Hasil untuk "Nutritional diseases. Deficiency diseases"

Md. Rokon Mia, Rakib Hossain Sajib, Abdullah Al Noman et al.

Early detection of rice leaf diseases is critical, as rice is a staple crop supporting a substantial share of the world's population. Timely identification of these diseases enables more effective intervention and significantly reduces the risk of large-scale crop losses. However, traditional deep learning models primarily rely on cross entropy loss, which often struggles with high intra-class variance and inter-class similarity, common challenges in plant pathology datasets. To tackle this, we propose a dual-loss framework that combines Center Loss and ArcFace Loss to enhance fine-grained classification of rice leaf diseases. The method is applied into three state-of-the-art backbone architectures: InceptionNetV3, DenseNet201, and EfficientNetB0 trained on the public Rice Leaf Dataset. Our approach achieves significant performance gains, with accuracies of 99.6%, 99.2% and 99.2% respectively. The results demonstrate that angular margin-based and center-based constraints substantially boost the discriminative strength of feature embeddings. In particular, the framework does not require major architectural modifications, making it efficient and practical for real-world deployment in farming environments.

Sofia Kaisaridi, Juliette Ortholand, Caglayan Tuna et al.

The progression of chronic diseases often follows highly variable trajectories, and the underlying factors remain poorly understood. Standard mixed-effects models typically represent inter-patient differences as random deviations around a common reference, which may obscure meaningful subgroups. We propose a probabilistic mixture extension of a mixed effects model, the Disease Course Mapping model, to identify distinct disease progression subtypes within a population. The mixture structure is introduced at the latent individual parameters, enabling clustering based on both temporal and spatial variability in disease trajectories. We evaluated the model through simulation studies to assess classification performance and parameter recovery. Classification accuracy exceeded 90% in simpler scenarios and remained above 80% in the most complex case, with particularly high recall and precision for fast-progressing clusters. Compared to a post hoc classification approach, the proposed model yielded more accurate parameter estimates, smaller biases, lower root mean squared errors, and reduced uncertainty. It also correctly recovered the true three-cluster structure in 93% of the simulations. Finally, we applied the model to a longitudinal cohort of CADASIL patients, identifying two clinically meaningful clusters, differentiating patients with early versus late onset and fast versus slow progression, with clear spatial patterns across motor and memory scores. Overall, this probabilistic mixture framework offers a robust, interpretable approach for clustering patients based on spatiotemporal disease dynamics.

Alayne Gatto, Gabriella Reingevurts, Mariana Leon et al.

Sergey Morozov, Vladimir Pilipenko, Vasily Isakov et al.

Erisya Damayanti, Endang Budiati, Sugeng Eko Irianto et al.

Background: East Lampung is an endemic area for filariasis. Survey results since 2004, showing Sekampung and Sekampung Udik sub-districts, indicate a microfilaria rate of filariasis above 1%. In 2024, 11 chronic filariasis patients were found. This research aims to analyze the influence of living habits, mosquito breeding sites, family support, the role of health workers, and the role of community leaders on the incidence of filariasis. Methods: This research type is quantitative with a case-control design. The research sample consisted of 44 respondents. The sampling technique used was purposive sampling. Data collection methods included interviews and observations using questionnaires and observation sheets as guidelines. Data analysis consisted of univariate, bivariate, and multivariate analyses. Results: Bivariate analysis showed the influence of living habits (p-value 0.043), mosquito breeding sites (p-value 0.004), family support (p-value 0.035), the role of health workers (p-value 0.027), and the role of community leaders (p-value 0.019) on the incidence of filariasis. Multivariate analysis revealed that living habits dominantly influence the incidence of filariasis with a p-value of 0.011. Conclusions: There is an influence of lifestyle habits on the incidence of filariasis, mosquito breeding, family support, the role of health workers, the role of community leaders on the incidence of filariasis in East Lampung Regency with the most influential variable, namely lifestyle habits, which dominantly affect the incidence of filariasis in East Lampung Regency.

Yasmine Probst, Emiliana Saffioti, Sarah Manche et al.

