Accurate Grading Research Articles

Comparing gastrointestinal dysfunction score and acute gastrointestinal injury grade for predicting short-term mortality in critically ill patients

BACKGROUND The prognosis of critically ill patients is closely linked to their gastrointestinal (GI) function. The acute GI injury (AGI) grading system, established in 2012, is extensively utilized to evaluate GI dysfunction and forecast outcomes in clinical settings. In 2021, the GI dysfunction score (GIDS) was developed, building on the AGI grading system, to enhance the accuracy of GI dysfunction severity assessment, improve prognostic predictions, reduce subjectivity, and increase reproducibility. AIM To compare the predictive capabilities of GIDS and the AGI grading system for 28-day mortality in critically ill patients. METHODS A retrospective study was conducted at the general intensive care unit (ICU) of a regional university hospital. All data were collected during the first week of ICU admission. The primary outcome was 28-day mortality. Multivariable logistic regression analyzed whether GIDS and AGI grade were independent risk factors for 28-day mortality. The predictive abilities of GIDS and AGI grade were compared using the receiver operating characteristic curve, with DeLong’s test assessing differences between the curves’ areas. RESULTS The incidence of AGI in the first week of ICU admission was 92.13%. There were 85 deaths (47.75%) within 28 days of ICU admission. There was no initial 24-hour difference in GIDS between the non-survival and survival groups. Both GIDS (OR 2.01, 95%CI: 1.25-3.24; P = 0.004) and AGI grade (OR 1.94, 95%CI: 1.12-3.38; P = 0.019) were independent predictors of 28-day mortality. No significant difference was found between the predictive accuracy of GIDS and AGI grade for 28-day mortality during the first week of ICU admission (Z = -0.26, P = 0.794). CONCLUSION GIDS within the first 24 hours was an unreliable predictor of 28-day mortality. The predictive accuracy for 28-day mortality from both systems during the first week was comparable.

DELINEATE-Regurgitation: deep learning for automated assessment of aortic, mitral, and tricuspid regurgitation from echocardiography

Abstract Background Valvular heart diseases, including aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR) are significant contributors to cardiovascular morbidity and mortality worldwide. Artificial intelligence (AI) may improve echocardiographic assessment, but no models to date have successfully evaluated valvular regurgitation. Purpose The goal of this study was to develop an AI system to improve the accuracy of echocardiographic AR, MR, and TR interpretation. Methods Complete transthoracic echocardiogram (TTE) studies performed at a single center were split into train (n=65,301), validation (14,018), and test sets (13,746). The Deep Learning for Echo Analysis, Tracking, and Evaluation of Valvular Regurgitation (DELINEATE-Regurgitation) system intakes entire TTE studies, identifies color Doppler clips showing each valve, and yields a study level classification of AR, MR, and TR on a 6-grade scale (none/trace, mild, mild-moderate, moderate, moderate-severe, severe) using the cardiologist interpretation as a reference standard. This system used a hybrid neural network, leveraging the spatiotemporal feature learning capabilities of convolutional neural networks with the sequential processing strengths of transformer networks. The end-to-end hybrid approach allows the model to learn nuanced representations of the data including features within and across color Doppler videos. Model accuracy was assessed using quadratically weighted Cohen’s kappa (k), area under the receiver operator characteristic curve (AUROC) for the detection of both "moderate or greater" and "severe" regurgitation, and AI-cardiologist agreement the 6-grade scale. Additionally, an analysis was conducted using a 4-grade regurgitation scale (none/trace, mild, moderate, and severe) by rolling up intermediate classes (mild-moderate and moderate-severe) into the next higher class for both cardiologist determinations and model predictions. Results The DELINEATE-Regurgitation system demonstrated a high accuracy in the classification of AR (k=0.857), MR (k=0.867), and TR (k=0.847) (Figure 1). DELINEATE predicted the same severity as cardiologists (AR 91.8%, MR 79.9%, TR 77.7%) or within ±1 grade accuracy (AR 99.5%, MR 98.6%, TR 98.6%). The models had excellent binary discrimination in the detection of moderate or greater (AR AUROC 99.4, MR 98.6, TR 98.1) and severe (AR 99.7, MR 99.3, TR 99.6) regurgitation (Figure 2). In the 4-grade scale, the model had exact agreement with cardiologists in 92.3% of AR, 82.4% of MR, and 80.6% of TR cases. Panel adjudication of AI-cardiologist disagreement and correlation with quantitative metrics at higher levels of regurgitation will be presented. Conclusions The DELINEATE-Regurgitation AI system demonstrates a high level of accuracy in the assessment of AR, MR, and TR using color Doppler TTE videos. Future work will focus on automating quantification and developing strategies for optimal clinical deployment.Figure 1 - Confusion MatricesFigure 2 - ROC Curves

The Impact of COVID-19: A study of academic stress in high school students in Vietnam due to the transition from offline to online lessons.

