Dimensional Measurements Research Articles

Deep learning for fully automated echocardiographic measurements of left ventricular wall thickness and chamber dimensions in the parasternal long-axis view

Abstract Background Accurate quantification of left ventricular (LV) wall thickness and chamber dimensions in the echocardiographic parasternal long-axis view (PLAX) is crucial for clinical decisions in patients with heart disease. However, there is a need for more reproducible and time-efficient methods. Fully automated deep learning (DL)-based methods could address this need. Purpose To develop a DL-based method for fully automated measurements of LV wall thickness and chamber dimensions in PLAX, validate its performance in a large population cohort, and evaluate its capacity to differentiate between hypertensive and normotensive groups. Methods DL networks were trained on PLAX recordings from 207 subjects. The DL method performed cardiac segmentation, timing of end-systole and end-diastole, and measured LV wall thickness and chamber dimensions in PLAX. The model was validated in a large populational echocardiography dataset including a subset with test-retest evaluation. Agreement with expert reference measurements, test-retest reproducibility, and comparison of normotensive and hypertensive (defined as those on hypertensive medication or with systolic blood pressure >160 mmHg) groups were evaluated. Results In the populational study of 2047 subjects, the novel DL method demonstrated a feasibility of 97.6%. Strong agreement was observed between the automated DL method and expert readers in measuring end-diastolic septal thickness, posterior wall thickness, and LV diameter (bias 0.1 mm, 0.2 mm, and 2.2 mm, respectively, Figure 1). In a test-retest dataset (n=40, with 2 recordings per patient and 4 readers), the novel DL method displayed improved reproducibility compared to inter-reader measurements, with minimal detectable change for interventricular septal diameter 2.2 mm versus 2.6 mm, posterior wall diameter 1.2 mm versus 2.5 mm, and LV diameter 5.0 mm versus 6.3 mm (Figure 2). The DL method measured significantly wider septal diameter in the hypertensive group compared to normotensive group (p<0.001). Mean analysis time for wall thickness and chamber dimensions using the DL method was 1.2 seconds. Conclusion The novel DL method accurately measured LV wall thickness and chamber dimensions in the PLAX view, and demonstrated improved test-retest reproducibility compared to experienced echocardiographers. Additionally, it was able to detect significantly wider septal diameters in the hypertensive population. By providing fast and reliable measurements in retrospective data, and with potential for real-time measurements during image acquisition, this DL method has the potential to improve the yield, efficiency, and workflow in echocardiography.Bland-Altman posterior wall diameterTest-retest posterior wall diameter

Indicators of primary health care workforce equity for attaining health equity

Abstract Background Healthcare workforce crises are often the result of inequities faced by healthcare workers. This study aims to discuss the primary policy questions related to health equity regarding the primary healthcare (PHC) workforce indicators. Methods Following the purposive review of published literature on HWF equity in PHC services, we applied an intersectional analysis of the compendiums of HWF indicators (e.g., supply, flows, skill mix, working conditions, promotion, training, funding, governance, etc.) to strategically link them to PHC policy issues for health equity and categorized qualitatively under each policy pillar against HWF crises. Results The proposed indicators address the measurement of multiple dimensions of health workforce equity. These include financial and non-financial incentives to monitor worker attrition reduction, gender pay gap, fairness in employment arrangements, remuneration, and working conditions. By monitoring the minimum wage for financially vulnerable health workforce groups, we can identify the need for potential increases to support well-being, reduce turnover, and increase productivity. Equally important is monitoring equity in health workforce education, planning, and management, which is crucial for achieving population health equity. By implementing indicators of health workforce diversity to address rural and underrepresented communities, primary healthcare managers can effectively plan the reduction of their unmet needs and minimize avoidable referrals. Conclusions To ensure equitable access to primary healthcare, we must measure workforce equity and its impact on population health. Monitoring the indicators of equitable health workforce policies and evaluating the improvement in retention and recruitment processes can more effectively address workforce crises. Key messages • Health workforce crises often stem from healthcare workforce inequities. • Health workforce equitable policies require a clear set of indicators.