Abstract Objective: Multiple sclerosis (MS) is a chronic, auto-immune, neurodegenerative condition with increasing global prevalence. People living with MS (plwMS) have reported limited guidance relating to nutrition information. Paired with varied health literacy levels, this makes plwMS susceptible to nutrition misinformation. Design: A cross-sectional online social network analysis (SNA) examining nutrition information for MS. Setting: A systematic SNA using Twitter/X and YouTube platforms using NodeXL to summarise metrics. Quality was assessed using the QUEST tool. Content analysis of YouTube videos was synthesised into themes for misinformation. Participants: Online publicly available social media user posts and video content. Results: Twitter/X SNA revealed keywords used most by an account representing 72·8 % of the user network with common diet mentions including Wahls (57 times), paleo (15 times) and ketogenic (11 times). ‘Favourite count’ metrics were strongly correlated with ‘repost count’ (r = 0·83, P = 0·000). Videos which endorsed a diet were more likely to have a lower QUEST score. User engagement metrics were higher for lower quality videos. The quality of online nutrition information relating to MS was moderate (61 %). Physicians were the most likely source of nutrition information endorsing a diet for MS. The content analysis identified a knowledge gap for both medical professionals and plwMS. Conclusions: Nutrition misinformation for MS occurs on social media and information quality is variable. Audiences need to be cautioned about users with large followings and evaluate the credibility of all information. This study reiterates the importance of evidence-based information for the MS community.

Takafumi Nogami, Satoshi Kagiwada, Hitoshi Iyatomi

Various deep learning-based systems have been proposed for accurate and convenient plant disease diagnosis, achieving impressive performance. However, recent studies show that these systems often fail to maintain diagnostic accuracy on images captured under different conditions from the training environment -- an essential criterion for model robustness. Many deep learning methods have shown high accuracy in plant disease diagnosis. However, they often struggle to generalize to images taken in conditions that differ from the training setting. This drop in performance stems from the subtle variability of disease symptoms and domain gaps -- differences in image context and environment. The root cause is the limited diversity of training data relative to task complexity, making even advanced models vulnerable in unseen domains. To tackle this challenge, we propose a simple yet highly adaptable learning framework called Target-Aware Metric Learning with Prioritized Sampling (TMPS), grounded in metric learning. TMPS operates under the assumption of access to a limited number of labeled samples from the target (deployment) domain and leverages these samples effectively to improve diagnostic robustness. We assess TMPS on a large-scale automated plant disease diagnostic task using a dataset comprising 223,073 leaf images sourced from 23 agricultural fields, spanning 21 diseases and healthy instances across three crop species. By incorporating just 10 target domain samples per disease into training, TMPS surpasses models trained using the same combined source and target samples, and those fine-tuned with these target samples after pre-training on source data. It achieves average macro F1 score improvements of 7.3 and 3.6 points, respectively, and a remarkable 18.7 and 17.1 point improvement over the baseline and conventional metric learning.

Sudipta Saha, Md. Kawsar Alam

Considerable attention has been directed towards the prognosis of lung diseases primarily due to their high prevalence. Despite advancements in detection technologies, current methods such as computed tomography, chest radiographs, bold proteomic patterns, nuclear magnetic resonance, and positron emission tomography still face limitations in detecting diseases related to the lungs. Consequently, there is a need for swift, non-invasive and economically feasible detection methods. Our study explores the interaction between BeS monolayer and breathe biomarkers related to lung disease utilizing the density functional theory (DFT) method. Through comprehensive DFT analysis, including electronic properties analysis, charge transfer evaluations, work function, optical properties assessment and recovery times, the feasibility and efficiency of BeS as a VOC (volatile organic compound) detection are investigated. Findings reveal significant changes in bandgap upon VOC adsorption, with notable alteration in work function for selective compounds. Optical property analyses demonstrate the potential for selective detection of biomarkers within specific wavelength ranges. Moreover, the study evaluates the impact of electric fields and strain on VOC-2D BeS interaction. Furthermore, the desorption of these VOCs from the BeS surface can be achieved through a heating process or under the illumination of UV light. This feature enables the reusability of the 2D material for biosensing applications. These findings highlight the potential of the BeS monolayer as a promising material for the sensitive and selective detection of breath biomarkers related to lung disease.

