Comparable Accuracy Research Articles

Assessing the effectiveness of measurement scales in evaluating the health-related quality of life in rare disease patients after treatment: a systematic review

BackgroundRare diseases often entail significant challenges in clinical management and health-related quality of life (HRQoL) assessment. HRQoL assessment tools for rare diseases show substantial variability in outcomes, influenced by disease heterogeneity, intervention types, and scale characteristics. The variability in reported quality of life (QoL) improvements following interventions reflects a need to evaluate the effectiveness of HRQoL assessment tools and understand their suitability across diverse contexts.ObjectiveThis systematic review aims to analyse the effectiveness of various assessment scales in evaluating QoL and explores the general trends observed in the studies using the same and different assessment scales on rare diseases.MethodsA comprehensive literature search was conducted across various databases to identify studies that reported QoL outcomes related to interventions for rare diseases. Search terms included various synonyms, and both the generic and specific terms related to rare diseases and QoL. Key variables, including intervention types, patient demographics, study design, and geographical factors, were analysed to determine their role in influencing the reported HRQoL outcomes. The findings were then compared with existing literature to identify consistent patterns and discrepancies.ResultsA total of 39 studies were included, comprising randomised controlled trials, observational studies, and cohort studies, with 4737 participants. Significant variations were observed in QoL improvements across studies, even when using the same assessment scales. These differences were primarily attributed to the heterogeneity in disease severity, intervention types, and patient characteristics. Studies employing disease-specific scales reported more nuanced outcomes than generic ones. Additionally, methodological differences, including study design and intervention type, contributed to variations in results and geographical factors influencing patients’ perceptions of health and well-being.ConclusionThe reported differences in QoL outcomes across studies can be explained by a combination of factors, including disease heterogeneity, treatment modalities, patient demographics, and assessment scale characteristics. These findings underscore the importance of selecting appropriate HRQoL assessment tools based on the research context and patient population. For more accurate comparisons across studies, it is crucial to consider these factors alongside consistent methodology and cultural adaptability of scales. Future research should focus on developing standardised guidelines for QoL assessments that accommodate the diverse needs of patients with rare diseases.

CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences

BackgroundTraditional supervised learning methods applied to DNA sequence taxonomic classification rely on the labor-intensive and time-consuming step of labelling the primary DNA sequences. Additionally, standard DNA classification/clustering methods involve time-intensive multiple sequence alignments, which impacts their applicability to large genomic datasets or distantly related organisms. These limitations indicate a need for robust, efficient, and scalable unsupervised DNA sequence clustering methods that do not depend on sequence labels or alignment.ResultsThis study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge, CGRclust is the first method to use unsupervised learning for image classification (herein applied to two-dimensional CGR images) for clustering datasets of DNA sequences. CGRclust overcomes the limitations of traditional sequence classification methods by leveraging unsupervised twin contrastive learning to detect distinctive sequence patterns, without requiring DNA sequence alignment or biological/taxonomic labels. CGRclust accurately clustered twenty-five diverse datasets, with sequence lengths ranging from 664 bp to 100 kbp, including mitochondrial genomes of fish, fungi, and protists, as well as viral whole genome assemblies and synthetic DNA sequences. Compared with three recent clustering methods for DNA sequences (DeLUCS, iDeLUCS, and MeShClust v3.0.), CGRclust is the only method that surpasses 81.70% accuracy across all four taxonomic levels tested for mitochondrial DNA genomes of fish. Moreover, CGRclust also consistently demonstrates superior performance across all the viral genomic datasets. The high clustering accuracy of CGRclust on these twenty-five datasets, which vary significantly in terms of sequence length, number of genomes, number of clusters, and level of taxonomy, demonstrates its robustness, scalability, and versatility.ConclusionCGRclust is a novel, scalable, alignment-free DNA sequence clustering method that uses CGR images of DNA sequences and CNNs for twin contrastive clustering of unlabelled primary DNA sequences, achieving superior or comparable accuracy and performance over current approaches. CGRclust demonstrated enhanced reliability, by consistently achieving over 80% accuracy in more than 90% of the datasets analyzed. In particular, CGRclust performed especially well in clustering viral DNA datasets, where it consistently outperformed all competing methods.

Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury

BackgroundNonsuicidal self-injury is a common health problem in adolescents and associated with future suicidal behavior. Predicting who will benefit from treatment is an urgent and a critical first step towards personalized treatment approaches. Machine-learning algorithms have been proposed as techniques that might outperform clinicians’ judgment. The aim of this study was to explore clinician predictions of which adolescents would abstain from nonsuicidal self-injury after treatment as well as how these predictions match machine-learning algorithm predictions.MethodsData from a recent trial evaluating an internet-delivered emotion regulation therapy for adolescents with nonsuicidal self-injury was used. Clinician predictions of which patients would abstain from nonsuicidal self-injury (measured using the youth version of Deliberate Self-harm Inventory) were compared to a random forest model trained on the same available data from baseline assessments.ResultsBoth clinician (accuracy = 0.63) and model-based (accuracy = 0.67) predictions achieved significantly better accuracy than a model that classified all patients as reaching NSSI remission (accuracy = 0.49 [95% CI 0.41 to 0.58]), however there was no statistically significant difference between them. Adding clinician predictions to the random forest model did not improve accuracy. Emotion dysregulation was identified as the most important predictor of nonsuicidal self-injury absence.ConclusionsPreliminary findings indicate comparable prediction accuracy between clinicians and a machine-learning algorithm in the psychological treatment of nonsuicidal self-injury in youth. As both prediction approaches achieved modest accuracy, the current results indicate the need for further research to enhance the predictive power of machine-learning algorithms. Machine learning model indicated that emotion dysregulation may be of importance in treatment planning, information that was not available from clinician predictions.Trial RegistrationNCT03353961||https://www.clinicaltrials.gov/, registered 2017–11-21.Preregistration at Open Science Framework: https://osf.io/vym96/.

THE STUDY OF ACCURACY OF AN OPERATOR’S PERCEPTION OF GEOMETRICAL OBJECT SIZES AND SHAPES IN THE VIRTUAL ENVIRONMENTS

This paper is devoted to the experimental comparison of accuracy of an operator’s perception of geometrical object sizes and shapes between the different conditions of information perception in the virtual environments and from the electronic displays. The experiments were conducted using a psychophysiological test for the accuracy of perceiving geometrical object sizes and shapes by an operator in the virtual environments and in the conditions of information perception from an electronic display. As a common metric of the accuracy of perceiving geometrical object sizes and shapes, an operator was offered to visually determine the object center of gravity. No significant differences in the measurement results of both the accuracy of perceiving geometrical object sizes and shapes and speed of this process were found based on the different methods of displaying the visual information to an operator.

An all integer-based spiking neural network with dynamic threshold adaptation

Spiking Neural Networks (SNNs) are typically regards as the third generation of neural networks due to their inherent event-driven computing capabilities and remarkable energy efficiency. However, training an SNN that possesses fast inference speed and comparable accuracy to modern artificial neural networks (ANNs) remains a considerable challenge. In this article, a sophisticated SNN modeling algorithm incorporating a novel dynamic threshold adaptation mechanism is proposed. It aims to eliminate the spiking synchronization error commonly occurred in many traditional ANN2SNN conversion works. Additionally, all variables in the proposed SNNs, including the membrane potential, threshold and synaptic weights, are quantized to integers, making them highly compatible with hardware implementation. Experimental results indicate that the proposed spiking LeNet and VGG-Net achieve accuracies exceeding 99.45% and 93.15% on the MNIST and CIFAR-10 datasets, respectively, with only 4 and 8 time steps required for simulating one sample. Due to this all integer-based quantization process, the required computational operations are significantly reduced, potentially providing a substantial energy efficiency advantage for numerous edge computing applications.