Aortic integral distensibility based on the auto-segmented dynamic motion of the aortic wall

Abstract Background Aortic stiffness is important in cardiovascular diseases and predicting outcomes. Echocardiography is a widely used noninvasive imaging modality for assessing aortic stiffness, but its accuracy can be limited by the low spatial resolution of ultrasound-based images and the subjectivity of manual measurements of aortic dimensions. Automated segmentation analysis can provide a more accurate and objective measure. Objective An automated algorithm and software have been developed for segmenting the aortic wall. A new measurement technique called "Aortic Integral Distensibility (AID)" has been formulated using this framework. This method overcomes limitations of traditional method focusing solely on the two positions of Aortic Distensibility (AD). Method In this particular investigation, 112 participants were subjected to echocardiography in order to quantify the dynamics of the aortic wall. An automated algorithm was devised to delineate the aortic wall by utilizing an M-mode echocardiography scan. The statistical analysis encompassed the measurement of AD and AID before and after the age of 65 group. This analysis was conducted considering various risk factors, including age, gender, weight, BMI, smoking, and blood pressure. Additionally, clinical parameters, specifically AIx @75 (Augmentation Index) and Framingham Risk Score (FRS), were evaluated as well. Inter-rater reliability was conducted between the traditional AD and the newly implemented AID. Results The results indicate that the group under 65 had more distensibility than the group over 65, and AID measurements were slightly higher than AD measurements. SBP showed a strong correlation with AID, rather than AD. AIx @75 and FRS had a negative relationship with distensibility. In the 65 years or older group, there was a significant negative correlation observed between AIx @75 and AID (r = -0.4, p = 0.012). Furthermore, a strong correlation was also observed between FRS and AID (r = -0.53, p = 0.002). Bland-Altman analysis demonstrated that the automated methodology had better reproducibility and less variability in distensibility measurements than the manual methodology. Conclusion The study suggests that AID could be valuable for assessing aortic stiffness. AID could help clinicians diagnose and manage cardiovascular diseases more accurately. The automatic approach provides reliable measurements for aortic distensibility and strain, which has clinical implications.Schematic Chart of the Study Process

Feasibility, accuracy, and reproducibility of device sizing in virtual reality for percutaneous left atrial appendage closure - a single center study

Abstract Background Left atrial appendage closure (LAAC) is a safe and effective procedure for stroke prevention in patient with non-valvular atrial fibrillation. Procedural planning and device sizing remains challenging due to the highly complex and variable three-dimensional (3D) anatomy of the left atrial appendage (LAA). Virtual reality (VR) is a innovative, recent technology allowing a superior spatial orientation visualization of 3D imaging datasets. Purpose Therefore, we aimed to evaluate feasibility and accuracy of LAA measurements in VR for device sizing in patients planned for LAAC. Methods Twenty-one patients (79±7 years, 62% male) with non-valvular atrial fibrillation scheduled for LAAC were randomly included in our study. Preprocedural a multi-slice computed tomography (MSCT) was performed for device sizing in all patients. MSCT-based 3D models were established and visualized in VR using a dedicated software (VMersive, Warsaw, Poland). LAA dimensions (Ostium, landing zone (LZ), and depth) were assessed directly in the virtual environment. Conventional measurements of LAA ostium, landing zone (LZ) and depth using commercially available software were compared to measurements in VR. Results 3D reconstruction of MSCT datasets and visualization in VR were successfully performed automatically in all patients within a few seconds. MSCT and VR demonstrated good correlations for ostium minimum (r = 0.93), maximum (r = 0.8) and mean (r = 0.88, all p<0.001) diameters. Additionally, LZ minimum (r=0.84), maximum (r=0.86) and mean (r=0.9) diameters showed good correlation (all p<0.001). Moderate correlation was observed for depth measurements (r=0.76, p<0.001). Furthermore, intraclass correlation coefficients ranging from 0.88 to 0.98 denoted high inter- and intraobserver agreement. Conclusion Immersive VR allows highly feasible, accurate and reproducible measurements of the LAA dimensions directly in the virtual environment, hence enabling accurate device sizing for LAAC.

Quantifying phenotypic variability of indigenous chickens using morphometric traits by applying multivariate analysis: Input for sustainable rural chicken farming