Hido Pinto, Eran Segal

Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is often constrained by the lack of their available disease labels. In this research, we demonstrate how machine learning can be leveraged to enhance explainability and uncover biologically meaningful associations, even when predictive improvements in disease modeling are limited. We train LightGBM models from scratch on our dataset (10K) to impute metabolomics features and apply them to the UK Biobank (UKBB) for downstream analysis. The imputed metabolomics features are then used in survival analysis to assess their impact on disease-related risk factors. As a result, our approach successfully identified biologically relevant connections that were not previously known to the predictive models. Additionally, we applied a genome-wide association study (GWAS) on key metabolomics features, revealing a link between vascular dementia and smoking. Although being a well-established epidemiological relationship, this link was not embedded in the model's training data, which validated the method's ability to extract meaningful signals. Furthermore, by integrating survival models as inputs in the 10K data, we uncovered associations between metabolic substances and obesity, demonstrating the ability to infer disease risk for future patients without requiring direct outcome labels. These findings highlight the potential of leveraging external bio-banks to extract valuable biomedical insights, even in data-limited scenarios. Our results demonstrate that machine learning models trained on smaller datasets can still be used to uncover real biological associations when carefully integrated with survival analysis and genetic studies.

Jayanth Mohan, Arrun Sivasubramanian, V Sowmya et al.

Skin diseases affect over a third of the global population, yet their impact is often underestimated. Automating skin disease classification to assist doctors with their prognosis might be difficult. Nevertheless, due to efficient feature extraction pipelines, deep learning techniques have shown much promise for various tasks, including dermatological disease identification. This study uses a skin disease dataset with 31 classes and compares it with all versions of Vision Transformers, Swin Transformers and DivoV2. The analysis is also extended to compare with benchmark convolution-based architecture presented in the literature. Transfer learning with ImageNet1k weights on the skin disease dataset contributes to a high test accuracy of 96.48\% and an F1-Score of 0.9727 using DinoV2, which is almost a 10\% improvement over this data's current benchmark results. The performance of DinoV2 was also compared for the HAM10000 and Dermnet datasets to test the model's robustness, and the trained model overcomes the benchmark results by a slight margin in test accuracy and in F1-Score on the 23 and 7 class datasets. The results are substantiated using explainable AI frameworks like GradCAM and SHAP, which provide precise image locations to map the disease, assisting dermatologists in early detection, prompt prognosis, and treatment.

Tess Watt, Christos Chrysoulas, Peter J Barclay

Image classification usually requires connectivity and access to the cloud which is often limited in many parts of the world, including hard to reach rural areas. TinyML aims to solve this problem by hosting AI assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without internet or cloud access. This pilot study explores the use of tinyML to provide healthcare support with low spec devices in low connectivity environments, focusing on diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, 10,000 images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). The model weights were then offloaded to a Raspberry Pi with a webcam attached, to be used for the classification of skin lesions without internet access. It was found that the developed prototype achieved a test accuracy of 78% and a test loss of 1.08.

Hasan Mujtaba Buttar, Muhammad Mahboob Ur Rahman, Muhammad Wasim Nawaz et al.

This paper is the first to present a novel, non-contact method that utilizes orthogonal frequency division multiplexing (OFDM) signals (of frequency 5.23 GHz, emitted by a software defined radio) to radio-expose the pulmonary patients in order to differentiate between five prevalent respiratory diseases, i.e., Asthma, Chronic obstructive pulmonary disease (COPD), Interstitial lung disease (ILD), Pneumonia (PN), and Tuberculosis (TB). The fact that each pulmonary disease leads to a distinct breathing pattern, and thus modulates the OFDM signal in a different way, motivates us to acquire OFDM-Breathe dataset, first of its kind. It consists of 13,920 seconds of raw RF data (at 64 distinct OFDM frequencies) that we have acquired from a total of 116 subjects in a hospital setting (25 healthy control subjects, and 91 pulmonary patients). Among the 91 patients, 25 have Asthma, 25 have COPD, 25 have TB, 5 have ILD, and 11 have PN. We implement a number of machine and deep learning models in order to do lung disease classification using OFDM-Breathe dataset. The vanilla convolutional neural network outperforms all the models with an accuracy of 97%, and stands out in terms of precision, recall, and F1-score. The ablation study reveals that it is sufficient to radio-observe the human chest on seven different microwave frequencies only, in order to make a reliable diagnosis (with 96% accuracy) of the underlying lung disease. This corresponds to a sensing overhead that is merely 10.93% of the allocated bandwidth. This points to the feasibility of 6G integrated sensing and communication (ISAC) systems of future where 89.07% of bandwidth still remains available for information exchange amidst on-demand health sensing. Through 6G ISAC, this work provides a tool for mass screening for respiratory diseases (e.g., COVID-19) at public places.

Nourelhouda Groun, Maria Villalba-Orero, Lucia Casado-Martin et al.

