Image Measurement Research Articles

Effect of meat type and fat level on burger meat characteristics: A combined approach using imaging and sensory techniques.

This study aimed to evaluate the effect of fat level and meat cut type on burger meat through color, texture, image, and sensory analyses, and to explore the ability of the imaging technique as a complementary tool for consumer quality perception. For this purpose, burger meat samples were prepared by combining pork and beef meat (50/50%) with other nonmeat ingredients. The differences between samples were fat level: around 15 g fat/100 g (code HF) or 10.5 g fat/100 g (code LF); cut types: from cow carcasses (code C) or yearling (code Y). Instrumental color and texture measures analysis, an image analysis, and a sensory evaluation were carried out on samples (raw and cooked) at two times: day 0 and day 1 before expiry dates. The results showed that the samples made with meat from cows presented lower L* and higher a* and b* values than the samples made from yearling. However, the fat level did not affect this parameter. This same pattern was observed for the image measurements. Regarding texture, the samples with higher fat content (and lower moisture content) had higher hardness values. Generally, meat type had no effect on textural parameters. The samples that consumers gave the highest overall acceptance scores were those made of cow meat at both fat content levels. These samples also had the highest raw and cooked color scores. The fat level slightly affected hardness, with the same pattern observed for the instrumental measures of texture. The analyses showed a good correlation between instrumental techniques and sensory evaluation.

Effect of solid loading and particle size on bubble behavior and flow field structure in slurry bubble column

The complexity of fluid dynamics in a slurry bubble column reactor introduces significant uncertainty in reactor design and scale-up. This paper investigates the hydrodynamic performance of the gas–liquid–solid system within the reactor by employing computational fluid dynamics-population balance modeling numerical simulations alongside particle image velocimetry (PIV) experiments. The effect of superficial gas velocity and particle conditions on the overall gas holdup were analyzed, focusing on the effects of particle size and solid concentration on bubble size, bubble behavior, flow field structure, and local gas holdup distribution at high superficial gas velocities. Bubble size was evaluated using calibrated image measurements, and the impact of varying solid conditions was thoroughly explored. The results revealed that an increase in solid size correlated with higher gas holdup and smaller bubble sizes, whereas a greater solid concentration resulted in decreased gas holdup and larger bubble sizes. PIV experiments indicated that bubbles exhibited a tendency to migrate toward the central region of the reactor, leading to the formation of larger bubbles that accelerated the rise of surrounding bubbles, while smaller bubbles near the wall moved downward. As the slurry bed height increased, the range of local gas holdup distribution expanded, resulting in a symmetrical distribution of radial local gas holdup in the fully developed stage at a height of 0.16 m.

Mitral valve phenotypes in SMAD3-related thoracic aortic disease: insights from the Montalcino Aortic Consortium