CoViD-19 had a detrimental impact on all humans in our society, from white-collar office workers to students. The outbreak caused stress in all individuals, leading to pres- sure, and a decline in learnability, even though its magnitude and origins varied. In con- text with the education domain, the students have suffered adverse repercussions due to the shift from traditional in-person classes to online learning modes, which has been a major cause of their mental health’s downward spiral, leading to poor performances, and the inability to learn in unmonitored classroom conditions. Aim. To assess the stress in high school students and correlate their stress levels with their end-of-year examination results for 2021. Method. This can be achieved using Pearson’s correlation coefficient, by assessing 78 high school students selected as per inclusion and exclusion criteria, from the public schools of Hanoi, with their respective results obtained for their end-of-year examina- tions of March 2021. The Academic stress will be calculated with the Student Academic Stress Scale (SASS), and, all the percentage of grades will be out of 100. Results. The results obtained from the following Pearson’s correlation coefficient tests showed a moderate negative correlation existing between Academic Stress levels and marks it obtained, with the r value of -0.447. Limitations: The sample was collected from 4 public schools in Hanoi, not taking the rest of the country into account, and the accuracy of the stress level grading using SASS which can be affected by an impairment in cognitive function, excludes outliers from the research that was conducted.

Correlation of degree and pattern of contrast enhancement on Magnetic Resonance Imaging with histopathological grade of intracranial astrocytoma

Background/Objective: Astrocytomas, a type of glioma, require accurate grading to guide treatment. This study evaluates the correlation between MRI contrast enhancement and histopathological grades of astrocytomas. Methods: A cross-sectional study of 37 patients with biopsy-confirmed astrocytomas was conducted. Non-probability or purposive sampling was carried out. MRI scans assessed contrast enhancement characteristics, and tumors were classified radiologically and histopathologically. Results: The age range of the patients was 11 to 74 years and mean age was 40.25±16.04 years. The male to female ratio was 4:1. Headache was the most common presenting symptoms (95%). Most of the tumors were located at the cerebral hemispheres (78%). None or slight contrast enhancement was found in 17 (46%) and the pattern of contrast enhancement was heterogeneous in 24 (65%) patients. Maximum 20 (54%) patients had high grade astrocytoma. In the present study, 85% high grade astrocytoma showed moderate to marked whereas 82.4% low grade astrocytoma showed none or slight contrast enhancement. Sensitivity & specificity of contrast enhanced MRI was 82.4% and 85% respectively. The overall accuracy was 83.78%. Conclusion: MRI is a reliable non-invasive tool for grading astrocytomas, with strong correlation between contrast enhancement and tumor grade, supporting its use in clinical decision-making. J Shaheed Suhrawardy Med Coll 2023; 15(1): 13-19

Preoperative prediction of diffuse glioma type and grade in adults: a gadolinium-free MRI-based decision tree.