In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the field faces significant challenges when integrating data science techniques, as a significant volume of images is required for these techniques. As a consequence, it is necessary to investigate different avenues to overcome this challenge. In this contribution, we offer an innovative tool to conquer this limitation. In particular, we delve into the application of a well recognized method known as the EigenFaces approach to classify cardiac diseases. This approach was originally motivated for efficiently representing pictures of faces using principal component analysis, which provides a set of eigenvectors (aka eigenfaces), explaining the variation between face images. As this approach proven to be efficient for face recognition, it motivated us to explore its efficiency on more complicated data bases. In particular, we integrate this approach, with convolutional neural networks (CNNs) to classify echocardiography images taken from mice in five distinct cardiac conditions (healthy, diabetic cardiomyopathy, myocardial infarction, obesity and TAC hypertension). Performing a preprocessing step inspired from the eigenfaces approach on the echocardiography datasets, yields sets of pod modes, which we will call eigenhearts. To demonstrate the proposed approach, we compare two testcases: (i) supplying the CNN with the original images directly, (ii) supplying the CNN with images projected into the obtained pod modes. The results show a substantial and noteworthy enhancement when employing SVD for pre-processing, with classification accuracy increasing by approximately 50%.

Morteza Maleki, Foad Haeri

Cardiovascular diseases are the leading cause of mortality globally, necessitating advancements in diagnostic techniques. This study explores the application of wavelet transformation for classifying electrocardiogram (ECG) signals to identify various cardiovascular conditions. Utilizing the MIT-BIH Arrhythmia Database, we employed both continuous and discrete wavelet transforms to decompose ECG signals into frequency sub-bands, from which we extracted eight statistical features per band. These features were then used to train and test various classifiers, including K-Nearest Neighbors and Support Vector Machines, among others. The classifiers demonstrated high efficacy, with some achieving an accuracy of up to 96% on test data, suggesting that wavelet-based feature extraction significantly enhances the prediction of cardiovascular abnormalities in ECG data. The findings advocate for further exploration of wavelet transforms in medical diagnostics to improve automation and accuracy in disease detection. Future work will focus on optimizing feature selection and classifier parameters to refine predictive performance further.

Anirudh Prabhakaran, YeKun Xiao, Ching-Yu Cheng et al.

Ocular diseases, including diabetic retinopathy and glaucoma, present a significant public health challenge due to their high prevalence and potential for causing vision impairment. Early and accurate diagnosis is crucial for effective treatment and management. In recent years, deep learning models have emerged as powerful tools for analysing medical images, such as retina imaging. However, challenges persist in model relibability and uncertainty estimation, which are critical for clinical decision-making. This study leverages the probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over latent discrete dropout masks for the classification and analysis of ocular diseases using fundus images. We develop a robust and generalizable method that utilizes GFlowOut integrated with ResNet18 and ViT models as the backbone in identifying various ocular conditions. This study employs a unique set of dropout masks - none, random, bottomup, and topdown - to enhance model performance in analyzing these fundus images. Our results demonstrate that our learnable probablistic latents significantly improves accuracy, outperforming the traditional dropout approach. We utilize a gradient map calculation method, Grad-CAM, to assess model explainability, observing that the model accurately focuses on critical image regions for predictions. The integration of GFlowOut in neural networks presents a promising advancement in the automated diagnosis of ocular diseases, with implications for improving clinical workflows and patient outcomes.

Kasimov Botir, Namozov Normamat, Islamov Sokhib

African millet is small, about 1000 seeds 5-10 gram and is the best feed for birds. The stalk is superior to all other fodder crops in silage and dry state in terms of its nutritive value and high protein content. Alcohol is extracted from grain and used in the production of beer in the food industry, flour is made from grain and added to wheat flour to make high-quality bread containing various minerals. Another fact is that grain yield is around 0.7-1.2 t/ha, and in some conditions, it reaches 1.8-2.0 t/ha. The root reaches more than 2 meters deep, which ensures drought resistance and wind resistance. The important biological properties of African millet are its high yield, disease resistance, rapid reproduction, high fodder and nutritional properties of the grain, drought resistance, high temperature deficiency resistance, green mass gives an abundant yield. Furthermore, it is less affected by pests and diseases than other cereal crops. African millet “HHVBC tall” and “EEBC” varieties were investigated in eight variants with three iterations according to feeding standards. According to the results, on May 22, 2015, the plant height was 44-45 cm in the EEBC variety and 47-48 cm in the HHVBC tall variety. It was found that when the phenological observations were made on June 20, the average plant height was 130-125 cm in the EEBC variety, whereas it was 95-120 cm in the HHVBC tall variety. It can be seen that the height of EEBC variety compared to HHVBC tall variety was greater.

