Grading Method Research Articles

Analysis of Finger Photo Pulse Plethysmography and Body Composition among Low Back Pain Subjects with Lumbar Disc Degenerative Diseases Diagnosed by MRI Analysis

Lumbar disc degenerative disc disease, accompanied by back pain, is a prominent health concern in society. The purpose of the study was to investigate the connection of arterial stiffness and body composition in the patients suffering low back pain with lumbar disc degenerative disorders. A comparative study was performed on 197 patients, both male and female, between the ages of 30 and 70, who were experiencing low back pain. These patients have undergone magnetic resonance imaging of the lumbosacral region to evaluate the intervertebral disc's nucleus and annulus as well as its signal intensity and height. The Pfirrmann grading method was used to determine the extent of disc degeneration. The statistical analysis revealed no significant link between overall body fat and stiffness index in disc bulging and nerve compression groups. However, the group experiencing nerve compression demonstrated a statistically significant association between visceral fat and reflection index in individuals who were overfat and obese. In addition, there was a notable correlation between PWV and the reflection index of the disc bulging groups. So, whole body fat and PWV had a different impact on disc degeneration. The PWV has a robust positive correlation with both the stiffness index and reflection index. The presence of subcutaneous fat and vascular stiffness may not have any impact on low back pain in patients with disc bulging and nerve compression. Early identification of vascular stiffness and study of body composition serve as initial measures to avoid the progression of disc degeneration.

MiRNAs in radiotherapy resistance of cancer; a comprehensive review.

While intensity-modulated radiation therapy-based comprehensive therapy increases outcomes, cancer patients still have a low five-year survival rate and a high recurrence rate. The primary factor contributing to cancer patients' poor prognoses is radiation resistance. A class of endogenous non-coding RNAs, known as microRNAs (miRNAs), controls various biological processes in eukaryotes. These miRNAs influence tumor cell growth, death, migration, invasion, and metastasis, which controls how human carcinoma develops and spreads. The correlation between the unbalanced expression of miRNAs and the prognosis and sensitivity to radiation therapy is well-established. MiRNAs have a significant impact on the regulation of DNA repair, the epithelial-to-mesenchymal transition (EMT), and stemness in the tumor radiation response. But because radio resistance is a complicated phenomena, further research is required to fully comprehend these mechanisms. Radiation response rates vary depending on the modality used, which includes the method of delivery, radiation dosage, tumor stage and grade, confounding medical co-morbidities, and intrinsic tumor microenvironment. Here, we summarize the possible mechanisms through which miRNAs contribute to human tumors' resistance to radiation.

Comparison of target agent treatment strategies for platinum-resistant recurrent ovarian cancer: A Bayesian network meta-analysis.

We aimed to compare 7 newer immunotherapies and targeted therapies for platinum-resistant relapsed ovarian cancer. We conducted a comprehensive search of PubMed, EMBASE, and Cochrane Library electronic databases for phase III trials involving platinum-resistant recurrent ovarian cancer (PRrOC) patients treated with immunotherapy or targeted therapy in combination with chemotherapy. The quality of the included trials was assessed using the GRADE method. The primary outcome of comparison was progression-free survival, and secondary outcomes included overall survival and safety. This analysis included 7 randomized phase III controlled trials, encompassing 2485 PRrOC patients. Combining bevacizumab plus chemotherapy and lurbinectedin demonstrated statistically significant differences in progression-free survival compared to all other regimens of interest. However, no statistically significant differences were observed in the overall survival. Nivolumab and mirvetuximab exhibited fewer serious adverse events than the other regimens of interest. Our findings indicate that bevacizumab combined with chemotherapy and lurbinectedin monotherapy has significant efficacy in patients with PRrOC. For patients with PRrOC who have exhausted treatment options, nivolumab and mirvetuximab may be considered as alternatives because of their better safety profiles.

