Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

UNASSIGNED: Diabetic retinopathy (DR), a sight-threatening ocular complication of diabetes mellitus, is one of the main causes of blindness in the working-age population. Dyslipidemia is a potential risk factor for the development or worsening of DR, with conflicting evidence in epidemiological studies. Fenofibrate, an antihyperlipidemic agent, has lipid-modifying and pleiotropic (non-lipid) effects that may lessen the incidence of microvascular events.
UNASSIGNED: Relevant studies were identified through a PubMed/MEDLINE search spanning the last 20 years, using the broad term \"diabetic retinopathy\" and specific terms \"fenofibrate\" and \"dyslipidemia\". References cited in these studies were further examined to compile this mini-review. These pivotal investigations underwent meticulous scrutiny and synthesis, focusing on methodological approaches and clinical outcomes. Furthermore, we provided the main findings of the seminal studies in a table to enhance comprehension and comparison.
UNASSIGNED: Growing evidence indicates that fenofibrate treatment slows DR advancement owing to its possible protective effects on the blood-retinal barrier. The protective attributes of fenofibrate against DR progression and development can be broadly classified into two categories: lipid-modifying effects and non-lipid-related (pleiotropic) effects. The lipid-modifying effect is mediated through peroxisome proliferator-activated receptor-α activation, while the pleiotropic effects involve the reduction in serum levels of C-reactive protein, fibrinogen, and pro-inflammatory markers, and improvement in flow-mediated dilatation. In patients with DR, the lipid-modifying effects of fenofibrate primarily involve a reduction in lipoprotein-associated phospholipase A2 levels and the upregulation of apolipoprotein A1 levels. These changes contribute to the anti-inflammatory and anti-angiogenic effects of fenofibrate. Fenofibrate elicits a diverse array of pleiotropic effects, including anti-apoptotic, antioxidant, anti-inflammatory, and anti-angiogenic properties, along with the indirect consequences of these effects. Two randomized controlled trials-the Fenofibrate Intervention and Event Lowering in Diabetes and Action to Control Cardiovascular Risk in Diabetes studies-noted that fenofibrate treatment protected against DR progression, independent of serum lipid levels.
UNASSIGNED: Fenofibrate, an oral antihyperlipidemic agent that is effective in decreasing DR progression, may reduce the number of patients who develop vision-threatening complications and require invasive treatment. Despite its proven protection against DR progression, fenofibrate treatment has not yet gained wide clinical acceptance in DR management. Ongoing and future clinical trials may clarify the role of fenofibrate treatment in DR management.

UNASSIGNED: Vascular endothelial growth factor (VEGF) is the primary substance involved in retinal barrier breach. VEGF overexpression may cause diabetic macular edema (DME). Laser photocoagulation of the macula is the standard treatment for DME; however, recently, intravitreal anti-VEGF injections have surpassed laser treatment. Our aim was to evaluate the efficacy of intravitreal injections of aflibercept or ranibizumab for managing treatment-naive DME.
UNASSIGNED: This single-center, retrospective, interventional, comparative study included eyes with visual impairment due to treatment-naive DME that underwent intravitreal injection of either aflibercept 2 mg/0.05 mL or ranibizumab 0.5 mg/0.05 mL at Al-Azhar University Hospitals, Egypt between March 2023 and January 2024. Demographic data and full ophthalmological examination results at baseline and 1, 3, and 6 months post-injection were collected, including the best-corrected distance visual acuity (BCDVA) in logarithm of the minimum angle of resolution (logMAR) notation, slit-lamp biomicroscopy, dilated fundoscopy, and central subfield thickness (CST) measured using spectral-domain optical coherence tomography.
UNASSIGNED: Overall, the 96 eyes of 96 patients with a median (interquartile range [IQR]) age of 57 (10) (range: 20-74) years and a male-to-female ratio of 1:2.7 were allocated to one of two groups with comparable age, sex, diabetes mellitus duration, and presence of other comorbidities (all P >0.05). There was no statistically significant difference in baseline diabetic retinopathy status or DME type between groups (both P >0.05). In both groups, the median (IQR) BCDVA significantly improved from 0.7 (0.8) logMAR at baseline to 0.4 (0.1) logMAR at 6 months post-injection (both P = 0.001), with no statistically significant difference between groups at all follow-up visits (all P >0.05). The median (IQR) CST significantly decreased in the aflibercept group from 347 (166) µm at baseline to 180 (233) µm at 6 months post-injection, and it decreased in the ranibizumab group from 360 (180) µm at baseline to 190 (224) µm at 6 months post-injection (both P = 0.001), with no statistically significant differences between groups at all follow-up visits (all P >0.05). No serious adverse effects were documented in either group.
UNASSIGNED: Ranibizumab and aflibercept were equally effective in achieving the desired anatomical and functional results in patients with treatment-naïve DME in short-term follow-up without significant differences in injection counts between both drugs. Larger prospective, randomized, double-blinded trials with longer follow-up periods are needed to confirm our preliminary results.