Modified ALVARADO and RIPASA Score Systems in Acute Appendicitis: A Diagnostic Comparative Accuracy Study

Modified ALVARADO and RIPASA Score Systems in Acute Appendicitis: A Diagnostic Comparative Accuracy Study

Homogeneous large field-of-view and compact iSCAT-TIRF setup for dynamic single molecule measurements

Interferometric scattering microscopy (iSCAT) enables prolonged and high frame rate single particle tracking (SPT) for studying molecular dynamics. Typical iSCAT setups employ conventional widefield or scanning illumination schemes. However, these implementations limit the field-of-view (FoV), the uniformity of the illumination and thus comparable accuracy over the whole FoV, and/or the maximum sampling rate, while in parts increasing hardware requirements and setup size. We demonstrate the realization of a large (60 µm x 60 µm) uniformly illuminated FoV through a passive refractive optical element in the iSCAT illumination path. This scanning-free iSCAT microscope setup is further combined with an objective based total internal reflection fluorescence microscopy (TIRF) channel for a complementary fluorescence readout, a focus-lock system, and a tailored control platform via the open-source ImSwitch software, and it has a compact footprint. As a proof-of-principle, we highlight the performance of the setup through the acquisition of iSCAT images with a uniform contrast and a constant ≤10 nm localization precision throughout the whole FoV. The performance is further demonstrated through dynamic iSCAT SPT and imaging fluorescence correlation spectroscopy (imaging FCS) of lipid diffusion in a model membrane system, highlighting the ability to track a large number of molecules with the same accuracy over a large FoV. Our iSCAT setup thus depicts an accurate and improved way of recording fast molecular dynamics in life sciences.

A dual-decoder banded convolutional attention network for bone segmentation in ultrasound images.

Ultrasound (US) has great potential for application in computer-assisted orthopedic surgery (CAOS) due to its non-radiative, cost-effective, and portable traits. However, bone segmentation from low-quality US images has been challenging. Traditional segmentation methods cannot achieve satisfactory results due to their high customization and dependence on bone morphology. Existing deep learning-based methods make it difficult to ensure efficient and accurate segmentation due to the ignorance of prior knowledge of bone features during feature learning. This paper aims to systematically investigate feature extraction and segmentation methodologies of bone US images and then proposes an innovative convolutional neural network to address the need for precise and efficient bone structure extraction in CAOS. This paper has proposed a dual-decoder banded convolutional attention network (BCA-Net), which takes the raw US image as input and simplified U-Net as the baseline network. Multiscale banded convolution kernels are employed internally in the BCA-Net model, leveraging the prior knowledge that bone surfaces in US images are exhibited as bright bands of a few millimeters in width. Additionally, a shared encoder to extract input features and two independent decoders to generate outputs for the bone surface and bone shadow mask are integrated into the BCA-Net model, leveraging the prior knowledge that US bone surfaces manifest low-intensity hollow shadows below. Then, a new task consistency loss is introduced that can utilize inter-task dependency fully and enhance the performance of our model. In the network construction process, a dataset containing 1623 sets of US images was adopted, and a five-fold cross-validation strategy was divided into the training and validation sets for the model's training and validation. Many vital metrics were introduced to comprehensively evaluate the model performance, including overlap, edge distance, area under curve, and model efficiency. Finally, the model performance was subjected to a confidence interval, Tukey's honest significant difference, and Cohen's d statistics at a significance level (5%) to ensure the accuracy and reliability of the obtained findings. The experimental results show that the BCA-Net model performs well in the bone surface segmentation task. Its average Dice coefficient reaches 87.51%, 4.04% higher than U-Net's, proving its superior bone surface segmentation accuracy. Meanwhile, the average distance error is 0.2mm, 0.33mm lower than U-Net's, highlighting its accuracy in detail capture and boundary recognition. Using a confidence distance threshold of 1.02mm, the Dice coefficient of the BCA-Net model exceeds 98%, an improvement of 1.87% over U-Net's, which is highly consistent with manual labeling. The BCA-Net model achieves a statistical significance of p-values<0.05 in the above accuracy comparisons. In addition, the BCA-Net model has a small parameter count (5.58M) and high computational efficiency (35.85 frames per second), further validating its excellent potential in bone surface segmentation tasks. The proposed method achieves excellent performance with high accuracy and efficiency, aligning well with clinical requirements and holding excellent potential for advancing the utilization of US images in CAOS.

