Grading Method Research Articles

Dental age estimation: A comparative study of convolutional neural network and Demirjian's method

The aim of this study is to compare a technique using Convolutional Neural Network (CNN) with the Demirjian's method for chronological age estimation of living individuals based on tooth age from panoramic radiographs. This research used 5898 panoramic X-ray images collected for diagnostic from pediatric patients aged 4–17 who sought treatment at Antalya Oral and Dental Health Hospital between 2015 and 2020. The Demirjian's method's grading was executed by researchers who possessed appropriate training and experience. In the CNN method, various CNN architectures including Alexnet, VGG16, ResNet152, DenseNet201, InceptionV3, Xception, NASNetLarge, InceptionResNetV2, and MobieNetV2 have been evaluated. Densenet201 exhibited the lowest MAE value of 0.73 years, emphasizing its superior accuracy in age estimation compared to other architectures. In most age categories, the predicted age closely matches the actual age. The most inconsistent results are observed at ages 12 and 13. The results highlight correspondence between the age predicted by CNN and the Demirjian's approach. In conclusion, the results show that the CNN method is adequate to be an alternative to the Demirjian's age estimation method. We suggest that convolutional neural network can effectively optimize the accuracy of age estimation and can be faster than traditional methods, eliminating the need for additional learning from experts.

A Non-Destructive Detection and Grading Method of the Internal Quality of Preserved Eggs Based on an Improved ConvNext.

As a traditional delicacy in China, preserved eggs inevitably experience instances of substandard quality during the production process. Chinese preserved egg production facilities can only rely on experienced workers to select the preserved eggs. However, the manual selection of preserved eggs presents challenges such as a low efficiency, subjective judgments, high costs, and hindered industrial production processes. In response to these challenges, this study procured the transmitted imagery of preserved eggs and refined the ConvNeXt network across four pivotal dimensions: the dimensionality reduction of model feature maps, the integration of multi-scale feature fusion (MSFF), the incorporation of a global attention mechanism (GAM) module, and the amalgamation of the cross-entropy loss function with focal loss. The resultant refined model, ConvNeXt_PEgg, attained proficiency in classifying and grading preserved eggs. Notably, the improved model achieved a classification accuracy of 92.6% across the five categories of preserved eggs, with a grading accuracy of 95.9% spanning three levels. Moreover, in contrast to its predecessor, the refined model witnessed a 24.5% reduction in the parameter volume, alongside a 3.2 percentage point augmentation in the classification accuracy and a 2.8 percentage point boost in the grading accuracy. Through meticulous comparative analysis, each enhancement exhibited varying degrees of performance elevation. Evidently, the refined model outshone a plethora of classical models, underscoring its efficacy in discerning the internal quality of preserved eggs. With its potential for real-world implementation, this technology portends to heighten the economic viability of manufacturing facilities.

Characteristics and Release Risk of Phosphorus from Sediments in a Karst Canyon Reservoir, China

With the continuous improvement of surface water environmental quality in China, sediment has gradually become the focus of research on internal pollution in reservoirs. To investigate the forms, distribution, and migration patterns of phosphorus in karst canyon reservoir sediments, we employed an improved sequential graded extraction method to determine phosphorus content in the sediments of the Wanfeng Reservoir. We studied the spatial distribution characteristics and release risk of phosphorus form in the sediments. The results showed that the total phosphorus (TP) content ranged from 79.37 to 438.04 mg·kg−1, while inorganic phosphorus (IP) content ranged from 77.32 to 424.64 mg·kg−1. Iron–aluminum-bound inorganic phosphorus (Fe/Al-Pi) accounted for 36.41% and was found to be the dominant form of IP. Organic phosphorus (OP) content ranged from 1.84 to 13.59 mg·kg−1, with weakly adsorbed organic phosphorus (H2O-Po) being the dominant form of OP. Potentially active inorganic phosphorus (NaHCO3-Pi) showed a highly significant positive correlation (p < 0.01) with Fe/Al-Pi, residual phosphorus (Res-P), and TP. This indicates that potentially active phosphorus (NaHCO3-P) is a significant potential source of phosphorus (P) in the reservoir. Biologically active phosphorus (BAP) content ranged from 66.97 to 201.46 mg·kg−1, with BAP/TP ratios ranging from 55.6% to 59.6%. The risk of phosphorus release from Wanfeng Reservoir sediments is high. The release of various forms of phosphorus from sediments is one of the important factors leading to the deterioration of water quality in the reservoir area in the future. To effectively manage water ecology in karst canyon reservoirs, it is essential to reduce the risk of endogenous phosphorus release.