To develop a gadolinium-free MRI-based diagnosis prediction decision tree (DPDT) for adult-type diffuse gliomas and to assess the added value of gadolinium-based contrast agent (GBCA)enhanced images. This study included preoperative grade 2-4 adult-type diffuse gliomas (World Health Organization 2021) scanned between 2010 and 2021. The DPDT, incorporating eleven GBCA-free MRI features, was developed using 18% of the dataset based on consensus readings. Diagnosis predictions involved grade (grade 2 vs. grade 3/4) and molecular status (isocitrate dehydrogenase (IDH) and 1p/19q). GBCA-free diagnosis was predicted using DPDT, while GBCA-enhanced diagnosis included post-contrast images. The accuracy of these predictions was assessed by three raters with varying experience levels in neuroradiology using the test dataset. Agreement analyses were applied to evaluate the prediction performance/reproducibility. The test dataset included 303 patients (age (SD): 56.7 (14.2) years, female/male: 114/189, low-grade/high-grade: 54/249, IDH-mutant/wildtype: 82/221, 1p/19q-codeleted/intact: 34/269). Per-rater GBCA-free predictions achieved ≥ 0.85 (95%-CI: 0.80-0.88) accuracy for grade and ≥ 0.75 (95%-CI: 0.70-0.80) for molecular status, while GBCA-enhanced predictions reached ≥ 0.87 (95%-CI: 0.82-0.90) and ≥ 0.77 (95%-CI: 0.71-0.81), respectively. No accuracy difference was observed between GBCA-free and GBCA-enhanced predictions. Group inter-rater agreement was moderate for GBCA-free (0.56 (95%-CI: 0.46-0.66)) and substantial for GBCA-enhanced grade prediction (0.68 (95%-CI: 0.58-0.78), p = 0.008), while substantial for both GBCA-free (0.75 (95%-CI: 0.69-0.80) and GBCA-enhanced (0.77 (95%-CI: 0.71-0.82), p = 0.51) molecular status predictions. The proposed GBCA-free diagnosis prediction decision tree performed well, with GBCA-enhanced images adding little to the preoperative diagnostic accuracy of adult-type diffuse gliomas. Question Given health and environmental concerns, is there a gadolinium-free imaging protocol to preoperatively evaluate gliomas comparable to the gadolinium-enhanced standard practice? Findings The proposed gadolinium-free diagnosis prediction decision tree for adult-type diffuse gliomas performed well, and gadolinium-enhanced MRI demonstrated only limited improvement in diagnostic accuracy. Clinical relevance Even inexperienced raters effectively classified adult-type diffuse gliomas using the gadolinium-free diagnosis prediction decision tree, which, until further validation, can be used alongside gadolinium-enhanced images to respect standard practice, despite this study showing that gadolinium-enhanced images hardly improved diagnostic accuracy.

Manipulator with Integrated Flexible Tactile Sensing Arrays for Kiwifruit Ripeness and Size Classification.

Fruit grading for ripeness and size is an essential process in the supply chain. Incorrect grading can easily lead to spoiled and degraded fruits entering the market, reducing consumers' confidence in purchasing. At the same time, it is easy to cause the fruit supply chain to reduce profits, unreasonable resource allocation, and related practitioners' income. The current mainstream machine vision grading and manual grading in the production line have dilemmas such as susceptibility to environmental interference, inconsistent grading standards, high cost, and labor shortage. To overcome these problems, this study proposes an integrated flexible tactile sensing array (3 × 4) manipulator for efficient, stable, low-cost, and accurate ripeness and size grading of kiwifruit. The flexible sensing manipulator grasps the kiwifruit, detects the hardness of the kiwifruit by relying on tactile sensing, and determines the ripeness level based on the hardness. The size of the kiwifruit is also differentiated according to whether there is a significant change in the resistance of the topmost sensing unit of the flexible pressure sensor array. The 0, 1, 2, 3, 4, and 5 anomalies that may occur in actual production were tested and combined with machine learning KNN, SVM, and RF algorithms for data modeling and grading. The results show that the lowest accuracy of 0, 1, 2, 3, 4, and 5 possible outliers is 86.67% (KNN), 95.83% (SVM), and 92.5% (RF), respectively. KNN has the lowest classification effect, and SVM has the best. This study overcomes the drawbacks of inefficient destructive detection and unstable manual detection and makes up for the vulnerability of single machine vision to interference from environmental factors. This study can alleviate the challenges caused by fruit wastage and promote the sustainable production and consumption of the fruit industry chain.

Minimizing overtreatment and maximizing oncologic outcomes in upper tract urothelial carcinoma