A comparative numerical evaluation of solar air heater performance having W-contoured, taper-contoured and reverse taper-contoured turbulators

The use of different turbulators in solar air heaters can significantly impact their thermal and hydraulic performance. This study compares solar air heaters equipped with W-contoured, taper-contoured, and reverse taper-contoured turbulators. It examines heat transfer coefficients, pressure drops, velocity contours, turbulent kinetic energy contours, and thermal perfor-mance factors for these systems under varying operating conditions. The air Reynolds number ranges from 4000 to 18 000, while design parameters such as relative roughness height and relative pitch ratio remain constant for accurate comparison. The simulations were conducted with a uniform heat flux of 1200 W/m2. The W-shaped contour roughness achieved the greatest heat transfer coefficient, surpassing both the tapered and reverse tapered configurations. In terms of friction factor, the tapered contour on the absorber plate led, followed by the reverse tapered and W-shaped contours. Overall, the W-shaped contour delivered the best performance. At lower Reynolds numbers, the reverse tapered contour outperformed the tapered contour, whereas at higher Reynolds numbers, the tapered contour showed superior performance compared to the reverse tapered contour.

Minimizing Communication Overhead in Decentralized Deep Neural Networks

Minimizing communication overhead in decentralized deep neural network (DNN) training has become a critical challenge, particularly with the increasing adoption of distributed systems for large-scale machine learning tasks. This paper introduces advanced techniques that leverage gradient compression, adaptive sparsification, and hybrid aggregation to optimize communication efficiency while maintaining model accuracy and convergence rates. Experimental results on benchmark datasets such as CIFAR-10 and ImageNet show that the proposed methods reduce communication costs by up to 70% compared to standard approaches while achieving comparable or superior model accuracy. Additionally, scalability tests on diverse neural network architectures highlight the robustness of the approach, demonstrating efficient performance across varying network sizes and computational setups. These findings underscore the potential of the proposed strategies to enable faster, cost-effective, and sustainable decentralized deep learning systems.

Comparison of accuracy of brain biopsy simulation between 3-dimensional-printed guides and neuronavigation in skull-brain tumor models of dogs and cats.

This study aimed to compare the accuracy of brain biopsies in skull-brain tumor models (SBTMs) of dogs and cats using 2 techniques: 3-D-printed brain biopsy guides (3D-BBGs) and electromagnetic (EM) neuronavigation. Based on the CT data from 12 dogs and 3 cats, a total of 30 SBTMs were created using 3-D-printing technology, with 2 models per data set. Thirty brain biopsies were performed: 15 using 3D-BBGs and 15 using EM neuronavigation. The accuracy of the brain biopsies was assessed by comparing the prebiopsy and postbiopsy models using computer-aided design software. The median needle placement error for all biopsies was 1.75 mm (range: 0.82 to 3.16 mm), with 1.79 mm (range: 0.94 to 2.94 mm) for the 3D-BBG group and 1.68 mm (range: 0.82 to 3.16 mm) for the EM neuronavigation group. There was no significant difference in accuracy between the 2 methods. In the EM neuronavigation group, the needle placement error correlated significantly with the total needle length, but no such correlation was observed in the 3D-BBG group. Both methods successfully retrieved samples from brain tumor models. There was no significant difference in the accuracy of brain biopsies performed using 3D-BBGs and EM neuronavigation. This suggests that the choice of method depends on veterinarian preference, available hospital resources, and patient-specific considerations. This study demonstrates that both 3D-BBG and EM neuronavigation are viable options for performing brain biopsies in veterinary practice, potentially improving the diagnosis and treatment of brain tumors in small animals.