How reaching numerical roundness on subgoals affects the completion of superordinate goals

This research shows that consumers who achieve numerically round subgoals perceive a higher chance of accomplishing their superordinate goals compared to those who achieve numerically non-round subgoals. This effect is mediated by the shifts in motivation to accomplish superordinate goals. First, a pilot test presents preliminary evidence from a large secondary dataset of runners’ timings in a 10 k race. Then, the effect is tested in a series of scenario-based experiments by manipulating the round vs. non-round subgoals in a variety of contexts, such as credit score, running, weight loss and loyalty programs. This is followed by a study employing a game setting where participants make guesses and earn (round vs. non-round) points. Next, a field experiment manipulates the grading method of student exams in an actual course to utilize round vs. non-round scales. Finally, a lab experiment demonstrates the moderating role of goal progress level.

Research and Design of a Chicken Wing Testing and Weight Grading Device

This thesis introduces a nondestructive inspection and weight grading device for chicken wings to replace the traditional manual grading operation. A two-sided quality nondestructive inspection model of chicken wings based on the YOLO v7-tiny target detection algorithm is designed and deployed in a Jetson Xavier NX embedded platform. An STM32 microcontroller is used as the main control platform, and a wing turning device adapting to the conveyor belt speed, dynamic weighing, and a high-efficiency intelligent grading unit are developed, and the prototype is optimized and verified in experiments. Experiments show that the device can grade four chicken wings per second, with a comprehensive accuracy rate of 98.4%, which is better than the traditional grading methods in terms of efficiency and accuracy.

APPLICATION AND EVALUATION OF A MULTI-SCALE ASSESSMENT MODEL FOR ELDERLY DISABILITY

As the aging population continues to grow, the increasing number of disabled elderly individuals poses ongoing challenges to society in terms of healthcare and medical burdens. Grading disability is of significant importance for improving medical services and allocating social resources. Traditional grading methods using a single assessment scale tend to be subjective and cannot comprehensively and accurately reflect the real situation. This paper presents the NES (Needs assessment of Elderly care in Shanghai) scale, based on 33 quantified scoring items from basic surveys, to enhance the objectivity and accuracy of disability grading. Correlation analysis of observed items using the C4.5 decision tree algorithm is conducted, followed by an analysis and evaluation of the scale’s performance. The data used in this study are derived from 266 questionnaires collected from eight regions in Shanghai and categorized into three classes: physiological (JKOM/BADL), psychological (BDI/SDS/SAS), and cognitive (CDR). The analysis results show that the NES scale exhibits better robustness in physiological assessment compared to the BADL and JKOM scales. In psychological assessment, the NES scale performs similarly to the SDS and SAS scales. In cognitive assessment, the NES scale’s grading level is slightly higher than that of the CDR scale, demonstrating good comprehensive disability assessment capabilities.