Purpose of review An update on the latest advances in the management of upper tract urothelial carcinoma (UTUC), with an emphasis on strategies to optimize oncologic outcomes while minimizing overtreatment. Recent findings Recent high-quality trials have changed the landscape of UTUC treatment. Emerging tools including 3D histology and measurement of cell free tumor DNA may improve diagnostic accuracy of disease grading and staging, and be used in monitoring treatment response. Novel therapies show promise of reducing low-grade UTUC disease recurrence at the cost of significant side-effects. Platinum-based neoadjuvant chemotherapy in high-grade/muscle-invasive disease showed complete pathological response in a subset of patients, but difficult to predict responders. Adjuvant platinum-based chemotherapy exhibited a clear survival benefit, but immunotherapy did not, suggesting possible overtreatment with these agents. Molecularly-targeted therapies in metastatic UTUC have shown the greatest recent oncologic advances, but exhibit a high adverse event-rate. Summary Low-grade UTUC has the potential for overtreatment, as it exhibits low metastatic-potential and excellent survival. For high-grade and advanced-stage UTUC, these carry high mortality rates and require more aggressive treatment, but studies are limited by inaccurate grading and staging which can lead to overtreatment especially in the neoadjuvant setting. Emerging technologies will help improve diagnostic accuracy and noninvasive monitoring of treatment response.

From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods

Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future.

Enhancing Faculty Grading Productivity Using Robotic Process Automation (RPA): The Development of a Structured Query Language (SQL) Automated Grading Tool

ABSTRACT Using the design science methodology, we develop an automated grading bot using robotic process automation (RPA) software. This grader demonstrates RPA’s capability to enhance the efficiency and accuracy of grading structured query language (SQL) queries. Validation of the RPA grader demonstrates that it can grade assignments and/or exams in 30 to 60 minutes, a task that would take a human grader approximately 50 hours. In contrast to human graders, who misgraded 5 to 8 percent of queries, the RPA grader graded all queries correctly. Notably, it correctly evaluated 188 distinct correct solutions submitted by students to a single question. To facilitate broad adoption, we provide instructions and the necessary code for implementing the RPA grader. We validate that professors and teaching assistants can successfully implement the RPA grader even with no previous bot building experience.

An overview of current developments and methods for identifying diabetic foot ulcers: A survey

AbstractDiabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding and effective management. Diagnosing DFU is imperative, as it plays a major role in the processes of diagnosis, treatment planning, and neuropathy research within automated health monitoring and diagnosis systems. To address this, various machine learning and deep learning‐based methodologies have emerged in the literature to support healthcare practitioners in achieving improved diagnostic analyses for DFU. This survey paper investigates various diagnostic methodologies for DFU, spanning traditional statistical approaches to cutting‐edge deep learning techniques. It systematically reviews key stages involved in diabetic foot ulcer classification (DFUC) methods, including preprocessing, feature extraction, and classification, explaining their benefits and drawbacks. The investigation extends to exploring state‐of‐the‐art convolutional neural network models tailored for DFUC, involving extensive experiments with data augmentation and transfer learning methods. The overview also outlines datasets commonly employed for evaluating DFUC methodologies. Recognizing that neuropathy and reduced blood flow in the lower limbs might be caused by atherosclerotic blood vessels, this paper provides recommendations to researchers and practitioners involved in routine medical therapy to prevent substantial complications. Apart from reviewing prior literature, this survey aims to influence the future of DFU diagnostics by outlining prospective research directions, particularly in the domains of personalized and intelligent healthcare. Finally, this overview is to contribute to the continual evolution of DFU diagnosis in order to provide more effective and customized medical care.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence

Epigenetic landscape reorganisation and reactivation of embryonic development genes are associated with malignancy in IDH-mutant astrocytoma

Accurate grading of IDH-mutant gliomas defines patient prognosis and guides the treatment path. Histological grading is challenging, and aside from CDKN2A/B homozygous deletions in IDH-mutant astrocytomas, there are no other objective molecular markers used for grading. RNA-sequencing was conducted on primary IDH-mutant astrocytomas (n = 138) included in the prospective CATNON trial, which was performed to assess the prognostic effect of adjuvant and concurrent temozolomide. We integrated the RNA-sequencing data with matched DNA-methylation and NGS data. We also used multi-omics data from IDH-mutant astrocytomas included in the TCGA dataset and validated results on matched primary and recurrent samples from the GLASS-NL study. Since discrete classes do not adequately capture grading of these tumours, we utilised DNA-methylation profiles to generate a Continuous Grading Coefficient (CGC) based on classification scores from a CNS-tumour classifier. CGC was an independent predictor of survival outperforming current WHO-CNS5 and methylation-based classification. Our RNA-sequencing analysis revealed four distinct transcription clusters that were associated with (i) upregulation of cell cycling genes; (ii) downregulation of glial differentiation genes; (iii) upregulation of embryonic development genes (e.g. HOX, PAX, and TBX) and (iv) upregulation of extracellular matrix genes. The upregulation of embryonic development genes was associated with a specific increase of CpG island methylation near these genes. Higher grade IDH-mutant astrocytomas have DNA-methylation signatures that, on the RNA level, are associated with increased cell cycling, tumour cell de-differentiation and extracellular matrix remodelling. These combined molecular signatures can serve as an objective marker for grading of IDH-mutant astrocytomas.