Comparative study of Claude 3.5-Sonnet and human physicians in generating discharge summaries for patients with renal insufficiency: assessment of efficiency, accuracy, and quality

BackgroundThe rapid development of artificial intelligence (AI) has shown great potential in medical document generation. This study aims to evaluate the performance of Claude 3.5-Sonnet, an advanced AI model, in generating discharge summaries for patients with renal insufficiency, compared to human physicians.MethodsA prospective, comparative study was conducted involving 100 patients (50 with acute kidney injury and 50 with chronic kidney disease) from the nephrology department of Ningbo Hangzhou Bay Hospital between January and June 2024. Discharge summaries were independently generated by Claude 3.5-Sonnet and human physicians. The main evaluation indicators included accuracy, generation time, and overall quality.ResultsClaude 3.5-Sonnet demonstrated comparable accuracy to human physicians in generating discharge summaries for both AKI (90 vs. 92 points, p &amp;gt; 0.05) and CKD patients (88 vs. 90 points, p &amp;gt; 0.05). The AI model significantly outperformed human physicians in terms of efficiency, requiring only about 30 s to generate a summary compared to over 15 min for physicians (p &amp;lt; 0.001). The overall quality scores showed no significant difference between AI-generated and physician-written summaries for both AKI (26 vs. 27 points, p &amp;gt; 0.05) and CKD patients (25 vs. 26 points, p &amp;gt; 0.05).ConclusionClaude 3.5-Sonnet demonstrates high efficiency and reliability in generating discharge summaries for patients with renal insufficiency, with accuracy and quality comparable to those of human physicians. These findings suggest that AI has significant potential to improve the efficiency of medical documentation, though further research is needed to optimize its integration into clinical practice and address ethical and privacy concerns.

A comparative study of GPT-4o and human ophthalmologists in glaucoma diagnosis

Artificial intelligence (AI), particularly large language models like GPT-4o, holds promise for enhancing diagnostic accuracy in healthcare. This study evaluates the diagnostic performance of GPT-4o compared to human ophthalmologists in glaucoma cases. A prospective, observational study was conducted at a tertiary care ophthalmology center. Twenty-six glaucoma cases, including both primary and secondary types, were selected from publicly available databases and institutional records. The cases were analyzed by GPT-4o and three ophthalmologists with varying levels of experience. The accuracy and completeness of primary and differential diagnoses were assessed using 10-point and 6-point Likert scales, respectively. Statistical analyses were performed using nonparametric methods, including the Kruskal–Wallis and Mann–Whitney U tests. GPT-4o was significantly less accurate in primary diagnosis compared to human ophthalmologists. Specifically, GPT-4o achieved a mean score of 5.500 (p < 0.001) compared to Doctor C, who had the highest score of 8.038 (p < 0.001). Completeness scores for GPT-4o 3.077 (p < 0.001) were also lower than Doctor B, who had the lowest score of 3.615 (p < 0.001) among human ophthalmologists. However, for differential diagnosis, GPT-4o (7.577) showed comparable accuracy to Doctor A (7.615) and Doctor C (7.673) (p < 0.0001) while achieving the highest completeness score (4.096), outperforming Doctor C (3.846), Doctor A (2.923), and Doctor B (2.808) (p < 0.0001). AI, including GPT-4o, is currently not an acceptable standalone method for diagnosing glaucoma due to its lower accuracy compared to human clinicians. These findings suggest that GPT-4o could serve as a valuable adjunct in clinical practice, particularly in complex cases, but should not replace human expertise, especially for initial diagnoses. Future improvements in AI models could enhance their utility in ophthalmology.