Despite environmentally influenced performance traits in farm animals, variations in morphometric characters reflect unswerving genetic effects. In this study, body weight and body dimension measurements of indigenous chickens reared in four rural communities were investigated using multivariate statistical procedures to elucidate the existence of phenotypic variability between chicken populations. The study districts were selected based on higher indigenous chicken population, geographic distance, and agro-ecological distinctiveness. Eleven morphometric traits were measured on 1060 adult chickens from the districts. The results revealed that there were significant (p < 0.0001) variations between populations across districts. Significantly (P < 0.0001) higher mean values for morphometric traits were obtained in chickens reared in the Hula (highland district) than in those of the three districts. Likewise, multivariate discriminant analysis showed that most of the variables had significant power to differentiate the chicken populations into separate groups. Accordingly, three discriminant functions were extracted, of which the first two explained 95.6 % of the total variances in the populations. Moreover, in cluster analysis, chickens were differentiated into two sub-groups: Hula as a separate population, and Shebedino, Aleta Chuko and Boricha populations clustered together. Furthermore, the discriminant analysis correctly assigned 56.7 %, 55.1 %, 51.5 %, and 53.3 % to their source populations of Hula, Aleta Chuko, Shebedino, Hula, and Boricha, respectively, showing high mixing and mobility of chickens across neighboring districts. Therefore, this study confirmed the existence of significant morphometric variability between the studied populations, which could be used as a valuable source of information for selective breeding and sustainable use of indigenous chickens, particularly for rural farmers who almost rely on phenotypic features to select breeding stocks. In addition, the higher morphometric measurements shown for Hula chickens should be confirmed by on-station evaluation, and further molecular tools should be applied to validate the genetic distinctiveness of the studied chicken populations.

Accurate and efficient dimensional measurement technique based on point cloud models and its application in the automotive industry

Accurate and efficient dimensional measurement technique based on point cloud models and its application in the automotive industry

Evaluating small coronary stents with dual-source photon-counting computed tomography: effect of different scan modes on image quality and performance in a phantom.

The study aimed to assess the feasibility and image quality of dual-source photon-counting detector computed tomography (PCD-CT) in evaluating small-sized coronary artery stents with respect to different acquisition modes in a phantom model. Utilizing a phantom setup mimicking the average patient's water-equivalent diameter, we examined six distinct coronary stents inflated in a silicon tube, with stent sizes ranging from 2.0 to 3.5 mm, applying four different CT acquisition modes of a dual-source PCD-CT scanner: "high-pitch," "sequential," "spiral" (each with collimation of 144 × 0.4 mm and full spectral information), and "ultra-high-resolution (UHR)" (collimation of 120 × 0.2 mm and no spectral information). Image quality and diagnostic confidence were assessed using subjective measures, including a 4-point visual grading scale (4 = excellent; 1 = non-diagnostic) utilized by two independent radiologists, and objective measures, including the full width at half maximum (FWHM). A total of 24 scans were acquired, and all were included in the analysis. Among all CT acquisition modes, the highest image quality was obtained for the UHR mode [median score: 4 (interquartile range (IQR): 3.67-4.00)] (P = 0.0015, with 37.5% rated as "excellent"), followed by the sequential mode [median score: 3.5 (IQR: 2.84-4.00)], P = 0.0326 and the spiral mode [median score: 3.0 (IQR: 2.53-3.47), P > 0.05]. The lowest image quality was observed for the high-pitch mode [median score: 2 (IQR: 1- 3), P = 0.028]. Similarly, diagnostic confidence for evaluating stent patency was highest for UHR and lowest for high-pitch (P < 0.001, respectively). Measurement of stent dimensions was accurate for all acquisition modes, with the UHR mode showing highest robustness (FWHM for sequential: 0.926 ± 0.061 vs. high-pitch: 0.990 ± 0.083 vs. spiral: 0.962 ± 0.085 vs. UHR: 0.941 ± 0.036, P = non-significant, respectively). Assessing small-sized coronary stents using PCD-CT technology is feasible. The UHR mode offers superior image quality and diagnostic confidence, while all modes show consistent and accurate measurements. These findings highlight the potential of PCD-CT technology, particularly the UHR mode, to enhance non-invasive coronary stent evaluation. Confirmatory research is necessary to influence the guidelines, which recommend cardiac CT only for stents of 3 mm or larger.

Automatic damage detection and segmentation using deep learning algorithms in reinforced concrete structure inspections