The usefulness of evaluating PTEN expression for accurate grading of phyllodes tumors

Phyllodes tumors (PTs) are classified as benign, borderline, or malignant based on histologic characteristics. However, because histological criteria are subjective and diagnosis requires integrating multiple findings, discrepancies often occur between observers. Therefore, it is necessary to discover biomarkers based on the molecular characteristics of PTs. This study aimed to identify dysregulated microRNAs (miRNAs) in PTs through miRNA profiling and determine whether expression of their target genes could be useful as PT biomarkers. MiRNA profiling was performed on 13 PTs (three malignant, three borderline, seven benign) and six fibroadenomas, and predicted target genes of dysregulated miRNAs were selected using three computation algorithms. The expression of two miRNAs, miR-155 and miR-200c, was 1.69-fold and 1.61-fold higher, respectively, in borderline and malignant PT groups than in the benign PT group (p < 0.05). KEGG pathway analysis revealed that the 374 target genes of these two miRNAs (miR-155 and miR200c) participated in several signaling pathways, adherens junction, cell cycle, and pathway in cancer. Immunohistochemical staining for PTEN, one of candidate target genes of miR-200c, was performed on whole slides of PT tissue classified as malignant (n = 9), borderline (n = 12), or benign (n = 21). Stromal tumor cells of high-grade PTs (borderline and malignant) had significantly lower PTEN expression than those of low-grade PTs (benign) (p-value ≤0.001). Semiquantitative analysis of PTEN expression, score 0–8, revealed that it correlated with histologic findings. Low PTEN expression (s-score of 6 or less) was used as a diagnostic criterion for high-grade PTs, it showed 100 % sensitivity and 95.2 % specificity in 42 cases of PTs. Currently, PT grading based solely on subjective histologic findings is challenging, but semiquantitative PTEN expression analysis could provide a more accurate and objective way to grade PTs.

Novel 3-dimensional effective regurgitation orifice area quantification serves as a reliable tool to identify severe mitral valve regurgitation.

A precise quantification of mitral regurgitation (MR) severity is essential for treatment and outcome of patients with MR. 3D echocardiography facilitates estimation of MR but selection of patients with necessity of invasive treatment remains challenging. We investigate effective regurgitation orifice area (EROA) quantification by 3D compared to 2D echocardiography in patients with MR and highlight the improved discrimination of MR severity. We consecutively enrolled fifty patients with primary or secondary and at least moderate MR undergoing 2D and 3D colour Doppler echocardiography prior to transcatheter edge-to-edge repair (TEER). Improved accuracy of MR grading using 3D vena contracta area (VCA) as an estimate of EROA was compared to 2D proximal isovelocity surface area (PISA) quantification method and a multiparameter reference standard. Quantification of EROA remarkably varies between 2D and 3D echocardiography and the discrimination between moderate and severe MR was significantly (p = 0.001) different using 2D PISA or 3D VCA, respectively. 3D VCA correlated significantly (r = 0.501, p < 0.001) better with the pre-defined MR severity. We detected crucial differences in the correct identification of severe MR between 2D and 3D techniques, thus 2D PISA significantly (p < 0.0001) underestimates EROA due to clinical and morphological parameters. The assessment of 3D VCA resulted in improved diagnostic accuracy.