#1103 Delayed initiation or reduced initial dose of calcineurin-inhibitors for kidney transplant recipients: a systematic review and meta-analysis

Abstract Background and Aims Delayed graft function (DGF) is more common in donors after cardiac death, especially expanded criteria donors and those with longer cold ischaemia time, older or with an increased serum creatinine. There is no agreement on the optimal immunosuppressive approach in patients at increased risk of DGF, with strategies including a delayed introduction of calcineurin inhibitors (CNI) or initial low dose CNI. Our aim was to evaluate the benefits and harms of delayed initiation of CNI or reduced CNI dose as initial immunosuppression therapy for kidney transplant (KT) recipients at high risk of DGF. Method We searched the Cochrane Kidney and Transplant Register of Studies up to 07 March 2023 through contact with the Information Specialist using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Register (ICTRP) Search Portal, and ClinicalTrials.gov. We included all randomized controlled trials (RCTs) and quasi-RCTs evaluating early vs delayed initiation of CNI or reduced vs standard initial dose of CNI in kidney transplant (KT) recipients at high risk of DGF. Three authors independently assessed study eligibility, and two assessed the risk of bias, certainty of evidence, extracted the data, and performed the analysis. Results were reported as risk ratios with 95% confidence intervals for dichotomous outcomes and as mean difference with 95% CI for continuous outcomes. Statistical analysis was performed using the random-effects model. Risk of bias was assessed with RoB2 and the certainty of the evidence according to GRADE methods. Results Thirteen studies were included (2386 randomized participants). Incidence of DGF (RR 1.09, 95% CI 0.89 to 1.32; I2 = 18%) and acute rejection (RR 0.98, 95% CI 0.72 to 1.33; I2 = 54%) were similar between early and delayed introduction of CNI, as well as between low vs standard dose of CNI (DGF: RR 1.16, 95% CI 0.90 to 1.50; I2 = 9%; acute rejection: RR 0.70, 95% CI 0.43 to 1.14; I2 = 58%). There were also no differences in secondary outcomes such as graft function, graft loss, risk of death, or infection. Conclusion In patients at high risk of DGF, the strategy of delaying CNI introduction or starting at a lower dose does not reduce the risk of DGF.

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning.

Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

Using optical coherence tomography to assess luster of pearls: technique suitability and insights

Luster is one of the vital indexes in pearl grading. To find a fast, nondestructive, and low-cost grading method, optical coherence tomography (OCT) is introduced to predict the luster grade through the texture features. After background removal, flattening, and segmentation, the speckle pattern of the region of interest is described by seven kinds of feature textures, including center-symmetric auto-correlation (CSAC), fractal dimension (FD), Gabor, gray level co-occurrence matrix (GLCM), histogram of oriented gradients (HOG), laws texture energy (LAWS), and local binary patterns (LBP). To find the relations between speckle-derived texture features and luster grades, four Four groups of pearl samples were used in the experiment to detect texture differences based on support vector machines (SVMs) and random forest classifier (RFC)) for investigating the relations between speckle-derived texture features and luster grades. The precision, recall, F1-score, and accuracy are more significant than 0.9 in several simulations, even after dimension reduction. This demonstrates that the texture feature from OCT images can be applied to class the pearl luster based on speckle changes.

Cross-modality transfer learning with knowledge infusion for diabetic retinopathy grading.

Ultra-wide-field (UWF) fundus photography represents an emerging retinal imaging technique offering a broader field of view, thus enhancing its utility in screening and diagnosing various eye diseases, notably diabetic retinopathy (DR). However, the application of computer-aided diagnosis for DR using UWF images confronts two major challenges. The first challenge arises from the limited availability of labeled UWF data, making it daunting to train diagnostic models due to the high cost associated with manual annotation of medical images. Secondly, existing models' performance requires enhancement due to the absence of prior knowledge to guide the learning process. By leveraging extensively annotated datasets within the field, which encompass large-scale, high-quality color fundus image datasets annotated at either image-level or pixel-level, our objective is to transfer knowledge from these datasets to our target domain through unsupervised domain adaptation. Our approach presents a robust model for assessing the severity of diabetic retinopathy (DR) by leveraging unsupervised lesion-aware domain adaptation in ultra-wide-field (UWF) images. Furthermore, to harness the wealth of detailed annotations in publicly available color fundus image datasets, we integrate an adversarial lesion map generator. This generator supplements the grading model by incorporating auxiliary lesion information, drawing inspiration from the clinical methodology of evaluating DR severity by identifying and quantifying associated lesions. We conducted both quantitative and qualitative evaluations of our proposed method. In particular, among the six representative DR grading methods, our approach achieved an accuracy (ACC) of 68.18% and a precision (pre) of 67.43%. Additionally, we conducted extensive experiments in ablation studies to validate the effectiveness of each component of our proposed method. In conclusion, our method not only improves the accuracy of DR grading, but also enhances the interpretability of the results, providing clinicians with a reliable DR grading scheme.