AbstractTraditional methods of detecting concrete damage, such as manual inspection, are typically slow, labor‐intensive, and subjective. Integrating deep learning algorithms has automated this process, representing a significant advance in building damage inspection. Semantic segmentation, a technique within deep learning, is increasingly recognized for its capability to identify the location and shape of concrete damage accurately. Unlike basic deep learning approaches like classification and object detection, semantic segmentation not only recognizes damage but also delineates its boundaries, and has great potential in facilitating dimension measurement. However, multi‐level, high‐precision detection for various structural damage assessments remains an area requiring further research. This study created a database of reinforced concrete surfaces with pixel‐level, multi‐category semantic segmentation annotations for various levels of component damage, including cracks and spalling of concrete, exposure, buckling, fracture of reinforcing bars, etc. The performance of advanced deep learning segmentation algorithms, including U‐Net, DeepLab, K‐Net, and FastSCNN, was consequently trained and evaluated in detecting various types of concrete damage. All models show good accuracy of more than 98%, but lower F1 scores around 70%. U‐Net and K‐Net demonstrate relatively stable performance, indicating a certain degree of consistency and a high‐performance peak. In contrast, DeepLabv3's F1 score fluctuates significantly, suggesting the model may suffer from overfitting or other stability issues during training, resulting in a relatively low average performance. FastSCNN exhibits the highest potential, achieving the highest F1 score. In addition, this study also tested the performance of each model on new damage images and explored the impact of the dataset proportion ratio (training set vs. validation set), contributing to providing insights into each algorithm's suitability for various inspection scenarios. Finally, perspectives on current challenges and future directions in the field have also been given.

Interior Profile Accuracy Assessment Method of Deep-Hole Parts Based on Servo Drive System.

Dimensional and profile measurements of deep-hole parts are key processes both in manufacturing and product lifecycle management. Due to the particularity of the space conditions of deep-hole parts, the existing measurement instruments and methods exhibit some limitations. Based on the multi-axis, highly precise servo drive system, a novel measuring device is developed. The laser displacement sensors are fed by the flux-switching permanent magnet linear motor, and the part is rotated by the servo motor. On this basis, the assessment methods of roundness, straightness, and cylindricity are proposed by employing the least square method (LSM). Additionally, considering the axial center deviation between the sensors and the part, the rotating center coordinate is optimized by the gradient descent algorithm (GDM). Then, the measurement system is constructed and the experiment study is conducted. The results indicate favorable evaluation error of the LSM fitting and GDM iteration. Compared with the coordinate measuring machine (CMM), the measured results show good consistency. In the error analysis, the angle positioning error of measured point is less than 0.01°, and the axial positioning error is less than 0.05 mm. The proposed system and assessment method are regarded as a feasible and promising solution for deep-hole part measurements.

Macrophage hyperfunction as a cellular trigger for fibroplastic changes in internal organs in pneumoconiosis among miners

Introduction. The significant prevalence of anthracosilicosis and silicosis, characterized by the rapid development of pneumofibrosis in coal mine workers, is a serious socially significant problem worldwide. A key component of the pathogenesis of pneumoconiosis is the macrophage reaction to fibrogenic dust, which is a complex immunological response, with prolonged and intense exposure leading to chronic inflammation in various organs and systems, fibrosis and a progressive decrease in their functions. A deep understanding and evaluation of the macrophage reaction as a potential biomarker for the diagnosis of lung damage in miners will allow us to develop more effective strategies to combat dust damage to organs and systems. The study aims to consider macrophage hyperfunction as a trigger mechanism for fibroplastic changes in internal organs in pneumoconiosis in miners. Materials and methods. Using light microscopy, specialists conducted extended cytological, histological and immunohistochemical studies of bronchial smears and autopsy material (fragments of bronchi, lungs, heart, liver and kidneys) of 50 miners of Kuzbass coal enterprises who died simultaneously as a result of a man-made disaster. Morphometric analysis of histological structures with measurement of linear dimensions was used for a comprehensive assessment of pathomorphological changes. Results. There was a sharp increase in the number and size of actively phagocytic alveolar macrophages loaded with dust particles in the lungs of miners. Pathological changes in internal organs were characterized by pronounced fibrosis in the peribronchial and perivascular zones, stroma with deposition of dust particles and a pronounced macrophage reaction. In a group of miners with more than 15 years of experience in the central and distal parts of the lungs, in the intermuscular zones of the myocardium, the portal tracts of the liver, and the central segments of the kidneys, there was a constant increase in the expression of immunohistochemical markers CD14, CD34, actin and vimentin, which can be used as diagnostic markers for the identification of macrophage inflammation and fibrosis, will allow to assess the degree of severity of the pathological process. A characteristic feature of the miners' long experience of dust exposure was the presence of progressive pronounced perivascular sclerosis extending beyond the vascular histione. Experts found fibroblastic cells with positive expression of CD14, CD31 and CD34 in the sclerotic zones. Limitation. The absence of cytogenetic and electron microscopic examination, which is expected in the future in conjunction with the expansion of the range of immunohistochemical research methods. Conclusion. The results of extended pathomorphological and cytological studies emphasize the importance of an integrated approach to the diagnosis of occupational diseases in the coal industry. The use of a combination of these methods will allow timely detection of macrophage inflammation and fibrous process in dust pathology, specific changes at the cellular and tissue levels, as well as to assess the severity of the pathological process. Ethics. The study of autopsy material was based on a secondary examination of blocks and ready-made histological micro-preparations (glasses) of the material of the Bureau of Forensic Medical examination of Novokuznetsk, Osinniki, Prokopyevsk. The studies of pathomorphological material were carried out strictly on the basis of Federal Law No. 323-FZ dated 11/21/2011 "On the Basics of Protecting the Health of Citizens in the Russian Federation", in particular Article 67 "Conducting pathological anatomical autopsies", Federal Law No. 8-FZ dated 12.01.1996 "On Burial and Funeral Business" (Article 5, pp. 1, 2), as well as in accordance with the Order of the Ministry of Health of the Russian Federation dated 06.06.2013 No. 354n "On the procedure for conducting pathological anatomical autopsies", Order of the Ministry of Health of the Russian Federation dated 03/24/2016 No. 179n "On the Rules for conducting pathological anatomical studies".