ResMT: A hybrid CNN-transformer framework for glioma grading with 3D MRI

Accurate grading of gliomas is crucial for treatment strategies and prognosis. While convolutional neural networks (CNNs) have proven effective in classifying medical images, they struggle with capturing long-range dependencies among pixels. Transformer-based networks can address this issue, but CNN-based methods often perform better when trained on small datasets. Additionally, tumor segmentation is essential for classification models, but training an additional segmentation model significantly increases workload. To address these challenges, we propose ResMT, which combines CNN and transformer architectures for glioma grading, extracting both local and global features efficiently. Specifically, we designed a spatial residual module (SRM) where a 3D CNN captures glioma's volumetric complexity, and Swin UNETR, a pre-trained segmentation model, enhances the network without extra training. Our model also includes a multi-plane channel and spatial attention module (MCSA) to refine the analysis by focusing on critical features across multiple planes (axial, coronal, and sagittal). Transformer blocks establish long-range relationships among planes and slices. We evaluated ResMT on the BraTs19 dataset, comparing it with baselines and state-of-the-art models. Results demonstrate that ResMT achieves the highest prediction performance with an AUC of 0.9953, highlighting hybrid CNN-transformer models' potential for 3D MRI classification.

Automated grading of diabetic retinopathy and Radiomics analysis on ultra-wide optical coherence tomography angiography scans

Diabetic retinopathy (DR), a progressive condition due to diabetes that can lead to blindness, is typically characterized by a number of stages, including non-proliferative (mild, moderate and severe) and proliferative DR. These stages are marked by various vascular abnormalities, such as intraretinal microvascular abnormalities (IRMA), neovascularization (NV), and non-perfusion areas (NPA). Automated detection of these abnormalities and grading the severity of DR are crucial for computer-aided diagnosis. Ultra-wide optical coherence tomography angiography (UW-OCTA) images, a type of retinal imaging, are particularly well-suited for analyzing vascular abnormalities due to their prominence on these images. However, accurate detection of abnormalities and subsequent grading of DR is quite challenging due to noisy data, presence of artifacts, poor contrast and subtle nature of abnormalities. In this work, we aim to develop an automated method for accurate grading of DR severity on UW-OCTA images. Our method consists of various components such as UW-OCTA scan quality assessment, segmentation of vascular abnormalities and grading the scans for DR severity. Applied on publicly available data from Diabetic retinopathy analysis challenge (DRAC 2022), our method shows promising results with a Dice overlap metric and recall values of 0.88 for abnormality segmentation, and the coefficient-of-agreement (κ) value of 0.873 for DR grading. We also performed a radiomics analysis, and observed that the radiomics features are significantly different for increasing levels of DR severity. This suggests that radiomics could be used for multimodal grading and further analysis of DR, indicating its potential scope in this area.

A multi-task learning model for clinically interpretable sesamoiditis grading

Sesamoiditis is a common equine disease with varying severity, leading to increased injury risks and performance degradation in horses. Accurate grading of sesamoiditis is crucial for effective treatment. Although deep learning-based approaches for grading sesamoiditis show promise, they remain underexplored and often lack clinical interpretability. To address this issue, we propose a novel, clinically interpretable multi-task learning model that integrates clinical knowledge with machine learning. The proposed model employs a dual-branch decoder to simultaneously perform sesamoiditis grading and vascular channel segmentation. Feature fusion is utilized to transfer knowledge between these tasks, enabling the identification of subtle radiographic variations. Additionally, our model generates a diagnostic report that, along with the vascular channel mask, serves as an explanation of the model’s grading decisions, thereby increasing the transparency of the decision-making process. We validate our model on two datasets, demonstrating its superior performance compared to state-of-the-art models in terms of accuracy and generalization. This study provides a foundational framework for the interpretable grading of similar diseases.

Ultrasound-Based Grading of Carpal Tunnel Syndrome: A Comparative Study of Cross-Sectional Area and Shear Wave Elastography at Different Wrist Joint Angles.

Carpal Tunnel Syndrome (CTS) is a prevalent neuropathy where accurate diagnosis is crucial for effective treatment planning. This study introduces a novel approach for CTS grading using ultrasound, specifically through the analysis of the cross-sectional area (CSA) and shear wave elastography (SWE) of the median nerve in various wrist positions. Our research involved subjects from outpatient clinics, diagnosed with CTS through Nerve Conduction Studies (NCS), and a control group of healthy individuals. High-frequency ultrasound and SWE measurements were conducted in three wrist positions: straight, 45° extension, and 45° flexion. The key findings revealed significant differences in median nerve CSA and SWE values between the CTS and control groups across all wrist positions, with notable variances in SWE values correlating with wrist positioning. SWE demonstrated enhanced sensitivity and specificity in distinguishing between mild, moderate, and severe CTS, especially at 45° wrist flexion. In contrast, CSA measurements were limited in differentiating between the varying severity stages of CTS. The study concludes that SWE, particularly at 45° wrist flexion, provides a more precise diagnostic benchmark for CTS severity grading than CSA. This advancement in non-invasive diagnostic methodology not only aids in accurate CTS grading but also has significant implications in formulating tailored treatment strategies, potentially reducing the reliance on more invasive diagnostic methods like NCS. This study marks a significant advancement in the ultrasound diagnosis of CTS. It particularly highlights the importance of applying SWE technology across various wrist joint angles, offering a new diagnostic benchmark. This discovery provides data support and additional insights for achieving an early consensus on ultrasound-based grading diagnosis of CTS.