Evaluating the impact of indoor aerosols on the performance of real-time radon sensors

Radon, a naturally occurring radioactive gas, poses a significant health risk by accumulating in buildings and potentially leading to lung cancer. Depending on building construction and geographical location, radon levels can vary substantially both within individual buildings and between different buildings. While previous studies have primarily focused on the impact of temperature and relative humidity on radon devices, the influence of aerosols remains largely unexplored. This paper presents a comprehensive evaluation of the influence of indoor aerosol sources on the performance of real-time radon sensors, encompassing consumer, medium, and research-grade devices. Measurements were performed at relatively low (300 Bq/m3) and high (2′000–3′000 Bq/m3) radon levels in a controlled environment—a stable atomic shelter with constant temperature and humidity conditions. Six different aerosols sources were introduced to produce aerosols of different sizes and concentrations. The results suggest that the tested indoor aerosols did not significantly influence the performance of radon devices, irrespective of their grade or detection method. Consequently, sensor performance and the radon levels being investigated may exert a more significant influence on the obtained results than aerosol levels alone. This paper provides valuable insights into the influence of indoor environment on the performance of radon measuring devices, underscoring the importance of understanding their utility and application scope for researchers, professionals, and the general public alike.

A machine learning method for precise detection of spine bone mineral density

Osteoporosis is prevalent among the elderly and requires precise diagnostic approaches for effective treatment and prevention. This work introduces a machine learning-based method for the accurate grading of osteoporosis via the analysis of high-resolution computed tomography (HRCT) images of the spine. This approach allows for the precise extraction of the trabecular component of vertebral bodies, thereby enabling accurate measurement of bone density. Evaluated in 183 individuals, our method demonstrated enhanced stability in bone density prediction and reduced bias when juxtaposed with traditional random sampling techniques. Despite the inherent risk of overestimation, the machine learning model more accurately approximates the actual bone quality compared to conventional clinical methods, which typically involve the random extraction of 3–4 trabecular bone slices from the spine. These findings offer a novel approach to the clinical assessment of osteoporosis, underscoring the significant potential of integrating machine learning into existing medical image analysis workflows.

Linee guida per la gestione del paziente adulto con diabete o con iperglicemia ricoverato in setting clinico non critico

Background/aim: This editorial is aimed at providing a reference for the management of adult patients with diabetes or hyperglycemia hospitalized in a non-critical clinical setting. Methods and results: The Italian guidelines have recently been published (https://www.iss.it/-/snlg-gestione-paziente-adulto-con-diabete-setting-non-critico) developed following the GRADE method by a panel nominated by Società Italiana di Medicina Interna, Associazione Medici Diabetologi, Società Italiana di Diabetologia, Federazione Associazioni dei Dirigenti Ospedalieri Internisti, Società Italiana di Gerontologia e Geriatria e Associazione Nazionale Infermieri di Medicina. Conclusions: The recommendations are conceived as indications for healthcare professionals responsible for diabetes care in non-critical clinical settings, taking into account primarily the clinical needs of people with diabetes and secondarily the organization of the healthcare system.

A systematic comparison of deep learning methods for Gleason grading and scoring

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.

Development of the first Iranian clinical practice guidelines for the diagnosis, treatment, and secondary prevention of acute coronary syndrome.