Abstract Background SMAD3 pathogenic variants (PV) predispose to heritable thoracic aortic aneurysms and dissections (HTAD). Mitral annular disjunction (MAD) associated with mitral valve prolapse (MVP) and mitral regurgitation (MR) was identified as a potential new marker of disease severity in Marfan syndrome. The prevalence and prognostic impact of these mitral phenotypes in other forms of HTAD are less known. Purpose We hypothesize that mitral abnormalities are increased in SMAD3-related HTAD and are associated with increased risk for valve and aortic complications. Methods The Montalcino Aortic Consortium (MAC) registry has enrolled participants with PV in 15 HTAD genes. Subjects >16 years old with complete imaging and clinical data were included in this study. MVP was defined according to current guidelines. MR was classified as mild, moderate, or severe. MAD was defined by a >3-millimeter gap between the posterior mitral valve leaflet hinge point and the inferolateral myocardium as confirmed by direct measurement of echocardiogram images. Frequencies of MVP, MR, and MAD were compared between MAC participants with SMAD3 PV and other HTAD PV. Associations between clinical and echo characteristics and the composite outcome of arrhythmia, aortic surgery, aortic dissection, or congestive heart failure was evaluated with multivariable logistic regression. Results In 671 MAC participants (129 with SMAD3 PV, 36 [IQR 19-51] years, 49% female), the prevalence of MVP was 15% and the prevalence of MR was 22%. Both MVP (31/129, 24%, vs. 67/542, 12%, OR 2.2 [1.4-3.6], P<0.001) and MR (40/129, 31%, vs. 108/542, 20%, OR 1.8 [1.2-2.8], P<0.006) were more common in participants with SMAD3 PV compared to other PV and were further enriched in cases with SMAD3 missense PV compared to loss of function PV (MR or MVP: 26/48, 54% vs. 7/30, 23%, OR 3.9 [1.4-10.8], P<0.007). Images were available from 238 MAC participants (67 with SMAD3 PV) to assess for MAD. MAD (29/67, 43%, vs. 28/171, 16%, OR 3.9 [2.1-7.3], P<0.0001), or the composite of MR, MVP, or MAD (42/67, 63%, vs. 69/171, 35%, OR 3.2 [1.8-5.7], P<0.0001) was more common with SMAD3 PV compared to other PV (Figure 1). Twelve participants with SMAD3 PV (18%) had prominent mitral phenotypes (>mild MR, >10 mm MAD, or MAD with MVP) but no significant aortic dilation (Z<3). When controlling for age and sex, MR or MVP, but not MAD, was independently associated with the composite outcome (OR 16 [2.4-110], Figure 2). Conclusion Mitral valve pathology, including MVP, MR, and MAD, is increased in individuals with SMAD3 PV compared to other HTAD PV. Prominent mitral phenotypes related to SMAD3 missense PV may identify a high-risk subgroup with adverse cardiovascular outcomes. Because congenital mitral disease may be the primary presenting feature of SMAD3 PV, genetic testing for HTAD should be considered for such patients, especially if they also have a family history of thoracic aortic disease.

RECOGNITION OF DURICRUST GEOMORPHIC SURFACES USING A RELIEF DISSECTION MATRIX

Landforms can orientate the hydrodynamics and distribution of materials on the ground, producing flattened and/or lowered geomorphic surfaces given the evolutionary characteristics from climatic, morphogenetic and pedogenetic factors. This work aims to demonstrate the compartmentalization and association of geomorphic surfaces covered by ferruginous duricrusts in Savannah areas in Brazil according to a relief dissection matrix. This matrix indicates the vertical cutting of the valleys and the interfluvial horizontal dimension of the hills through landform measurements in radar images, classification in alphanumeric codes and statistical correlations. The results indicated details in the compartmentalization of the surface according to the landform roughness, making it possible to link, through data from the dissection matrix and the statistical correlation, the association between the compartments according to summit positions and remaining depressed areas. Despite the fact that more robust measurements obtained from high-precision mappings can point out further landform evolutionary stages, it is understood that a dissection matrix and its statistical correlations can not only compartmentalize the landscape and provide an initial overview of the landscape’s evolution, but also indicate geomorphic surfaces that could have been worked at the same time. In this sense, the recognition of geomorphic surfaces, occurrence of duricrusts and landforms in this work allowed a certain morphogenetic correspondence that can contribute to geomorphological studies, although the results must be observed with caution when verified from the tropical perspective of relief evolution. Keywords: Valley Cutting; Interfluvial Dimension; Dissection Matrix; Geomorphic Surfaces; Duricrusts

Mitral Annular Disjunction in Heritable Thoracic Aortic Disease: Insights From the Montalcino Aortic Consortium.