Advancements and applications of AI technologies in pathology: a scoping review

ABSTRACT The benefits of AI in pathology are demonstrated by the growing potential of diagnostic accuracy and efficiency in tissue segmentation, cancer detection and grading. In this scoping review, we studied how artificial intelligence (AI) technologies have been developed for usage in pathology. In the study, a literature search on databases and publisher portals PubMed, ScienceDirect, ProQuest, and CINAHL was conducted to collect peer-reviewed studies published between 2019 and 2023. Then after a thorough database search, 57 articles covering the topic were included out of 19 185 potential ones and were categorised into groups by their main purposes, such as pre-processing, segmentation, analysis, and prediction. The findings demonstrate that AI technologies like deep learning and convolutional neural networks (CNNs) enhance medical imaging for tissue segmentation and cancer detection by reducing human error and variability.

A FAPα-activated MRI nanoprobe for precise grading diagnosis of clinical liver fibrosis

Molecular imaging holds the potential for noninvasive and accurate grading of liver fibrosis. It is limited by the lack of biomarkers that strongly correlate with liver fibrosis grade. Here, we discover the grading potential of fibroblast activation protein alpha (FAPα) for liver fibrosis through transcriptional analysis and biological assays on clinical liver samples. The protein and mRNA expression of FAPα are linearly correlated with fibrosis grade (R2 = 0.89 and 0.91, respectively). A FAPα-responsive MRI molecular nanoprobe is prepared for quantitatively grading liver fibrosis. The nanoprobe is composed of superparamagnetic amorphous iron nanoparticles (AFeNPs) and paramagnetic gadoteric acid (Gd-DOTA) connected by FAPα-responsive peptide chains (ASGPAGPA). As liver fibrosis worsens, the increased FAPα cut off more ASGPAGPA, restoring a higher T1-MRI signal of Gd-DOTA. Otherwise, the signal remains quenched due to the distance-dependent magnetic resonance tuning (MRET) effect between AFeNPs and Gd-DOTA. The nanoprobe identifies F1, F2, F3, and F4 fibrosis, with area under the curve of 99.8%, 66.7%, 70.4%, and 96.3% in patients’ samples, respectively. This strategy exhibits potential in utilizing molecular imaging for the early detection and grading of liver fibrosis in the clinic.

Effects of Growth and Treatment Conditions on the Quality of Norway Spruce (Picea abies L.) Sawn Timber

A study was conducted to improve the effectiveness of silvicultural production of structural sawn timber from softwoods. It intends to explore prediction methods for mechanical timber quality. The study material was obtained from six stands divided into age groups of approximately 40- and 80-year-old trees (examining the influence of age). The stands were differentiated by their applied thinning system of thinning from below or above (examining the influence of the thinning system). Resulting from these different levels of data, i.e., stand parameters, tree anatomy, and visual board properties are examined and analyzed in ordinal logistic models and linear mixed models. Visual board properties were discerned by means of the German standard for visual grading of sawn timber. The mechanical board properties were measured in on-edge bending strength tests and allocated into strength classes, which were modeled in dependence of visual characteristics and forestry conditions. The evaluation of mechanical properties attributed a significant loss of timber quality to short rotation periods, non-ideal water supply, and a single-tree management system. The prediction capabilities of models based on site and tree characteristics were on par with the accuracy of visual grading. Management adaptations by intense thinning from above can lead to a significant decline in Norway spruce (Picea abies L.) timber quality when site factors coincide. Particular care should be taken in the management of locations with high yield potential. Non-destructive evaluation based on site characteristics combined with terrestrial laser scan data of tree characteristics has potential as a pregrading method.