Comparison of Accuracy between Random Forest Method and K-Nearest Neighbors Method for Recognizing Solar Panel Energy Conversion Temperature

This study analyzes the classification of temperature image pattern recognition on solar panels to improve the accuracy of energy conversion performance affected by weather changes. The process begins by capturing the surface temperature image of the panel as primary data, which is then processed through the pattern recognition stage. The pattern recognition method is chosen to detect and understand patterns in temperature images that will be used as datasets. This study also compares the results of pattern recognition using the Random Forest classification method with the K-Nearest Neighbors (KNN) method, in order to build an effective model in analyzing images based on weather temperature in Medan City. The results of the study obtained that the solar panel produced a maximum output of 15.74 Wp at 12:00 pm, when the temperature tends to be higher and sunlight is optimal. Then the results of the random forest method showed good performance with an accuracy of 84% and the K-Nearest Neighbors method had an accuracy of 78%.

Evaluating the diagnostic value of 18F-FAPI-04 PET/CT in various malignant tumors: a head-to-head comparison with 18F-FDG PET/CT.

This study aims to evaluate the diagnostic efficacy of 18F-fibroblast activation protein inhibitor-04 (18F-FAPI-04) PET/CT for various malignant tumors and compare it head-to-head with 18F-FDG PET/CT. This single-center, prospective study continuously recruited patients with suspected or confirmed malignant tumors for concurrent 18F-FDG and 18F-FAPI-04 PET/CT scans from April 2022 to October 2023. The pathological diagnosis or clinical follow-up served as the reference standard. The Z-test for two proportions was used to compare the sensitivity, specificity, and accuracy of tumor diagnosis between the two imaging agents. Wilcoxon signed-rank tests were employed to compare the uptake of the two radiotracers and the tumor-to-background ratio (TBR) differences in tumors. The study involved 15 types of tumors and included 88 patients, comprising 53 males and 39 females, with an average age of 57.7 ± 10.8 years. In patient-based analysis, 18F-FAPI-04 PET/CT demonstrated higher diagnostic accuracy than 18F-FDG PET/CT for both initial staging and restaging patients (77.4% vs 56.6%, p = 0.0389; 94.3% vs 54.3%, p < 0.001), prompting treatment plan adjustments in 17% of restaged patients. The lesion-based analysis revealed comparable diagnostic accuracy of 18F-FAPI-04 PET/CT and 18F-FDG PET/CT for primary tumors (78.9% vs 75.4%, p = 0.8234), while showing superior accuracy for residual/recurrent tumors, lymph node metastases, and distant metastases compared to 18F-FDG PET/CT (100.0% vs 50.0%, p = 0.002; 98.8% vs 86.0%, p < 0.001; 98.3% vs 79.3%, p < 0.001). 18F-FAPI-04 PET/CT exhibits higher uptake and TBR in most tumors demonstrating superior diagnostic efficacy for primary lesions, residual/recurrent disease, and metastases compared to 18F-FDG PET/CT, particularly beneficial for restaging post-treatment patients.

A Centrality-Weighted Bidirectional Encoder Representation from Transformers Model for Enhanced Sequence Labeling in Key Phrase Extraction from Scientific Texts