Mitral annular disjunction (MAD), posterior displacement of the mitral valve leaflet hinge point, predisposes to arrhythmias or sudden cardiac death. We evaluated the burden of MAD, mitral valve prolapse (MVP), and mitral regurgitation (MR) by heritable thoracic aortic disease gene in a cross-sectional analysis of 2014-2023 data in the Montalcino Aortic Consortium registry. MAD was determined by direct measurement of echocardiographic images. MR and MVP were defined according to current clinical guidelines. Associations were evaluated using χ2 or Fisher exact tests. MR and MVP were enriched in Montalcino Aortic Consortium participants (672) with pathogenic variants (PV) in transforming growth factor-β pathway genes. The combination of MR and MVP was associated with mitral surgery and arrhythmias. In the subgroup with available images, MAD was enriched in SMAD3 PV compared with other transforming growth factor-β PV (prevalence ratio 1.8 [1.1-2.8], P <0.02). Severe disjunction (>10 mm) was only observed in the transforming growth factor-β subgroup and was further enriched in participants with SMAD3 PV (prevalence ratio 3.1 [1.1-8.6]). MVP (prevalence ratio 5.2 [3.0-9.0]) and MR (PR 2.7 [1.8-3.9]) were increased in participants with MAD, but MAD was not independently associated with adverse cardiac or aortic events. Pathological mitral valve phenotypes are more prevalent in individuals with PV in transforming growth factor-β pathway genes, particularly SMAD3. MR and MVP but not MAD are associated with adverse aortic and cardiac events. Because congenital mitral disease may be the primary presenting feature of SMAD3 PV, genetic testing for heritable thoracic aortic disease should be considered for such individuals, especially if they also have a family history of heritable thoracic aortic disease.

Applications of deep learning-based denoising methodologies for scanning electron microscope images

Abstract In this paper, we use five types of deep-learning algorithms for denoising scanning electron microscope (SEM) measurement data. Denoising of SEM images is an important task since the images often suffer from noise, which can make it difficult to accurately interpret the data. We also investigate realistic SEM denoising characteristics using a variety of metrics to assess the quality of denoised images. Overall, we find that the trained generative models provide superior denoising performance and that it is crucial to objectively quantify the performance, just like in the scanning process itself. It is anticipated that the deep-learning based technique can accelerate image measurements, which can be utilized for very fast analytical investigations. We also demonstrate that the success of a generative model may depend on the appropriate assessment of noise characteristics in the specific image data analysis of interest. Moreover, it is addressed that denoising performance can be properly evaluated when a relevant metrics that aligns well with human visual systems.

Piximi - An Images to Discovery web tool for bioimages and beyond.

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

A new method to evaluate fluoroscopic system collimator performance.

Fluoroscopy uses collimators to limit the radiation field size. Collimators are often evaluated annually during equipment performance evaluations to maintain compliance with regulatory and/or accreditation bodies. A method to evaluate and quantify fluoroscopy collimator performance was developed. A radiation field and displayed image measurement device consisting of radiopaque rulers and radiochromic film strips was placed on the x-ray source assembly exit window to evaluate fluoroscopy collimator performance. This method was used to evaluate collimator performance on 79 fluoroscopic imaging systems including fixed C-arms, mobile C-arms, mini C-arms, and radiographic fluoroscopic systems. The excess length (EL), excess width (EW), and sum EL + EW of the radiation field relative to the displayed image were measured and compared to the limits specified in 21CFR1020.32. Four systems exceeded these limits. Placing the radiation measurement device at the x-ray source assembly exit window relative to the image receptor cover increased the film exposure rate by a factor up to 14.6. The time required to set up and complete the fluoroscopy collimator performance measurements using this method ranged from 5 to 10 min. This method provides an easily implemented quantitative measure of fluoroscopy system collimator performance that satisfies regulatory and accreditation body requirements.

Photonic-aid tri-band multi-frequency measurement system based on zero-IF architecture

A photonic-aid tri-band multi-frequency measurement system based on a zero-IF architecture is proposed. An linearly frequency modulated (LFM) waveform and the signal under test (SUT) are carrier-suppressed double sideband modulated onto a dual-wavelength light wave. By utilizing a fiber Bragg grating (FBG) to perform optical spectrum separation, two frequency measurement channels are constructed. After post-processing of the measured waveform, a tri-band frequency-to-time mapping (FTTM) is realized. In the proof-of-concept experiment, single and multi-tone frequency measurement spanning 2-3.2 GHz, 6.7-7.9 GHz, and 11.9-13.1 GHz are demonstrated. Measurement errors below 3 MHz, 30 MHz, and 30 MHz, and a resolution under 0.8 MHz are achieved. Thanks to the zero-IF architecture and tri-band FTTM, the proposed scheme shows better robustness to image interference and measurement resolution compared with previous schemes based on high IF structures and single FTTM.