Assessment of artificial intelligence model and manual morphokinetic annotation system as embryo grading methods for successful live birth prediction: a retrospective monocentric study

PurposeThe introduction of the time-lapse monitoring system (TMS) and the development of predictive algorithms could contribute to the optimal embryos selection for transfer. Therefore, the present study aims at investigating the efficiency of KIDScore and iDAScore systems for blastocyst stage embryos in predicting live birth events.MethodsThe present retrospective study was conducted in a private IVF Unit setting throughout a 10-month period from October 2021 to July 2022, and included the analysis of 429 embryos deriving from 91 IVF/ICSI cycles conducted due to infertility of various etiologies. Embryos incubated at the Embryoscope+ timelapse incubator were analyzed through the established scoring systems: KIDScore and iDAScore®. The main outcome measure was the comparison of the two scoring systems in terms of live birth prediction. Embryos with the higher scores at day 5 (KID5 score/iDA5 score) were transferred or cryopreserved for later use.ResultsEmbryos with high KID5 and iDA5 scores positively correlated with the probability of successful live birth, with KID5 score yielding a higher efficiency in predicting a successful reproductive outcome compared to a proportionally high iDA5 score. KID5 demonstrated conservative performance in successfully predicting live birth compared to iDA5 score, indicating that an efficient prediction can be either provided by a relatively lower KID5 score or a relatively higher iDA5 score.ConclusionThe developed artificial intelligence tools should be implemented in clinical practice in conjunction with the conventional morphological assessment for the conduction of optimized embryo transfer in terms of a successful live birth.

Standards-Based Grading in Ensemble Music

Nearly all music educators assign grades to students. However, not all methods for grading are equally effective at reporting student achievement. This article describes one approach to grading, standards-based grading, that has the potential to support music educators’ efforts to achieve grades that are honest, meaningful, and fair. General principles and specific recommendations for ensemble music teachers are discussed.

An Evaluation of Low-Grade Red Oak and Soft Maple as Raw Material for Cross-Laminated Timber Panel Production

Abstract Utilization of hardwoods in the manufacture of cross-laminated timber (CLT) faces many challenges, one of which is the selection of raw material that is both technically and economically feasible. From an economic perspective, it would make sense to choose species and grades that are both readily available and relatively competitive with softwood species currently used in CLT manufacturing. For the purposes of this study, lower grades (2A Common, 2B Common, 3A Common, and 3B Common) of red oak and soft maple were deemed appropriate in fitting this profile. All lumber was visually graded according to both National Hardwood Lumber Association (NHLA) and Northeastern Lumber Manufacturers Association (NeLMA) grading rules, as well as nondestructively tested through flatwise bending to determine modulus of elasticity (MOE). The yield distribution of each species and NHLA grade, by visual structural grade, were analyzed. The mechanical testing of each species was analyzed based on the minimum allowable MOE of 1.2 x 106 psi as required by ANSI/PRG-320. Mechanical testing resulted in much higher yields of acceptable CLT material than visual grading, for both species. A total net worth analysis was conducted to evaluate the value of NHLA graded lumber being processed into structurally graded lumber for both species and both grading methods. Finally, a procurement analysis was conducted to determine the volume of lumber required for both species, in order to achieve a fixed volume of CLT-ready lumber.

Classification and rating of disintegrated dolomite strata for slope stability analysis

Although disintegrated dolomite, widely distributed across the globe, has conventionally been a focus of research in underground engineering, the issue of slope stability issues in disintegrated dolomite strata is gaining increasing prominence. This is primarily due to their unique properties, including low strength and loose structure. Current methods for evaluating slope stability, such as basic quality (BQ) and slope stability probability classification (SSPC), do not adequately account for the poor integrity and structural fragmentation characteristic of disintegrated dolomite. To address this challenge, an analysis of the applicability of the limit equilibrium method (LEM), BQ, and SSPC methods was conducted on eight disintegrated dolomite slopes located in Baoshan, Southwest China. However, conflicting results were obtained. Therefore, this paper introduces a novel method, SMRDDS, to provide rapid and accurate assessment of disintegrated dolomite slope stability. This method incorporates parameters such as disintegrated grade, joint state, groundwater conditions, and excavation methods. The findings reveal that six slopes exhibit stability, while two are considered partially unstable. Notably, the proposed method demonstrates a closer match with the actual conditions and is more time-efficient compared with the BQ and SSPC methods. However, due to the limited research on disintegrated dolomite slopes, the results of the SMRDDS method tend to be conservative as a safety precaution. In conclusion, the SMRDDS method can quickly evaluate the current situation of disintegrated dolomite slopes in the field. This contributes significantly to disaster risk reduction for disintegrated dolomite slopes.