UNASSIGNED: The American Optometric Association defines computer vision syndrome (CVS), also known as digital eye strain, as \"a group of eye- and vision-related problems that result from prolonged computer, tablet, e-reader and cell phone use\". We aimed to create a well-structured, valid, and reliable questionnaire to determine the prevalence of CVS, and to analyze the visual, ocular surface, and extraocular sequelae of CVS using a novel and smart self-assessment questionnaire.
UNASSIGNED: This multicenter, observational, cross-sectional, descriptive, survey-based, online study included 6853 complete online responses of medical students from 15 universities. All participants responded to the updated, online, fourth version of the CVS questionnaire (CVS-F4), which has high validity and reliability. CVS was diagnosed according to five basic diagnostic criteria (5DC) derived from the CVS-F4. Respondents who fulfilled the 5DC were considered CVS cases. The 5DC were then converted into a novel five-question self-assessment questionnaire designated as the CVS-Smart.
UNASSIGNED: Of 10 000 invited medical students, 8006 responded to the CVS-F4 survey (80% response rate), while 6853 of the 8006 respondents provided complete online responses (85.6% completion rate). The overall CVS prevalence was 58.78% (n = 4028) among the study respondents; CVS prevalence was higher among women (65.87%) than among men (48.06%). Within the CVS group, the most common visual, ocular surface, and extraocular complaints were eye strain, dry eye, and neck/shoulder/back pain in 74.50% (n = 3001), 58.27% (n = 2347), and 80.52% (n = 3244) of CVS cases, respectively. Notably, 75.92% (3058/4028) of CVS cases were involved in the Mandated Computer System Use Program. Multivariate logistic regression analysis revealed that the two most statistically significant diagnostic criteria of the 5DC were ≥2 symptoms/attacks per month over the last 12 months (odds ratio [OR] = 204177.2; P <0.0001) and symptoms/attacks associated with screen use (OR = 16047.34; P <0.0001). The CVS-Smart demonstrated a Cronbach\'s alpha reliability coefficient of 0.860, Guttman split-half coefficient of 0.805, with perfect content and construct validity. A CVS-Smart score of 7-10 points indicated the presence of CVS.
UNASSIGNED: The visual, ocular surface, and extraocular diagnostic criteria for CVS constituted the basic components of CVS-Smart. CVS-Smart is a novel, valid, reliable, subjective instrument for determining CVS diagnosis and prevalence and may provide a tool for rapid periodic assessment and prognostication. Individuals with positive CVS-Smart results should consider modifying their lifestyles and screen styles and seeking the help of ophthalmologists and/or optometrists. Higher institutional authorities should consider revising the Mandated Computer System Use Program to avoid the long-term consequences of CVS among university students. Further research must compare CVS-Smart with other available metrics for CVS, such as the CVS questionnaire, to determine its test-retest reliability and to justify its widespread use.