Investigation of droplet splashing behavior during oblique jet impact onto a wall

For the issue of jet impingement on the wall in industrial cooling processes, an experimental setup based on high-speed photography for oblique jet impingement onto the wall was constructed. The experimental focus was on the study of liquid droplet splashing behavior after oblique jet impingement on the wall, discussing the liquid droplet splashing behavior under three jet impingement modes: Rayleigh regime, first wind-induced regime, and secondary wind-induced regime. By employing methods such as trajectory imaging and particle image velocity for liquid droplet parameter image measurement, obtaining the particle size, velocity, and distribution of splashed droplets after oblique jet impact on walls under different working conditions. The impact of jet impingement velocity and angle on droplet splashing parameters was analyzed. The results showed that when the impingement point is before the breakup length, with increasing flow velocity, the surface wave of the liquid column, and the spreading liquid film became more pronounced, but the loss of liquid-phase components due to splashing was relatively small. When the impingement point is after the breakup length, the secondary breakup resulting in a “crown”-shaped liquid film after droplet impingement leads to a significant loss of liquid-phase components through splashing. As the inlet velocity of the jet increases, there is a decreasing trend in droplet size and an increasing trend in droplet velocity. With an increase in jet angle, there is a decreasing trend in droplet size and velocity. Based on the concentration, size, and velocity distribution characteristics of splashing droplets, the area after oblique jet impingement on the wall can be divided into the impingement zone, low-concentration low-velocity zone, high-concentration high-velocity zone, and lateral splashing zone. This has significant implications for understanding the splashing mechanism after oblique jet impingement on the wall and optimizing operating conditions.

Non-Contact Human Body Dimension Estimation Methods Based on Deep Learning: A Critical Analysis

With the advancement of information technology, Human Body Dimension Estimation (HBDE) is moving towards digitalization and non-contact methods. This paper critically reviews non-contact HBDE methods based on deep learning, primarily covering image-based methods, the Adaptive Body Structure Segmentation (ABSS) algorithm with height and weight measurements, and 3D meshes or point cloud data methods. Convolutional Neural Networks (CNNs) excel in image measurement, while the ABSS algorithm with adaptive segmentation achieves high accuracy. Recent studies, such as the Grey Wolf Optimizer-Elman Neural Network (GBWO-ENN) model, have significantly improved prediction accuracy by optimizing neural network structures. Additionally, 3D point cloud data combined with the Skinned Multi-Person Linear (SMPL) model has proven effective in complex scenarios. This paper summarizes the performance of various methods, analyzes common datasets, and explores future development directions for non-contact HBDE. Despite significant progress, this field still faces challenges such as data quality, scarcity of extreme samples, and privacy issues. Future research should focus on building high-quality datasets and developing lightweight measurement networks to meet practical application needs.

DMD and microlens array as a switchable module for illumination angle scanning in optical diffraction tomography.

Optical diffraction tomography (ODT) enables label-free and morphological 3D imaging of biological samples using refractive-index (RI) contrast. To accomplish this, ODT systems typically capture multiple angular-specific scattering measurements, which are used to computationally reconstruct a sample's 3D RI. Standard ODT systems employ scanning mirrors to generate angular illuminations. However, scanning mirrors are limited to illuminating the sample from only one angle at a time. Furthermore, when operated at high speeds, these mirrors may exhibit mechanical instabilities that compromise image quality and measurement speed. Recently, newer ODT systems have been introduced that utilize digital-micromirror devices (DMD), spatial light modulators (SLMs), or LED arrays to achieve switchable angle-scanning with no physically-scanning components. However, these systems associate with power inefficiencies and/or spurious diffraction orders that can also limit imaging performance. In this work, we developed a novel non-interferometric ODT system that utilizes a fully switchable module for angle scanning composed of a DMD and microlens array (MLA). Compared to other switchable ODT systems, this module enables each illumination angle to be generated fully independently from every other illumination angle (i.e., no spurious diffraction orders) while also optimizing the power efficiency based on the required density of illumination angles. We validate the quantitative imaging capability of this system using calibration microspheres. We also demonstrate its capability for imaging multiple-scattering samples by imaging an early-stage zebrafish embryo.