Ellen Greene, Celine Murrin

Abstract Objective: To review research on the influence of parent-related factors on children’s dairy products consumption. Design: A search of electronic databases and a narrative synthesis of the literature were conducted. English-language articles were included if they reported data relating to parental influences on children’s consumption of dairy products and if statistical significance was reported. Setting: Studies were carried out in the USA (n 8) and in a range of countries across Europe (n 12) and Asia (n 5). Participants: The subjects of this research were children aged between 2 and 12 years of age, from a range of geographical locations. Results: Twenty-five studies met the inclusion criteria. The studies examined children’s dairy products consumption in relation to parental socio-economic status (education level and income) (n 12), home availability (n 2), home food environment (n 3), parental dairy products consumption (n 4), parent feeding practices (n 3), parents’ beliefs and attitudes (n 3) and parental nutrition knowledge (n 3). Results on the association between socio-economic status and children’s dairy products consumption varied; however, studies reporting a significant association generally observed a positive relationship. Fifteen studies reported children’s total dairy products intake as an outcome measure, with the remaining studies reporting intake of milk or other dairy products as individual foods. Conclusions: This review identified literature exploring a range of parental factors in relation to children’s dairy products intake. However, there were limited numbers of studies published within each category of modifiable factors. Further research on the parent-related determinants of dairy products consumption in children is required in order to identify potential intervention targets in this age group.

Shankara Narayanan, Sneha Varsha M, Syed Ashfaq Ahmed et al.

The mouth, often regarded as a window to the internal state of the body, plays an important role in reflecting one's overall health. Poor oral hygiene has far-reaching consequences, contributing to severe conditions like heart disease, cancer, and diabetes, while inadequate care leads to discomfort, pain, and costly treatments. Federated Learning (FL) for object detection can be utilized for this use case due to the sensitivity of the oral image data of the patients. FL ensures data privacy by storing the images used for object detection on the local device and trains the model on the edge. The updated weights are federated to a central server where all the collected weights are updated via The Federated Averaging algorithm. Finally, we have developed a mobile app named OralH which provides user-friendly solutions, allowing people to conduct self-assessments through mouth scans and providing quick oral health insights. Upon detection of the issues, the application alerts the user about potential oral health concerns or diseases and provides details about dental clinics in the user's locality. Designed as a Progressive Web Application (PWA), the platform ensures ubiquitous access, catering to users across devices for a seamless experience. The application aims to provide state-of-the-art segmentation and detection techniques, leveraging the YOLOv8 object detection model to identify oral hygiene issues and diseases. This study deals with the benefits of leveraging FL in healthcare with promising real-world results.

Nikhil J. Dhinagar, Conor Owens-Walton, Emily Laltoo et al.

There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. Here we define a curriculum to progressively increase the difficulty of the training data corresponding to the Hoehn and Yahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls; age range: 20.0-84.9 years). Even with our multi-task setting using pre-trained CNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI was challenging (ROC AUC: 0.59-0.65), but curriculum training boosted performance (by 3.9%) compared to our baseline model. Future work with multimodal imaging may further boost performance.

Haeyun Lee, Yongsoon Eun, Jae Youn Hwang et al.

Background and Objective: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying coronary artery lesions by echocardiography is important for the timely diagnosis of and favorable outcomes in KD. Moreover, similar to KD, coronavirus disease 2019, currently causing a worldwide pandemic, also manifests with fever; therefore, it is crucial at this moment that KD should be distinguished clearly among the febrile diseases in children. In this study, we aimed to validate a deep learning algorithm for classification of KD and other acute febrile diseases. Methods: We obtained coronary artery images by echocardiography of children (n = 88 for KD; n = 65 for pneumonia). We trained six deep learning networks (VGG19, Xception, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) using the collected data. Results: SE-ResNext50 showed the best performance in terms of accuracy, specificity, and precision in the classification. SE-ResNext50 offered a precision of 76.35%, a sensitivity of 82.64%, and a specificity of 58.12%. Conclusions: The results of our study suggested that deep learning algorithms have similar performance to an experienced cardiologist in detecting coronary artery lesions to facilitate the diagnosis of KD.