Three-Dimensional Printing Limitations of Polymers Reinforced with Continuous Stainless Steel Fibres and Curvature Stiffness

This study investigates the printability limitations of 3D-printed continuous 316L stainless steel fibre-reinforced polymer composites obtained using the Materials Extrusion (MEX) technique. The objective was to better understand the geometric printing limitations of composites fabricated using continuous steel fibres, based on a combination of bending stiffness testing and piezoresistive property studies. The 0.5 mm composite filaments used in this study were obtained by co-extruding polylactic acid (PLA), with a 316 L stainless steel fibre (SSF) bundle. The composite printability limitations were evaluated by the printing of a series of ’teardrop’ shaped geometries with angles in the range from 5° to 90° and radii between 2 and 20 mm. The morphology and dimensional measurements of the resulting PLA-SSF prints were evaluated using μCT scanning, optical microscopy, and calliper measurements. Sample sets were compared and statistically examined to evaluate the repeatability, turning ability, and geometrical print limitations, along with dimensional fluctuations between designed and as-printed structures. Comparisons of the curvature bending stiffness were made with the PLA-only polymer and with 3D-printed nylon-reinforced short and long carbon fibre composites. It was demonstrated that the stainless steel composites exhibited an increase in bending stiffness at smaller radii. The change in piezoresistance response of the PLA-SSF with load applied during the curvature bending stiffness testing demonstrated that the 3D-printed composites may have the potential for use as structural health monitoring sensors.

Dimensional change and springback of spherical shell in cryogenic forming

Cryogenic forming has been developed to manufacture thin-walled curved aluminum alloy components, whose final dimensions are affected by cryogenic shrinkage and springback. Therefore, dimensional changes of spherical shell in cryogenic forming were studied theoretically and experimentally. The cryogenic forming process was discussed to elucidate the factors affecting the dimensional change. The stress distribution was analyzed to qualitatively reveal the springback behavior. Cryogenic dimensional measurement devices were built to quantitatively evaluate the dimensional changes in the forming processes of punch cooling, springback, and specimen restoration to room temperature. The temperature dependencies of the elastic modulus and expansion coefficient were modeled to quantitatively calculate the effect of thermal expansion and contraction on the dimensions of specimen and punch. The theoretical analysis results indicate that depth reduction and opening expansion are produced by cryogenic springback, determined by the radial stress, hoop stress, and bending moment in different deformation regions. The cryogenic springback in the biaxial tensile stress zone was reduced by 37.8 % owing to the increasing radial deformation and decreasing bending deformation. In contrast, cryogenic springback in the tensile-compressive stress zone increased by 30.8 %. The punch cooling shrinkage and specimen warming expansion in depth direction can reduce for the dimensional deviation caused by springback but cause the opposite effect in hoop direction. Expansion and shrinkage were effectively predicted using the proposed model, with an error of less than 16 %. The deformation region of the biaxial tensile stress can be enlarged by the significantly improved hardening ability at cryogenic temperature, which benefits enhancing deformation uniformity and further reduces springback. Therefore, cryogenic forming offers considerable potential for the precision manufacturing of aluminum alloy deep-cavity thin-walled components.