Ultrasound for assessing paediatric body composition and nutritional status: Scoping review and future directions.

This scoping review aims to assess the utility of ultrasound as a prospective tool in measuring body composition and nutritional status in the paediatric population. We provide a comprehensive summary of the existing literature, identify gaps, and propose future research directions. We conducted a systematic scoping review following the PRISMA Extension for Scoping Reviews guidelines. This involved screening titles and abstracts of relevant studies, followed by a detailed full-text review and extraction of pertinent data. We identified and synthesised 34 articles. The review revealed that while ultrasound has been used to assess body composition and bone properties in children, significant gaps remain in the literature. These include limited studies on ultrasound performance, insufficient attention to relevant sample characteristics, reliance on manual image measurements, and limited sample diversity. Point-of-care ultrasound shows significant promise for assessing paediatric body composition and nutritional status. To validate and enhance its effectiveness, further research is needed. Future studies should include larger and more diverse patient cohorts and conduct longitudinal investigations to evaluate nutritional interventions. Additionally, developing artificial intelligence (AI) for standardising and automating data interpretation will be crucial in improving the accuracy and efficiency of ultrasound assessments.

High-Precision Measurement of Microscales Based on Optoelectronics and Image Integration Method.

Currently, there are various types of microscales and the conventional line detection system usually has only one detection method, which is difficult to adapt to the diverse calibration needs of microscales. This article investigates the high-precision measurement method of a microscale based on optoelectronics and the image integration method to solve the diversified calibration needs of microscales. The automatic measurement and processing system integrates two methods: the photoelectric signal measurement method and the photoelectric image measurement method. This article studies the smooth motion method based on ordinary linear guides, investigates the method of reducing the cosine error of a small-range interference length measurement, proposes an image-based line positioning method, and studies the edge and center recognition algorithms of the line. According to the experimental data, the system's measurement accuracy was analyzed using the photoelectric signal measurement method to measure the 1 mm microscale, the maximum difference from the reference value was 0.105 μm, the standard uncertainty was 0.068 μm, and the absolute value of normalized error was less than 1. The accuracy of the image measurement method to measure the 1 mm microscale was consistent with that of the photoelectric signal method. The results show good consistency in the measurement results between the two methods of the integrated measurement system. The photoelectric signal method has the technical characteristics of high measurement efficiency and high accuracy, while the pixel-based measurement of the image method has two-dimensional measurement characteristics, which can realize measurements that cannot be realized by the photoelectric signal method; therefore, the measurement system of optoelectronics and image integration is characterized by high precision and a wide range of measurement adaptations.

Image measurements of body size and behaviour of cultured fish

Image measurements of body size and behaviour of cultured fish

Development of a true density-based automated quality grading device for unboiled arecanut kernels

The automated sorting of arecanut kernels is a significant challenge that has not been effectively addressed thus far. Scientific grading techniques are necessary given the paradigm change toward investigating alternative uses for arecanuts in industry and the medical field. This research work emphasizes the relatively unexplored aspect of the post-harvest process; quality grading of kernels based on physical properties. It aimed to develop a novel approach for classifying unboiled (Chali) arecanut kernels cultivated in Goa, India based on their true density, using a combination of mechanical and visual techniques. The study explored the potential of true density as a quality indicator for real-time grading of the kernels. To achieve this, automated grading equipment was devised, utilizing a load cell to measure the kernel's mass and the ellipsoid approximation method to estimate its volume. A machine vision system captured the top and side images of the kernels to measure their volume. Python programs were created to enable image acquisition, processing, object detection, measurement and kernel segregation. Real-time kernel classification was accomplished by establishing serial data communication between the Python code and the Arduino board. The kernel segregation process was facilitated by servomechanism and a stepper motor. The kernels were classified into acceptable and non-acceptable categories based on a threshold value of true density. The research successfully established a method that utilizes the physical attributes of arecanut kernels as parameters for quality grading. However, the study encountered challenges with the density measurements, as the paired t-test results revealed significant differences between the kernel true density measured by the device and the true density estimated using the weighing scale-water displacement method, indicating a percentage error of 13.2%. Addressing these challenges would lead to more accurate density calculations, thereby enhancing the overall effectiveness of the kernel classification process. Furthermore, the technique allowed for the offline estimation of the kernels' porosity, which was found to be 45.3%. In future research, the integration of density and porosity measurement systems could be explored for real-time quality evaluation based on porosity, offering potential opportunities for further enhancement and optimization of the grading process. The technology could be further applied to other types of nuts and agricultural products, thereby overcoming the limitations of color-based sorting using image processing. Key words: Quality grading, True density, Machine vision, Arecanut kernel, Python