An evidence-based guideline on treating lumbar disc herniation with traditional Chinese medicine.

Lumbar disc herniation (LDH), as one of the most common causes of lower back pain, imposes a heavy economic burden on patients and society. Conservative management is the first-line choice for the majority of LDH patients. Traditional Chinese medicine (TCM) is an important part of conservative treatment and has attracted more and more international attention. Evidence-based guideline. We formed a guideline panel of multidisciplinary experts. The clinical questions were identified on the basis of a systematic literature search and a consensus meeting. We searched the literature for direct evidence on the management of LDH and assessed its certainty-generated recommendations using the grading of recommendations, assessment, development, and evaluation (GRADE) approach. The guideline panel made 20 recommendations, which covered the use of Shentong Zhuyu decoction, Shenzhuo decoction, Simiao San decoction, Duhuo Jisheng decoction, Yaobitong capsule, Yaotongning capsule, Osteoking, manual therapy, needle knife, manual acupuncture, electroacupuncture, Chinese exercise techniques (Tai Chi, Baduanjin, or Yijinjing), and integrative medicine, such as combined non-steroidal anti-inflammatory drugs, neural nutrition, and traction. Recommendations were either strong or weak, or in the form of ungraded consensus-based statement. This is the first LDH treatment guideline for TCM and integrative medicine with a systematic search, synthesis of evidence, and using the GRADE method to rate the quality of evidence. We hope these recommendations can help support healthcare workers caring for LDH patients.

Accurate system for assessment and selection of human embryo after in vitro fertilization

In Vitro Fertilization (IVF) is a complex medical procedure designed to assist individuals in overcoming infertility by facilitating the union of sperm and egg outside the human body. Not all embryos created during the IVF process have the same potential for successful implantation and development. Embryo grading allows embryologists to assess the quality of embryos based on specific criteria, such as cell division, symmetry, and the presence of fragmentation. By assigning grades, they can identify the most viable embryos with the highest likelihood of successful implantation. The conventional methods for embryo grading are time-consuming, labor-intensive, and prone to errors due to their subjective nature. Therefore, there is a pressing need for the development of an accurate and automated grading system to address these challenges. In this study, I propose a human embryo grading system based on machine learning. The proposed system utilizes embryo images as inputs and produces probabilities to predict the success or failure of each embryo. I utilize a convolutional neural network to develop the grading model and introduce a novel hierarchical feature extraction approach to enhance accuracy. Through extensive experiments, I demonstrate that the proposed method surpasses previous approaches with a significant performance margin.

A Two-Stage Approach to the Study of Potato Disease Severity Classification

Early blight and late blight are two of the most prevalent and severe diseases affecting potato crops. Efficient and accurate grading of their severity is crucial for effective disease management. However, existing grading methods are limited to assessing the severity of each disease independently, often resulting in low recognition accuracy and slow grading processes. To address these challenges, this study proposes a novel two-stage approach for the rapid severity grading of both early blight and late blight in potato plants. In this research, two lightweight models were developed: Coformer and SegCoformer. In the initial stage, Coformer efficiently categorizes potato leaves into three classes: those afflicted by early blight, those afflicted by late blight, and healthy leaves. In the subsequent stage, SegCoformer accurately segments leaves, lesions, and backgrounds within the images obtained from the first stage. Furthermore, it assigns severity labels to the identified leaf lesions. To validate the accuracy and processing speed of the proposed methods, we conduct experimental comparisons. The experimental results indicate that Coformer achieves a classification accuracy as high as 97.86%, while SegCoformer achieves an mIoU of 88.50% for semantic segmentation. The combined accuracy of this method reaches 84%, outperforming the Sit + Unet_V accuracy by 1%. Notably, this approach achieves heightened accuracy while maintaining a faster processing speed, completing image processing in just 258.26 ms. This research methodology effectively enhances agricultural production efficiency.