This article introduces the first national guidelines for the management including diagnosis, treatment, and secondary prevention of acute coronary syndrome (ACS) in Iran. The members of the guideline development group (GDG) were specialists and experts in fields related to ACS and were affiliated with universities of medical sciences or scientific associations in the country. They carefully examined the evidence and clinical concerns related to ACS management and formulated 13 clinical questions that were sent to systematic review group who developed related evidence using Grade method. Finally the GDG developed the recommendations and suggestions of the guideline. The first three questions in the guideline focus on providing recommendations for handling a patient who experience chest pain at home, in a health house or center, during ambulance transportation, and upon arrival at the emergency department (ED) as well as the initial diagnostic measures in the ED. Subsequently, the recommendations related to the criteria for categorizing patients into low, intermediate and high-risk groups are presented. The guideline addressed primary treatment measures for ACS patients in hospitals with and without code 247 or having primary percutaneous coronary intervention (PCI) facilities, and the appropriate timing for PCI based on the risk assessment. In addition, the most efficacious antiplatelet medications for ACS patients in the ED as well as its optimal duration of treatment are presented. The guideline details the recommendations for therapeutic interventions in patients with ACS and acute heart failure, cardiogenic shock, myocardial infarction with nonobstructive coronary arteries (MINOCA), multivessel occlusion, as well as the indication for prescribing a combined use of anticoagulants and antiplatelet during hospitalization and upon discharge. Regarding secondary prevention, while emphasizing the referral of these patients to rehabilitation centers, other interventions that include pharmaceutical and nonpharmacological ones are addressed, In addition, necessary recommendations for enhancing lifestyle and posthospital discharge pharmaceutical treatments, including their duration, are provided. There are specific recommendations and suggestions for subgroups, such as patients aged over 75 years and individuals with heart failure, diabetes, and chronic kidney disease. Developing guidelines for ACS diagnosis, treatment and secondary prevention according to the local context in Iran can improve the adherence of our health care providers, patients health, and policy makers plans.

Non‐destructive grading technique for mangoes using a flexible impedance sensing system and YOLOv5s_CBAM

AbstractFlexible sensors for food quality control are experiencing rapid development. The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.Practical applicationsThe purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.

Evaluation of reduced-scale railway ballast particle size effects for different test sample sizes using monotonic triaxial tests

This study rigorously investigated the mechanical characteristics of railway ballast using largescale triaxial (LST) testing, focusing on shear strength, stress–strain relationships, and volumetric alterations by altering particle sizes under controlled conditions. Samples with a size ratio (D/dmax) of 4.7 are typically used in LST testing. However, the ballast used in Thailand typically features a maximum grain size of 70.00 mm, whereas LST testing requires samples with diameter exceeding 300 mm, which poses logistical and material challenges. To mitigate this issue, our test introduced a parallel grading method to decrease sample size while preserving essential ballast properties and morphological features. This test applied two grain-size distributions: normal grain-size distribution (NGD) and parallel grain-size distribution (PGD). The study aimed to ascertain whether the smaller-sized samples can still provide accurate assessments of mechanical behaviors. The outcomes demonstrated that both NGD and PGD approaches yielded similar stress–strain behaviors and cohesion values. A marginal difference in friction angle values of 4° was noted, suggesting the occurrence of minor variances in shear resistance, which did not significantly affect the overall mechanical properties. The findings validate the effectiveness of the parallel grading technique, thus offering practical implications for testing procedures in railway engineering.

Fully Automatic Fine-Grained Grading of Lumbar Intervertebral Disc Degeneration Using Regional Feature Recalibration Network.