Training Artificial Neural Networks to Detect Multiple Sclerosis Lesions Using Granulometric Data from Preprocessed Magnetic Resonance Images with Morphological Transformations

The symptoms of multiple sclerosis (MS) are determined by the location of demyelinating lesions in the white matter of the brain and spinal cord. Currently, magnetic resonance imaging (MRI) is the most common tool used for diagnosing MS, understanding the course of the disease, and analyzing the effects of treatments. However, undesirable components may appear during the generation of MRI scans, such as noise or intensity variations. Mathematical morphology (MM) is a powerful image analysis technique that helps to filter the image and extract relevant structures. Granulometry is an image measurement tool for measuring MM that determines the size distribution of objects in an image without explicitly segmenting each object. While several methods have been proposed for the automatic segmentation of MS lesions in MRI scans, in some cases, only simple data preprocessing, such as image resizing to standardize the input dimensions, has been performed before the algorithm training. Therefore, this paper proposes an MRI preprocessing algorithm capable of performing elementary morphological transformations in brain images of MS patients and healthy individuals in order to delete undesirable components and extract the relevant structures such as MS lesions. Also, the algorithm computes the granulometry in MRI scans to describe the size qualities of lesions. Using this algorithm, we trained two artificial neural networks (ANNs) to predict MS diagnoses. By computing the differences in granulometry measurements between an image with MS lesions and a reference image (without lesions), we determined the size characterization of the lesions. Then, the ANNs were evaluated with the validation set, and the performance results (test accuracy = 0.9753; cross-entropy loss = 0.0247) show that the proposed algorithm can support specialists in making decisions to diagnose MS and estimating the disease progress based on granulometry values.

Accuracy of Orthodontic Anchor Screw Placement Using a 3D-Printed Surgical Guide.

Background Although radiographs and computed tomography (CT) images are reviewed before temporary anchorage device (TAD) implantation, implantation of TADs exactly as planned is difficult. This study aimed to evaluate the accuracy of TAD implantation using an original surgical guide fabricated using cone-beam CT data and computer-aided design software. Methodology The studyparticipants included six experienced orthodontists who had implanted ≥20 TADs, and six inexperienced orthodontists who had never implanted a TAD. Maxillary dental typodont models with radiopaque tooth crowns and roots were used. A total of four TADs were implanted on the buccal sides: between the second bicuspid and first molars and between the first and second molars bilaterally. The accuracy of TAD implantation was examined in two groups: in 12 dental typodont models, TAD implantation was performed using a surgical guide (guide group), and in 12 dental typodont models, TAD implantation was performed without a surgical guide (freehand group). All dental typodont models implanted a total of 96 TADs.The TAD position was evaluated using the CT coordinate system and 3D image measurement software. Using the long axis of the TAD as a reference, the distance between the coronal and apical ends of the implanted TAD and those of the planned TAD, i.e., the ideal implantation position, was measured in both groups along the x, y, and z axes. The medians of the values were compared between the groups. Additionally, the presence of root contact was compared between the experienced and inexperienced orthodontists. Results On the x-axis, the linear deviations (median) of the coronal and apical ends of the TAD in the freehand group were 1.06 mm and 1.36 mm, respectively. In contrast, in the guide group, the deviations were 0.65 mm and 0.90 mm, respectively, and the difference was statistically significant (p = 0.002 and p = 0.005, respectively). On the y-axis, the deviations in the freehand group were 1.13 mm and 1.08 mm, respectively. In contrast, the deviations in the guide group were 0.71 mm and 0.79 mm, respectively, and only the coronal deviations were significantly different between the groups (p = 0.006). On the z-axis, the deviations in the freehand group were 1.44 mm and 1.86 mm, respectively. In contrast, the deviations in the guide group were 0.75 mm and 1.16 mm, respectively, and the difference was statistically significant (p = 0.006 and p = 0.002, respectively). Conclusions The use of a surgical guide allowed for more accurate TAD implantation. Additionally, TAD implantation using a guide prevented root damage.