Tight conic approximation of testing regions for quantum statistical models and measurements

Quantum statistical models (i.e., families of normalized density matrices) and quantum measurements (i.e., positive operator-valued measures) can be regarded as linear maps: the former, mapping the space of effects to the space of probability distributions; the latter, mapping the space of states to the space of probability distributions. The images of such linear maps are called the testing regions of the corresponding model or measurement. Testing regions are notoriously impractical to treat analytically in the quantum case. Our first result is to provide an implicit outer approximation of the testing region of any given quantum statistical model or measurement in any finite dimension: namely, a region in probability space that contains the desired image, but is defined implicitly, using a formula that depends only on the given model or measurement. The outer approximation that we construct is minimal among all such outer approximations, and close, in the sense that it becomes the maximal inner approximation up to a constant scaling factor. Finally, we apply our approximation to provide sufficient conditions, that can be tested in a semi-device-independent way, for the ability to transform one quantum statistical model or measurement into another.

Model-based dimensional NDE from few X-ray radiographs: Application to the evaluation of wall thickness in metallic turbine blades

The extraction of 3D dimensional measurements based on a limited number of 2D X-ray radiographs of a part would offer a significant speed-up of quality control procedures in industry. However, there are challenges with respect to both measurements and uncertainties. This work addresses these challenges by creating an estimated numerical model of the imaged part on which dimensional measurements can be made. The numerical model is chosen as a parametric deformable model that encodes the expected shape variability of the parts resulting from the manufacturing process. The parameters and uncertainties of the numerical model of the imaged part are estimated by the registration of the computed projections of the model and the observed radiographs without the need of any segmentation. The registration requires the model, the initial parameters, and the observed radiographs. The proposed approach is applied to the inspection of turbine blades manufactured by investment casting, and in particular to the measurement of their wall thickness, which is a critical control. The deformable model consists in partitioning the inner ceramic core into multiple subparts, which may undergo a rigid body motion with respect to the master die. Wall thickness measurements are determined from the estimation of these rigid body motions. To assess the reliability of the proposed procedure, a repeatability study is performed. In addition, wall thickness measurements were compared to corresponding measurements from the surface of the metal boundary obtained by X-ray computed tomography. This surface was determined from a reconstructed tomogram using commercial software. Both analyses show that such measurements are reliable and efficient. Furthermore, residual differences between captured and computed projections reveal localized shape deviations from the CAD model, meaning that despite localized model errors, the approach is operable.

Prediction of interface morphology formed by the oblique gas jet impinging on a liquid surface

This study presents a theoretical analysis of the oblique gas jet impingement process on a liquid surface, elucidating the evolution of the flow field distribution and the influence of jet parameters on cavity shape dynamics. By integrating surface tension effects, the existing Blanks and Chandrasekhara model was refined to develop an advanced predictive model for cavity morphology. The theoretical framework was substantiated through numerical simulations and corroborated with experimental measurements of cavity dimensions, captured using state-of-the-art machine vision technology. The findings reveal a consistent trend in cavity dimension variations: an increase in the cavity surface width with the elevation of the impinging angle from the vertical and an escalation in gas jet velocity. Conversely, a reduction in the impinging angle coupled with an increase in jet velocity leads to a deeper cavity. To enhance the predictive accuracy, the model underwent iterative optimization, incorporating experimental data and accounting for jet parameters. The refined model demonstrated achieved a maximum error of 0.135 mm and a minimum error of 0.03 mm, providing reliable forecasts of cavity depth, which is pivotal for applications in fluid dynamics and related engineering fields.

Revealing in vivo broiler chicken growth state: Integrating CT imaging and deep learning for non-invasive reproductive phenotypic measurement