Deep learning approaches, utilizing Bidirectional Encoder Representation from Transformers (BERT) and advanced fine-tuning techniques, have achieved state-of-the-art accuracies in the domain of term extraction from texts. However, BERT presents some limitations in that it primarily captures the semantic context relative to the surrounding text without considering how relevant or central a token is to the overall document content. There has also been research on the application of sequence labeling on contextualized embeddings; however, the existing methods often rely solely on local context for extracting key phrases from texts. To address these limitations, this study proposes a centrality-weighted BERT model for key phrase extraction from text using sequence labelling (CenBERT-SEQ). The proposed CenBERT-SEQ model utilizes BERT to represent terms with various contextual embedding architectures, and introduces a centrality-weighting layer that integrates document-level context into BERT. This layer leverages document embeddings to influence the importance of each term based on its relevance to the entire document. Finally, a linear classifier layer is employed to model the dependencies between the outputs, thereby enhancing the accuracy of the CenBERT-SEQ model. The proposed CenBERT-SEQ model was evaluated against the standard BERT base-uncased model using three Computer Science article datasets, namely, SemEval-2010, WWW, and KDD. The experimental results show that, although the CenBERT-SEQ and BERT-base models achieved higher and close comparable accuracy, the proposed CenBERT-SEQ model achieved higher precision, recall, and F1-score than the BERT-base model. Furthermore, a comparison of the proposed CenBERT-SEQ model to that of related studies revealed that the proposed CenBERT-SEQ model achieved a higher accuracy, precision, recall, and F1-score of 95%, 97%, 91%, and 94%, respectively, than related studies, showing the superior capabilities of the CenBERT-SEQ model in keyphrase extraction from scientific documents.

Enhanced Biomechanical Risk Assessment in Manual Lifting: Comparing Inertial Measurement Units with Optoelectronic Systems for Composite Lifting Index Calculation

This study aims to improve the assessment of biomechanical risk in manual lifting tasks by introducing a method for calculating composite lifting index (CLI) using wearable inertial measurement units (IMUs). While the revised NIOSH lifting equation (RNLE) is widely used to evaluate the risk associated with lifting tasks, traditional methods often struggle with accuracy, especially in complex tasks. To address this, we compared the CLI values obtained using IMUs with those derived from a gold standard optoelectronic system during laboratory tests involving three levels of lifting risk. Ten participants performed standardized lifting tasks under controlled conditions, and the results showed that the IMU-based method provided comparable accuracy to the optoelectronic system, with negligible differences. Despite some variability in horizontal multiplier (HM) values, the IMU system demonstrated potential for real-world applications due to its ease of use and automatic calculation capabilities. Future improvements may include refining distance measurements and expanding the method for more complex lifting scenarios. This novel approach offers a practical and precise tool for ergonomic risk assessments, particularly in dynamic work environments.

Comparative analysis of smartphone colorimeter apps and spectrophotometry for measuring forehead skin color in maxillofacial prosthesis fabrication.

This study aimed to evaluate the accuracy and precision of two smartphone colorimeter apps, Color Grab, and Color Picker, in measuring forehead skin color and to compare their readings with those from a spectrophotometer. Fifty participants (26 males, 24 females; median age 23 years, range 21-45) were included. Using a smartphone camera, images of forehead skin were captured, and CIELAB color values were reported by both apps. Measurements from a reference spectrophotometer (MiniScan EZ 4500L, 45°/0° geometry) served as the gold standard. Trueness and precision were assessed using CIELAB and CIEDE2000 formulas. Statistical analyses included one-way ANOVA and Mann-Whitney tests, with significance at p<0.05. Both apps showed comparable accuracy in capturing skin color, with absolute trueness (ΔEAbs) for Color Grab at 7.59 (CIEDE2000) and Color Picker at 7.65. Relative trueness (ΔERel) was 3.79 for Color Grab and 3.70 for Color Picker. Precision (MCDM) demonstrated significant differences between the apps: Color Grab at 1.34 (CIEDE2000) compared to Color Picker at 0.96 (p<0.0001). While smartphone apps may not match the accuracy of spectrophotometers, they offer valuable alternatives for color matching in maxillofacial prostheses. Future studies should focus on minimizing systematic errors related to environmental factors and camera settings to enhance measurement accuracy.

A quantum leaky integrate-and-fire spiking neuron and network

Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing — a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.

Comparison of Diagnostic Accuracy between CT scan and Colour Doppler USG in Detecting Malignant Ovarian Tumour

Comparison of Diagnostic Accuracy between CT scan and Colour Doppler USG in Detecting Malignant Ovarian Tumour