UNASSIGNED: The research project focuses on the creation and assessment of an innovative computer vision system designed to identify dental irregularities in individuals undergoing orthodontic treatment.
UNASSIGNED: To establish the computer vision system, a comprehensive dataset of dental images was collected, encompassing various orthodontic cases. The system\'s algorithm was trained to recognize patterns indicative of common dental anomalies, such as malocclusions, spacing issues, and misalignments. Rigorous testing and refinement of the algorithm were conducted to enhance its accuracy and reliability.
UNASSIGNED: The validation of the system was carried out using the dental records and images of the 40 patients. The computer vision system\'s performance was evaluated against assessments made by experienced orthodontists. The results demonstrated a commendable level of concurrence between the system\'s automated detections and the orthodontists\' evaluations, suggesting its potential as a valuable diagnostic tool.
UNASSIGNED: In conclusion, the development and validation of this novel computer vision system exhibit promising outcomes in its ability to automatically detect dental anomalies in orthodontic patients.

UNASSIGNED: The widespread use of minimally invasive surgery generates vast amounts of potentially useful data in the form of surgical video. However, raw video footage is often unstructured and unlabeled, thereby limiting its use. We developed a novel computer-vision algorithm for automated identification and labeling of surgical steps during robotic-assisted radical prostatectomy (RARP).
UNASSIGNED: Surgical videos from RARP were manually annotated by a team of image annotators under the supervision of 2 urologic oncologists. Full-length surgical videos were labeled to identify all steps of surgery. These manually annotated videos were then utilized to train a computer vision algorithm to perform automated video annotation of RARP surgical video. Accuracy of automated video annotation was determined by comparing to manual human annotations as the reference standard.
UNASSIGNED: A total of 474 full-length RARP videos (median 149 minutes; IQR 81 minutes) were manually annotated with surgical steps. Of these, 292 cases served as a training dataset for algorithm development, 69 cases were used for internal validation, and 113 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between artificial intelligence‒enabled automated video analysis and manual human video annotation was 92.8%. Algorithm accuracy was highest for the vesicourethral anastomosis step (97.3%) and lowest for the final inspection and extraction step (76.8%).
UNASSIGNED: We developed a fully automated artificial intelligence tool for annotation of RARP surgical video. Automated surgical video analysis has immediate practical applications in surgeon video review, surgical training and education, quality and safety benchmarking, medical billing and documentation, and operating room logistics.

Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)-involving automation of dataset labelling-represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers.

BACKGROUND: Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions.
METHODS: This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions.
RESULTS: The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions.
CONCLUSIONS: AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

The quality of beef products relies on the presence of a cherry red color, as any deviation toward brownish tones indicates a loss in quality. Existing studies typically analyze individual color channels separately, establishing acceptable ranges. In contrast, our proposed approach involves conducting a multivariate analysis of beef color changes using white-box machine learning techniques. Our proposal encompasses three phases. (1) We employed a Computer Vision System (CVS) to capture the color of beef pieces, implementing a color correction pre-processing step within a specially designed cabin. (2) We examined the differences among three color spaces (RGB, HSV, and CIELab*) (3) We evaluated the performance of three white-box classifiers (decision tree, logistic regression, and multivariate normal distributions) for predicting color in both fresh and non-fresh beef. These models demonstrated high accuracy and enabled a comprehensive understanding of the prediction process. Our results affirm that conducting a multivariate analysis yields superior beef color prediction outcomes compared to the conventional practice of analyzing each channel independently.

Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this work, we used the Deep Learning (DL) architecture named U-Net to estimate water holding capacity (WHC) from filter paper images of pork samples obtained using the press method. We evaluated the ability of the U-Net to segment the different regions of the WHC images and, since the images are much larger than the traditional input size of the U-Net, we also evaluated its performance when we change the input size. Results show that U-Net can be used to segment the external and internal areas of the WHC images with great precision, even though the difference in the appearance of these areas is subtle.