The measurement of body dimensions in broiler chickens has long been of great interest to the poultry industry, especially regarding reproductive phenotypes closely linked to breeding traits and the growth status of roosters. Existing measurement methods suffer from subjectivity, destructiveness, and various limitations. Therefore, we propose a novel method for the in vivo measurement of broiler chicken reproductive phenotype based on a three-dimensional medical imaging segmentation network. Leveraging our proposed FDTR medical imaging segmentation algorithm, this method achieves robust segmentation performance on a broiler chicken dataset. However, due to the morphological diversity and extreme class imbalance of broiler chicken reproductive phenotypes, we introduce a meta-learning paradigm and design a specialized decoder path. Ultimately, we establish relationships between the predicted results obtained from the segmentation network and the reproductive phenotypes to achieve phenotypic measurement. To evaluate this method, we collected and constructed the YF2023 broiler chicken reproductive phenotype dataset. Extensive cross-validation experiments demonstrate that the proposed method effectively measures the reproductive phenotypes of live roosters using computed tomography (CT) medical imaging. The proposed segmentation model achieves a mean fold dice similarity coefficient (DSC) of 0.6353, a mean fold Hausdorff distance (HD) of 11.84 mm, and a mean fold average volume distance (AVD) of 1.72 mm3. In addition, we evaluated our model’s generalization performance on unseen data, where the model achieved a DSC of 0.6825, an HD of 7.58 mm, and an AVD of 1.40 mm3. The predicted results exhibit a mean correlation coefficient of 66.18% with the weights of reproductive phenotype. This validates the enormous potential of the method in broiler chicken breeding and provides new insights and solutions for the accurate segmentation of broiler chicken reproductive phenotype. The code is available at https://github.com/Github-XKou/MetaFDTR.

Adaptation of the normative rating procedure for the International Affective Picture System to a remote format

BackgroundThe Self-Assessment Manikin (SAM), a pictorial scale for the measurement of pleasure and arousal dimensions of emotions, is one of the most applied tools in the emotion research field.ObjectiveWe present a detailed description of a remote method to collect affective ratings in response to pictures by using the SAM scale.MethodsTo empirically validate our remote method, we conducted a study using a digitized version of the SAM scale and delivered online didactic instructions that followed the normative rating procedure for the International Affective Picture System (IAPS) to the participants. We presented 70 pictures from the IAPS and an additional set of 22 food pictures to the participants.ResultsWe found strong correlations between the ratings of IAPS pictures obtained in our sample and those reported by North American and Brazilian participants in previous in-person studies that applied the same pictures and methodology. We were also able to obtain an additional standardized set of food pictures.ConclusionThe protocols described here may be useful for researchers interested in collecting remotely valid and reliable affecting ratings.

Assessment of Interrater Reliability and Accuracy of Cerebral Aneurysm Morphometry Using 3D Virtual Reality, 2D Digital Subtraction Angiography, and 3D Reconstruction: A Randomized Comparative Study.

Detailed morphometric analysis of an aneurysm and the related vascular bifurcation are critical factors when determining rupture risk and planning treatment for unruptured intracranial aneurysms (UIAs). The standard visualization of digital subtraction angiography (DSA) and its 3D reconstruction on a 2D monitor provide precise measurements but are subject to variability based on the rater. Visualization using virtual (VR) and augmented reality platforms can overcome those limitations. It is, however, unclear whether accurate measurements of the aneurysm and adjacent arterial branches can be obtained on VR models. This study aimed to assess interrater reliability and compare measurements between 3D VR, standard 2D DSA, and 3D DSA reconstructions, evaluating the reliability and accuracy of 3D VR as a measurement tool. A pool of five neurosurgeons performed three individual analyses on each of the ten UIA cases, measuring them in completely immersed 3D VR and the standard on-screen format (2D DSA and 3D reconstruction). This resulted in three independent measurements per modality for each case. Interrater reliability of measurements and morphology characterization, comparative differences, measurement duration, and VR user experience were assessed. Interrater reliability for 3D VR measurements was significantly higher than for 3D DSA measurements (3D VR mean intraclass correlation coefficient [ICC]: 0.69 ± 0.22 vs. 3D DSA mean ICC: 0.36 ± 0.37, p = 0.042). No significant difference was observed between 3D VR and 2D DSA (3D VR mean ICC: 0.69 ± 0.22 vs. 2D DSA mean ICC: 0.43 ± 0.31, p = 0.12). A linear mixed-effects model showed no effect of 3D VR and 3D DSA (95% CI = -0.26-0.28, p = 0.96) or 3D VR and 2D DSA (95% CI = -0.02-0.53, p = 0.066) on absolute measurements of the aneurysm in the anteroposterior, mediolateral, and craniocaudal dimensions. 3D VR technology allows for reproducible, accurate, and reliable measurements comparable to measurements performed on a 2D screen. It may also potentially improve precision for measurements of non-planar aneurysm dimensions.

