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Detecting and monitoring dental plaque is an important issue in research and clinical practice. In this context, new digital imaging methods that permit permanent documentation of the clinical findings could be promising tools. The aim of the study was therefore to investigate whether disclosed plaque can be reliably visualised on 2D and 3D images captured with digital intraoral imaging devices. Clinical examination was the reference method. Twenty subjects (27.5±1.2 years) were included and plaque was measured at three different stages: habitual plaque (T1), after 72 h without oral hygiene (T2) and after a subsequent habitual brushing exercise (T3). At each time point, plaque was disclosed followed by the clinical examination and capturing the 2D and 3D images (intraoral-camera CS 1500 and intraoral-scanner CS 3600; Carestream Dental, Germany). Plaque amounts were recorded on oral and vestibular surfaces of the Ramfjord-teeth (16, 21, 24, 36, 41, 44) using the Rustogi-modified-Navy-Plaque-Index (RMNPI) and expressed as percentage of plaque-containing RMNPI areas of all RMNPI areas. At T1, percentages (mean±SD) obtained from the clinical examination, 2D and 3D images were 62.2±10.6, 65.1±10.0 and 64.4±10.6 resp. increasing to 76.9±8.0, 77.9±8.6 and 77.5±9.4 resp. at T2. After toothbrushing (T3), values decreased to 56.3±11.1, 58.2±12.1 and 61.2±10.8 resp. All methods were able to show statistically significant changes in plaque amounts at the different time points with in part statistically significant but minor differences between them. The Bland-Altmann analysis revealed a good agreement between values from both 2D and 3D images with the clinical examination. The agreement of the scores obtained with the both image-based methods for the single RMNPI areas with the clinical examination was mainly classified as substantial to almost perfect. Amounts of plaque can be reliably detected and monitored on 2D images from an intraoral camera and on 3D images from an intraoral scanner. [ABSTRACT FROM AUTHOR]

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide. [ABSTRACT FROM AUTHOR]

A new semi-automated method is proposed in this study that detects joint traces from digital images and calculates the length distribution through three-dimensional data structuring. The method comprises detecting the pixels corresponding to the joint trace in digital images and calculating the length distribution through three-dimensional data structuring of the pixels detected as the joint trace. Semantic segmentation based on deep learning techniques is applied to the detection of joint trace pixels to overcome the limitations of rule-based image processing techniques. For training a deep learning network, various rock joint images were sampled and labeled, following which the data sets for training, validation, and testing were prepared through classification and augmentation. Using the prepared datasets, the performance of the classifiers based on widely used semantic segmentation networks, namely U-Net (University of Freiburg, Germany) network and DeepLabV3+ (Google, USA) network, was compared. The classifier based on the DeepLabV3+ network was ultimately selected and applied. The trained classifier successfully detected pixels corresponding to the joint trace in images of both flat and uneven rock surfaces, indicating that the deep learning technique could be applied effectively to joint trace detection from digital images. Further, a data structuring technique is proposed to calculate the joint trace length using pixelwise data detected through the trained classifier. This technique uses point cloud data obtained by photogrammetry, and comprises two-dimensional thinning and segmentation, three-dimensional projection, segmentation, and segment linking. The entire proposed method, from detecting the joint trace pixels to three-dimensional data structuring, was verified by applying it to both simple flat and uneven rock surface models. • New technique for joint trace detection using deep learning network is proposed. • New data structuring technique for pixelwise data is proposed. • Semi-automatic calculation of the trace length is possible using the two techniques. • Proposed techniques is verified by applying it to both flat and uneven rock surfaces. [ABSTRACT FROM AUTHOR]

Background: Scoliosis is defined as a curvature of the spine in the coronal plane, with its idiopathic form being the most frequently encountered. Diagnosis is based on spinal X-Ray imaging, performing a Cobb angle measurement, which represents the angle calculated between two perpendicular lines traced at the superior margin of the uppermost involved vertebra, and the inferior margin of the lowest affected vertebra. A Cobb angle greater than 10 degrees is considered as positive evidence of spinal deformity and confirms the diagnosis. The traditional Cobb angle measurements are time-consuming and prone to human errors; therefore, an automated measuring algorithm could reduce the diagnostic interval, and hypothetically improve measurement accuracy. Objective: The aim of this study is to investigate and test the application of artificial intelligence (AI), and its existing algorithms, for automated Cobb angle measurement and scoliosis assessment. Material and methods: Radiographic images of 197 patients have been obtained from the database of a private clinic from Targu-Mures, spanning the period between 30/11/2023 and 20/02/2024. The acquired images underwent processing using the AI Open-Source Software "Cobb Angle Calculator". Additionally, a manual calculation was performed using the "RadiAnt DICOM Viewer" software to ensure a comparison between automated and manual methods. Statistical analysis was carried out utilizing the GraphPad InStat software to statistically assess differences between the automated and the manually calculated measurements. Results: Out of the 197 analyzed X-Ray images, the manual measurement of the Cobb angle led to a diagnosis of scoliosis in 170 patients. The measurements were conducted three times, in order to minimize errors. Among these, 52 patients were diagnosed with thoracic scoliosis, 51 with lumbar deformities, and 67 with combined thoraco-lumbar curvatures. The roentgenograms were then processed with the AI software "Cobb Angle Calculator", and the outcomes compared with the ones calculated manually. Statistical analysis demonstrated that the difference in accuracy of the two methods is extremely significant (p value=0.0002), thus rejecting the null hypothesis. Subsequently, each X-ray image underwent a contrast-enhancing process to improve visualization of the skeletal structures, and the results were compared a second time. The obtained p value of 0.3019, considered not statistically significant, confirmed the null hypothesis, therefore proving the valuable aid of AI in Cobb angle quantification as an alternative to manual evaluation. Conclusions: Automated Cobb angle measurement, being at the beginning of its era, lacks sufficient accuracy when employed without a specific image contrast-enhancing procedure, hence displaying high probability of scoliosis misdiagnosis. Integrating an image contrast-enhancing process is a pivotal step forward in measurement automation, and consequently offers a crucial alternative, and possibly future substitution, to the available standard techniques. [ABSTRACT FROM AUTHOR]

Load forecasting is of crucial importance for operations of electric power systems. In recent years, deep learning based methods are emerging for load forecasting because their strong nonlinear approximation capabilities can provide more forecasting precision than conventional statistical methods. However, they usually suffer from some problems, e.g., the gradient vanishment and over-fitting. In order to address these problems, an unshared convolution based deep learning model with densely connected network is proposed. In this model, the backbone is the unshared convolutional neural network and a densely connected structure is adopted, which could alleviate the gradient vanishment. What is more, we use a regularization method named clipped $L_2$ -norm to overcome over-fitting, and design a trend decomposition strategy to address the possible distribution differences between the training and validation data. Finally, we conduct five case studies to verify the outperformance of our proposed deep learning model for deterministic and interval load forecasting. Two high-voltage and an medium-voltage real load datasets from Australia, Germany and America are used for model training and validation, respectively. Results show that the proposed model can achieve higher load forecasting accuracy, compared with other existing methods including the popular conventional methods such as naive forecast and generalized additive model, and deep learning methods, e.g., long short-term memory network, convolutional neural network, fully connected network, etc. [ABSTRACT FROM AUTHOR]