim is to specify the optimum lifetime of the firefighting vehicles by the analysis of firefighting vehicles’ economical operation. Theoretical calculations of the optimum lifetime have been processed with implementing both the method of exponential trends and the Brown method. The residual value of vehicles has been calculated both according to the current Czech tax law, and to the Expert Standard Valuation of motor vehicles in force in the Czech Republic.

Assessing Knowledge Structures: Don't Always Call an Expert

Measures of knowledge structures can be used to access and evaluate conceptual understanding for assessment and training purposes. Typically, the quality of an individual's knowledge structure is determined by comparing it to a standard knowledge structure that is an aggregate of the structures of several experts. Recent research suggests that this approach may not be appropriate for all domains. This study investigated different approaches for forming a standard knowledge structure for two knowledge structure measures: relatedness ratings and a diagramming task. Three approaches to developing knowledge standards were compared: a standard derived from expert data, a standard based on high-performing students, and a rational standard developed through an analysis of instructional materials. The knowledge standards were compared in their ability to predict performance on a multiple-choice test. The results showed that comparison of students' structures with a standard constructed by aggregating high-performing student structures produced scores that were independently predictive of performance for both measures, whereas the expert standard resulted in independently predictive knowledge scores for only the diagramming task. For both measures, the high-performer standard and the aggregate expert standard were superior to the rational standard. These results offer support for using standards other than the expert-consensus standard typically used when assessing the quality of knowledge structures.