Detection of moisture and size of winter melon seeds based on hyperspectral imaging and convex polygon size measurement

The level of moisture content and size of winter melon seeds affect their storage, germination and growth processes. Moderate moisture and size are essential for seed germination and growth. Therefore, detecting the moisture and size of winter melon seeds is beneficial to improve their germination rate. Traditional seed moisture testing methods are complex to operate and require destructive sample preparation or chemical treatment. Common dimensional measurement methods are also time-consuming and laborious. Hyperspectral imaging technology can acquire information about the surface and internal structure of the target separately, it can be used to quickly and non-destructively detect the moisture and size of winter melon seeds. In this study, partial least squares regression (PLSR) and partial least squares support vector machine (LSSVM) models were established to predict the moisture content of winter melon seeds by using reflection and transmittance spectral data. The models were optimized using five variable selection methods. The optimal performance of the LSSVM model based on the CARS algorithm was achieved in both single reflection and transmission spectra. The RP2 and RMSEP of the model based on the reflection spectra were 0.9667 % and 0.0215 %, respectively. The RP2 and RMSEP of the model based on the transmission spectra were 0.9644 % and 0.0223 %, respectively. In low-level data fusion, the LSSVM model based on the CARS algorithm also achieved optimal performance, but with only a little improvement compared to a single model (reflection spectra or transmission spectra), with RP2 and RMSEP of 0.9679 % and 0.0212 %, respectively. In the mid-level data fusion, the LSSVM model also based on the CARS algorithm achieved the optimal performance, and the performance of the model was further improved. The RP2 and RMSEP of the model were 0.9738 % and 0.0191 %, respectively. Finally, the image processing algorithm and the convex polygon size measurement method was proposed to measure the size of winter melon seeds. The absolute error between the calibrated winter melon seed length and width and the true length and width was less than 0.22 mm, and the relative error was less than 2 %. The results show that hyperspectral imaging technology can accurately detect the water content of winter melon seeds. Data fusion method and LSSVM model based on CARS algorithm can detect the water content of winter melon seeds more accurately. The image processing algorithm combined with the convex polygon size measurement method can be effectively used to detect the size of winter melon seeds with high precision.

Ultrasonographic estimation of kidney size in adult Bangladeshi population without known renal disease: a preliminary report

Background: The kidney size of a patient is a valuable diagnostic parameter in clinical practice. Renal size varies with age, gender, body mass index (BMI), pregnancy, race and co-morbid conditions like diabetes mellitus (DM) and hypertension (HTN). Measurements of renal dimensions can be carried out by using different modern techniques like ultrasonography (USG), computed tomography (CT) scan and magnetic resonance imaging (MRI). Out of these modalities USG is choice because of its simplicity, inexpensive, easily available, non-invasive and easy reproducibility. There is no established nomogram for renal size in the Bangladeshi population. Aim of the study was to determine the ultrasonographic renal size in adult Bangladeshi population with no known renal disease and to correlate renal size with age, gender and BMI. In addition, we assess whether hypertension and diabetes mellitus affect renal size. Methods: This cross-sectional study was done in BIRDEM General Hospital and included 55 patients with age &gt;18 years over 6 month period. Study population were not known to have renal disease documented by urine routine microscopy, spot urine for albumin creatinine ratio (ACR), serum creatinine and USG. Their body weight, height and BMI were calculated along with duration of DM and HTN. Analysis was done for differences due to age, gender and laterality. The correlation of renal dimensions with anthropometric parameters were also done. Results: A total of 55 patients were analyzed. There were 19 (34.5%) male and 36 (65.5%) female. The mean age was 46±.10 years. There mean weight, height and BMI were 61 ± 11 kg, 1.54±.069 meter, 25.7 ± 4.8 kg/m2 respectively. In total population 34.5% (19) were diabetic and 32.5% (18) were hypertensive. The means of length, width and parenchymal thickness (PT) of right kidneys (RK) were 10.1 ± 0.87, 4.2 ± 0.67 and 1.4± 0.32 cm respectively. The means of length, width and PT of left kidneys (LK) were 10.3 ± 0.89, 4.5 ± 0.49 and 1.5 ± 0.32 cm respectively. Measurements were significantly higher in LK than those of RK respectively (p=.003, p&lt;.001 and p=.041). There were significant differences in kidney length and PT between sex and age. There were also significant differences in kidney length and PT between subjects of higher BMI, long duration of DM and HTN. The correlation between renal length and weight showed a positive correlation (r = 0.16, p &lt; 0.023). Conclusion: The variation of parameters of both kidneys varies on gender, age, obesity, comorbidity like DM &amp; HTN and with their duration. Length of both kidneys depends on body weight. Further study needed with large samples. BIRDEM Med J 2024; 14(3): 151-157