Graph-based multi-level feature fusion network for diabetic retinopathy grading using ultra-wide-field images

Diabetic Retinopathy (DR) grading plays an important role for early intervention in managing the disease. However, conventional grading methods predominantly rely on traditional color fundus photography, which may not fully capture crucial pathological information. In contrast, Ultra-wide-field (UWF) represents a novel non-contact and non-invasive modality, capable of generating images with an unprecedented field of view, ranging from 180∘ to 200∘. This offers distinct advantages over conventional color fundus photography for DR grading. Furthermore, given the subtle distinctions in imaging characteristics among adjacent levels of DR, the grading task becomes exceptionally challenging. Therefore, we propose a new multi-level feature fusion network based on the graph convolution to accomplish the DR grading task using UWF images. Specifically, we first use the progressive multi-level constraint module to iteratively refine features by imposing constraints on model features at different levels. This process serves to minimize the inter-individual distances within the intra-class feature space, ultimately reducing the overlap of feature spaces between adjacent classes. Subsequently, since features at different network levels exhibit distinct semantics and distributions, conventional aggregation techniques, such as splicing and summing, may introduce redundant information and noise interference. To address this issue, we introduce a graph-based feature fusion module, capable of capturing spatial relationships and effectively fusing multi-layered features. Experimental results evaluated on an in-house UWF dataset with 1234 images and a publicly available fundus dataset with 3662 images illustrate that the proposed approach outperforms the state-of-the-art methods and shows promise for DR grading, with a respective accuracy of 0.81 and 0.84.

Noninvasive measurement of ambulatory venous pressure via column interruption duration in chronic venous disease

BackgroundColumn interruption duration (CID) is a noninvasive surrogate for venous refill time (VFT), a parameter used in ambulatory venous pressure measurement. CID is more accurate than invasive VFT measurement because it avoids errors involved with indirect access of the deep system through the dorsal foot vein. The aim of this retrospective single center study is to analyze the clinical usefulness of CID in assessment of chronic venous disease (CVD). MethodsA total of 1551 limbs (777 patients) were referred with CVD symptoms over a 5-year period (2018-2023); CID, air plethysmography, and duplex reflux data were analyzed. Of these limbs, 679 had supine venous pressure data as well. The pathology was categorized as obstruction if supine peripheral venous pressure was >11 mm Hg and as reflux if duplex reflux time in superficial or deep veins was >1 second. CID was measured via Doppler monitoring of flow in the great saphenous vein (GSV) and one of the paired posterior tibial (PT) veins near the ankle in the erect posture. The calf is emptied by rapid inflation cuff. CID is the time interval in seconds when cephalad venous flow in great saphenous vein and posterior tibial veins reappear after calf ejection. A CID <20 seconds in either vein is abnormal similar to the threshold used in VFT measurement. ResultsThirty-two percent of the limbs had obstruction, 17% had reflux, and 37% had a combination; 14% had neither. Higher clinical-etiology-anatomy-pathophysiology (CEAP) clinical classes (C4-6) were prevalent in 44% of pure reflux, significantly less (P < .0001) than in pure obstruction (73%) or obstruction plus reflux subsets (72%), partly reflecting distribution of pathology. There is a progressive increase in supine venous pressure and abnormal CID (P < .0001 and P < .0001, respectively) in successive CEAP clinical class. No such correlation between CEAP and any of the reflux severity grading methods (reflux segment score, Venous Filling Index, and Kistner axial grading) was observed. Abnormal CID (55%) was more prevalent in higher CEAP classes (>4) (P < .0001) than in lesser clinical classes (0-2) or limbs with neither obstruction nor reflux (P < .01). ConclusionsObstruction seems to be a more dominant pathology in clinical progression among CEAP clinical classes than reflux. CID is abnormal in both obstructive and refluxive pathologies and may represent a common end pathway for similar clinical manifestations (eg, ulcer). These data suggest a useful role for CID measurement in clinical assessment of limbs with CVD.