Validation of Artificial Intelligence in the Classification of Adolescent Idiopathic Scoliosis and the Compairment to Clinical Manual Handling.

The accurate measurement of Cobb angles is crucial for the effective clinical management of patients with adolescent idiopathic scoliosis (AIS). The Lenke classification system plays a pivotal role in determining the appropriate fusion levels for treatment planning. However, the presence of interobserver variability and time-intensive procedures presents challenges for clinicians. The purpose of this study is to compare the measurement accuracy of our developed artificial intelligence measurement system for Cobb angles and Lenke classification in AIS patients with manual measurements to validate its feasibility. An artificial intelligence (AI) system measured the Cobb angle of AIS patients using convolutional neural networks, which identified the vertebral boundaries and sequences, recognized the upper and lower end vertebras, and estimated the Cobb angles of the proximal thoracic, main thoracic, and thoracolumbar/lumbar curves sequentially. Accordingly, the Lenke classifications of scoliosis were divided by oscillogram and defined by the AI system. Furthermore, a man-machine comparison (n = 300) was conducted for senior spine surgeons (n = 2), junior spine surgeons (n = 2), and the AI system for the image measurements of proximal thoracic (PT), main thoracic (MT), thoracolumbar/lumbar (TL/L), thoracic sagittal profile T5-T12, bending views PT, bending views MT, bending views TL/L, the Lenke classification system, the lumbar modifier, and sagittal thoracic alignment. In the AI system, the calculation time for each patient's data was 0.2 s, while the measurement time for each surgeon was 23.6 min. The AI system showed high accuracy in the recognition of the Lenke classification and had high reliability compared to senior doctors(ICC 0.962). The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.

Evaluating Friction Characteristics of High Friction Surface Treatment Application Under Varied Polishing and Slippery Conditions

The frictional attributes of high friction surface treatment (HFST) play a crucial role in ensuring optimal traffic safety, particularly in wet weather conditions. Friction consists of two important components: adhesion and hysteresis. This research focuses on evaluating these essential factors in HFST using two different aggregates and two distinct sizes by considering various abrasion and polishing methods. To isolate these components for assessment, testing was carried out under various slippery conditions, including dry, wet with water, and wet with water + soap. The inclusion of liquid hand soap in the test procedure effectively minimized or even eliminated the adhesion component’s influence, making it possible to primarily focus on the hysteresis component. Consequently, the British Pendulum Number (BPN) measured in this research predominantly reflected the hysteresis-related friction. The analysis of variance results emphasized the substantial impact of different abrasion and polishing methods on the BPN values obtained under various surface conditions. Notably, Micro-Deval Abrasion (MDA) with 105, 180, and 240 min abrasion exhibited the most pronounced influence on BPN variation and the higher F-value for the MDA 105 min indicated that this specific abrasion time exerted a more significant influence on the variation in BPN values than other factors. Furthermore, the utilization of the Aggregate Image Measurement System yielded valuable insights into the micro-texture of the aggregates. It revealed that the calcined bauxite HFST size is anticipated to provide the rougher surface morphology (texture) on a pavement surface compared with other sources in this study, thereby contributing to high pavement surface friction. The findings from this study contribute to a deeper understanding of the frictional characteristics of HFST under different scenarios, providing valuable insights for optimizing HFST applications to enhance road safety and skid resistance.