Accurate fine-grained grading of lumbar intervertebral disc (LIVD) degeneration is essential for the diagnosis and treatment design of high-incidence low back pain. However, the grading accuracy is still challenged by lacking the fine-grained degenerative details, which is mainly due to the existing grading methods are easily dominated by the salient nucleus pulposus regions in LIVD, overlooking the inconspicuous degeneration changes of the surrounding structures. In this study, a novel regional feature recalibration network (RFRecNet) is proposed to achieve accurate and reliable LIVD degeneration grading. Detection transformer (DETR) is first utilized to detect all LIVDs and then input to the proposed RFRecNet for the fine-grained grading. To obtain sufficient features from both the salient nucleus pulposus and the surrounding regions, a regional cube-based feature boosting and suppression (RC-FBS) module is designed to adaptively recalibrate the feature extraction and utilization from the various regions in LIVD, and a feature diversification (FD) module is proposed to capture the complementary semantic information from the multi-scale features for the comprehensive fine-grained degeneration grading. Extensive experiments were conducted on a clinically collected dataset, which consists of 500 MR scans with a total of 10225 LIVDs. An average grading accuracy of 90.5%, specificity of 97.5%, sensitivity of 90.8%, and Cohen's kappa correlation coefficient of 0.876 are obtained, which indicate that the proposed framework is promising to provide doctors with reliable and consistent fine-grained quantitative evaluation results of the LIVD degeneration conditions for the optimal surgical plan design.

Interventional Embolization of Unilateral Cavernous Sinus With ONXY Glue Combined With Coils for the Treatment of Bilateral Dural Arteriovenous Fistula.

To summarize the clinical experience of unilateral sponge sinus embolization of cavernous sinus dural arteriovenous fistula (CS DAVF) via jugular vein access with ONXY gel and spring coil intervention. The authors retrospectively analyzed the clinical data of 3 patients who were treated with unilateral cavernous sinus embolization for bilateral cavernous sinus dural arteriovenous fistula (CS DAVF) from October 2020 to May 2022 in our department by using transjugular vein access spring coil + ONXY gel interventional embolization. Immediate postoperative angiography suggested that the cavernous sinus fistula was completely embolized in all 3 patients, and cerebral angiography was carried out from 6 to 12 months after the operation and assessed using the Class grading method, and there was no recurrence in any of the 3 patients. Transvenous access ONXY adhesive combined with spring coil interventional embolization of unilateral cavernous sinus to cure bilateral cavernous sinus dural arteriovenous fistula is feasible, which can avoid the excessive use of spring coils and ONXY adhesive, and it is the key to the effective cure of DAVF in cavernous sinus area and to reduce the complication.

Deep Learning Model for Grading and Localization of Lumbar Disc Herniation on Magnetic Resonance Imaging.

Methods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time-consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities. To develop an interpretable DL model capable of grading and localizing LDH. Retrospective. 1496 patients (M/F: 783/713) were evaluated, and randomly divided into training (70%), validation (10%), and test (20%) sets. 1.5T MRI for axial T2-weighted sequences (spin echo). The training set was annotated by three spinal surgeons using the Michigan State University classification to train the DL model. The test set was annotated by a spinal surgery expert (as ground truth labels), and two spinal surgeons (comparison with the trained model). An external test set was employed to evaluate the generalizability of the DL model. Calculated intersection over union (IoU) for detection consistency, utilized Gwet's AC1 to assess interobserver agreement, and evaluated model performance based on sensitivity and specificity, with statistical significance set at P < 0.05. The DL model achieved high detection consistency in both the internal test dataset (grading: mean IoU 0.84, recall 99.6%; localization: IoU 0.82, recall 99.5%) and external test dataset (grading: 0.72, 98.0%; localization: 0.71, 97.6%). For internal testing, the DL model (grading: 0.81; localization: 0.76), Rater 1 (0.88; 0.82), and Rater 2 (0.86; 0.83) demonstrated results highly consistent with the ground truth labels. The overall sensitivity of the DL model was 87.0% for grading and 84.0% for localization, while the specificity was 95.5% and 94.4%. For external testing, the DL model showed an appreciable decrease in consistency (grading: 0.69; localization: 0.66), sensitivity (77.2%; 76.7%), and specificity (92.3%; 91.8%). The classification capabilities of the DL model closely resemble those of spinal surgeons. For future improvement, enriching the diversity of cases could enhance the model's generalization. Stage 2.