Research of ordinal model fusion based apple grading

Near infrared spectrum has been applied for the rapid non-destructive prediction and classification of the internal soluble solids content (SSC) of apples, due to its rapid, non-destructive and non-polluting nature. However, current apple grading methods do not make full use of the orderliness relationship existing in the apple grading labels. Therefore, ordinal model is introduced in the issue of apples grading based on the internal SSC. In details, the orderliness in the classification model is considered and ordinal regression is combined with apple classification model to establish ordered partition neural network and ordinal regression extreme learning machine, respectively. Meanwhile, to address the problems of poor applicability of single prediction model and the grading uncertainty associated with compulsory segmentation of grading boundaries, a Gaussian mass function generating method is proposed based on the distance between the predicted ordinal class labels and the real grading boundaries, and the multiple models can be fused through the Dempster combination rule, making a fuller description of the uncertainty problem on ordinal class labels prediction, providing a research basis for online non destructive testing grading of apples. Finally, the multiple model fusion process is fully demonstrated by simulation examples, the grading accuracy of Yantai Red Fuji apples is also improved than single ordinal model.

Association between frailty and clinical outcomes in patients undergoing craniotomy—systematic review and meta-analysis of observational studies

BackgroundFrailty in patients undergoing craniotomy may affect perioperative outcomes. There have been a number of studies published in this field; however, evidence is yet to be summarized in a quantitative review format. We conducted a systematic review and meta-analysis to examine the effects of frailty on perioperative outcomes in patients undergoing craniotomy surgery.MethodsOur eligibility criteria included adult patients undergoing open cranial surgery. We searched MEDLINE via Ovid SP, EMBASE via Ovid SP, Cochrane Library, and grey literature. We included retrospective and prospective observational studies. Our primary outcome was a composite of complications as per the Clavien–Dindo classification system. We utilized a random-effects model of meta-analysis. We conducted three preplanned subgroup analyses: patients undergoing cranial surgery for tumor surgery only, patients undergoing non-tumor surgery, and patients older than 65 undergoing cranial surgery. We explored sources of heterogeneity through a sensitivity analysis and post hoc analysis.ResultsIn this review of 63,159 patients, the pooled prevalence of frailty was 46%. The odds ratio of any Clavien–Dindo grade 1–4 complication developing in frail patients compared to non-frail patients was 2.01 [1.90–2.14], with no identifiable heterogeneity and a moderate level of evidence. As per GradePro evidence grading methods, there was low-quality evidence for patients being discharged to a location other than home, length of stay, and increased mortality in frail patients.ConclusionIncreased frailty was associated with increased odds of any Clavien–Dindo 1-4 complication. Frailty measurements may be used as an integral component of risk-assessment strategies to improve the quality and value of neurosurgical care for patients undergoing craniotomy surgery.Ethics and disseminationFormal ethical approval is not needed, as primary data were not collected.Systematic review registrationPROSPERO identification number: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=405240

Newborn and child health national and provincial clinical practice guidelines in South Africa, Nigeria and Malawi: a scoping review

BackgroundLow and middle-income countries remain disproportionately affected by high rates of child mortality. Clinical practice guidelines are essential clinical tools supporting implementation of effective, safe, and cost-effective healthcare. High-quality evidence-based guidelines play a key role in improving clinical management to impact child mortality. We aimed to identify and assess the quality of guidelines for newborn and child health published in South Africa, Nigeria and Malawi in the last 5 years (2017–2022).MethodsWe searched relevant websites (June–July 2022), for publicly available national and subnational de novo or adapted guidelines, addressing newborn and child health in the three countries. Pairs of reviewers independently extracted information from eligible guidelines (scope, topic, target population and users, responsible developers, stakeholder consultation process, adaptation description, assessment of evidence certainty). We appraised guideline quality using the Appraisal of Guidelines for Research & Evaluation (AGREE II) instrument.ResultsWe identified 40-guidelines from the three countries. Of these, 8/40 reported being adopted from a parent guideline. More guidelines (n = 19) provided guidance on communicable diseases than on non-communicable diseases (n = 8). Guidelines were most often developed by national health ministries (n = 30) and professional societies (n = 14). Eighteen guidelines reported on stakeholder consultation; with Nigeria (10/11) and Malawi (3/6) faring better than South Africa (5/23) in reporting this activity. The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was used in 1/7 guidelines that reported assessing certainty of evidence. Overall guidelines scored well on two AGREE II domains: scope and purpose median (IQR) score 68% (IQR 47–83), and clarity of presentation 81% (67–94). Domains critical for ensuring credible guidance scored below 20%: rigour of development 11% (4–32) and editorial independence 6% (0–27).ConclusionNational ministries and professional societies drive guideline activities in Malawi, Nigeria and South Arica. However, the methods and reporting do not adhere to global standards. We found low AGREE II scores for rigour of guideline development and editorial independence and limited use of GRADE or adaptation methods. This undermines the credibility of available guidelines to support evidence-informed care. Our findings highlight the importance of ongoing efforts to strengthen partnerships, capacity, and support for guideline development.

Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review

On a global scale diabetic retinopathy, or DR, is the most common cause of vision loss. Blindness can be prevented with prompt treatment and early identification with retinal screening. Automated analysis of fundus imagery is growing prominently as a means of increasing screening efficiency, thanks to the development of deep learning. This work focuses on deep learning methods for automatic DR severity grading using color channel information. First, we give some basic information on the etiology and color features of DR lesions. Next, a novel support for deep learning technique that use unprocessed color photos as input for comprehensive feature learning. A review is mentioned on color space encodings, data augmentation methods. A summary of the evaluation parameters and public databases that were utilized to benchmark DR techniques are provided. The objective of how color channel information in retinal pictures can be efficiently utilized by deep learning models for automated DR screening has been discussed with statistical support.

Glioma grading using an optimized T1-weighted dynamic contrast-enhanced magnetic resonance imaging paradigm

BackgroundGlioma grading is a critical procedure for selecting the most effective treatment policy. Biopsy result is the gold standard method for glioma grading, but inherent sampling errors in the biopsy procedure could lead to tumor misclassification.AimThis study evaluated grading performances of a more comprehensive collection of the physiological indices quantified using an optimized dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) paradigm for glioma grading.MethodsThirty-five patients with glioma underwent DCE-MR imaging to evaluate the grading performances of DCE-MRI-derived physiological indices. The statistical differences in the physiological indices between the different grades of gliomas were studied, and the grading performances of these parameters were evaluated using the leave-one-out cross-validation method.ResultsThere were significant statistical differences in DCE-MRI-derived physiological indices between the different grades of gliomas. The mean rCBVs for grade II (low-grade glioma, LGG), grade III, grade IV, and high-grade (HGG) gliomas were 2.03 ± 0.78, 3.61 ± 1.64, 7.14 ± 3.19, and 5.28 ± 3.02, respectively. The mean rCBFs of 1.94 ± 0.97, 2.67 ± 0.96, 4.57 ± 1.77, and 3.57 ± 1.68 were, respectively, quantified for grade II (LGG), grade III, grade IV, and high-grade gliomas. The leave-one-out cross-validation method indicates that the grades of glioma tumors could be determined based on a specific threshold for each physiological index; for example, the optimal cutoff values for rCBF, rCBV, Ktrans, Kep, and Vp indices to distinguish between HGGs and LGGs were 2.11, 2.80, 0.025 mL/g min, 0.29 min−1, and 0.065 mL/g, respectively.ConclusionsFrom the results, it could be concluded that glioma grades could be determined using DCE-MRI-derived physiological indices with an acceptable agreement with histopathological results.