Research on joint scheduling method of order grading and machine maintenance

AbstractIn the multi‐variety and large‐scale order production mode, enterprises must balance delivery deadlines and maintain customer satisfaction while also considering the health status of machines. Therefore, the authors propose a method for jointly optimising production scheduling and machine maintenance. Before machine processing, an order value grading and sorting model and a machine health‐status group partitioning model are constructed to classify orders into different production value levels and machines into different health‐status groups, respectively. During machine processing, based on the Weibull distribution theory, a ‘health evaluation function value’ constraint machine preventive maintenance (PM) model and PM strategy are proposed to account for the changing health status of machines; these are integrated with the order allocation machine strategy as decision‐making elements in the production schedule. Finally, two case studies are used to verify the effectiveness of this proposed model and method. The results show that compared to general scheduling schemes, the proposed method can reduce total delay and improve customer satisfaction. Additionally, the PM plan proposed in this method can improve production efficiency and line stability compared to periodic maintenance.

A discrepancy-aware self-distillation method for multi-modal glioma grading

Gliomas are the most common intracranial primary tumors. Accurate grading of gliomas is crucial in determining treatment options and prognosis. Clinicians conventionally rely on multiple Magnetic Resonance Imaging (MRI) sequences for accurate glioma assessment. Traditional deep learning methods typically utilize the individual MRI sequence with the annotations of Region of Interest (ROIs), incurring substantial manual effort while losing the complementary information provided by other sequences. This task is inherently a standard multi-modal problem. However, existing methods often adopt complex multi-stream networks for feature extraction and fusion, leading to high resource demands and potential feature redundancy. To address the above challenge, a discrepancy-aware self-distillation method is proposed for multi-modal glioma grading, which requires only a single-stream network to concurrently analyze multiple MRI sequences without auxiliary ROIs. The first Modality Discrepancy-aware Fusion (MDF) module considers the MRI imaging differentiation and widens the inter-modal contrasts, allowing the modality-specific features to be highlighted and the modality-invariant features to be diminished. Furthermore, the proposed Class Activation Self-Distillation (CASD) strategy leverages the generated Class Activation Maps (CAMs) as dark knowledge for the distillation process. This guides shallow layers to focus on the category discriminative features specifically within the lesion region. Extensive experiments were conducted on the BraTS2018 and BraTS2019 datasets to evaluate the effectiveness of our method. The results show that our method outperforms other relevant glioma grading and multi-modal fusion methods on both datasets.

RELIABILITY OF CLINICAL GRADING OF DIABETIC RETINOPATHY COMPARED WITH GRADING OF ULTRA-WIDEFIELD IMAGES.

To evaluate the reliability of clinical grading of diabetic retinopathy (DR) severity compared with grading on ultra-widefield pseudocolor fundus (UWF-CF) and ultra-widefield fluorescein angiography (UWF-FA) images and their relative detection of sight-threatening DR and referable DR. A total of 184 diabetic eyes were analyzed. UWF-CF and UWF-FA images were graded based on the International Clinical Diabetic Retinopathy severity scale. Agreement between clinical and UWF-based severity grading was evaluated using Cohen's kappa coefficient. The rate of sight-threatening DR and referable DR was evaluated for each grading method. Moderate agreement was found between clinical grading and UWF-CF (k = 0.456, P < 0.001) and between UWF-CF and UWF-FA (k = 0.443, P < 0.001). The agreement between clinical grading and UWF-FA was fair (k = 0.397, P < 0.001). UWF-based grading identified a higher DR grade in 56 eyes (30%) on UWF-CF and 85 eyes (46.2%) on UWF-FA. Compared with clinical grading, UWF-FA detected a higher rate of sight-threatening DR (44%; 81/184 vs. 22.3%; 41/184), while UWF-CF detected more referable eyes (58.1%; 107/184 vs. 45.65%; 84/184). Ultra-widefield pseudocolor fundus is a valuable tool for identifying referable eyes and can be a useful, noninvasive adjunct to clinical grading. The results suggest that UWF-FA is particularly useful for detecting unsuspected sight-threatening DR in eyes with clinically referable DR.