Hasil untuk "Nutritional diseases. Deficiency diseases"

Khang Nguyen Quoc, Phuong D. Dao, Luyl-Da Quach

Foundation models and vision-language pre-training have significantly advanced Vision-Language Models (VLMs), enabling multimodal processing of visual and linguistic data. However, their application in domain-specific agricultural tasks, such as plant pathology, remains limited due to the lack of large-scale, comprehensive multimodal image--text datasets and benchmarks. To address this gap, we introduce LeafNet, a comprehensive multimodal dataset, and LeafBench, a visual question-answering benchmark developed to systematically evaluate the capabilities of VLMs in understanding plant diseases. The dataset comprises 186,000 leaf digital images spanning 97 disease classes, paired with metadata, generating 13,950 question-answer pairs spanning six critical agricultural tasks. The questions assess various aspects of plant pathology understanding, including visual symptom recognition, taxonomic relationships, and diagnostic reasoning. Benchmarking 12 state-of-the-art VLMs on our LeafBench dataset, we reveal substantial disparity in their disease understanding capabilities. Our study shows performance varies markedly across tasks: binary healthy--diseased classification exceeds 90% accuracy, while fine-grained pathogen and species identification remains below 65%. Direct comparison between vision-only models and VLMs demonstrates the critical advantage of multimodal architectures: fine-tuned VLMs outperform traditional vision models, confirming that integrating linguistic representations significantly enhances diagnostic precision. These findings highlight critical gaps in current VLMs for plant pathology applications and underscore the need for LeafBench as a rigorous framework for methodological advancement and progress evaluation toward reliable AI-assisted plant disease diagnosis. Code is available at https://github.com/EnalisUs/LeafBench.

Farzia Hossain, Samanta Ghosh, Shahida Begum et al.

Human nail diseases are gradually observed over all age groups, especially among older individuals, often going ignored until they become severe. Early detection and accurate diagnosis of such conditions are important because they sometimes reveal our body's health problems. But it is challenging due to the inferred visual differences between disease types. This paper presents a machine learning-based model for automated classification of nail diseases based on a publicly available dataset, which contains 3,835 images scaling six categories. In 224x224 pixels, all images were resized to ensure consistency. To evaluate performance, four well-known CNN models-InceptionV3, DenseNet201, EfficientNetV2, and ResNet50 were trained and analyzed. Among these, InceptionV3 outperformed the others with an accuracy of 95.57%, while DenseNet201 came next with 94.79%. To make the model stronger and less likely to make mistakes on tricky or noisy images, we used adversarial training. To help understand how the model makes decisions, we used SHAP to highlight important features in the predictions. This system could be a helpful support for doctors, making nail disease diagnosis more accurate and faster.

Leming Zhou, Zuo Wang, Zhigang Liu

Due to the insufficient diagnosis and treatment capabilities at the grassroots level, there are still deficiencies in the early identification and early warning of acute exacerbation of Chronic obstructive pulmonary disease (COPD), often resulting in a high prevalence rate and high burden, but the screening rate is relatively low. In order to gradually improve this situation. In this paper, this study develop a Causal Heterogeneous Graph Representation Learning (CHGRL) method for COPD comorbidity risk prediction method that: a) constructing a heterogeneous Our dataset includes the interaction between patients and diseases; b) A cause-aware heterogeneous graph learning architecture has been constructed, combining causal inference mechanisms with heterogeneous graph learning, which can support heterogeneous graph causal learning for different types of relationships; and c) Incorporate the causal loss function in the model design, and add counterfactual reasoning learning loss and causal regularization loss on the basis of the cross-entropy classification loss. We evaluate our method and compare its performance with strong GNN baselines. Following experimental evaluation, the proposed model demonstrates high detection accuracy.

Gabriela Ozegovic, Thorsten Ruprechter, Denis Helic

The increased popularity of food communities on social media shapes the way people engage with food-related content. Due to the extensive consequences of such content on users' eating behavior, researchers have started studying the factors that drive user engagement with food in online platforms. However, while most studies focus on visual aspects of food content in social media, there exist only initial studies exploring the impact of nutritional content on user engagement. In this paper, we set out to close this gap and analyze food-related posts on Reddit, focusing on the association between the nutritional density of a meal and engagement levels, particularly the number of comments. Hence, we collect and empirically analyze almost 600,000 food-related posts and uncover differences in nutritional content between engaging and non-engaging posts. Moreover, we train a series of XGBoost models, and evaluate the importance of nutritional density while predicting whether users will comment on a post or whether a post will substantially resonate with the community. We find that nutritional features improve the baseline model's accuracy by 4%, with a positive contribution of calorie density towards prediction of engagement, suggesting that higher nutritional content is associated with higher user engagement in food-related posts. Our results provide valuable insights for the design of more engaging online initiatives aimed at, for example, encouraging healthy eating habits.

Hussein Aliu Sule, Abdulwakil Olawale Saba, Choo Yee Yu

Aquaculture is pivotal for global food security but faces significant challenges from infectious diseases, particularly those caused by Streptococcus species such as Streptococcus iniae and Streptococcus agalactiae. These pathogens induce severe systemic infections in various fish species, resulting in high morbidity and mortality rates. This review consolidates current knowledge on the epidemiology, pathogenesis, and clinical manifestations of these infections in fish and provides a comprehensive analysis of multifaceted control and prebention strategies. Advancements in genetic engineering and selective breeding are highlighted, demonstrating significant potential in developing disease-resistant fish strains through technologies like CRISPR-Cas9 and genomic selection. We examine the impact of farming practices on disease prevalence, emphasizing the roles of stocking density, feeding regimes, and biosecurity measures. The integration of big data analytics and IoT technologies is shown to revolutionize disease monitoring and management, enabling real-time surveillance and predictive modeling for timely interventions. Progress in vaccine development, including subunit, DNA, and recombinant protein vaccines, highlights the importance of tailored immunoprophylactic strategies. Furthermore, this review emphasizes the One-Health approach and the essential collaboration among industry, academia, and government to address the interconnected health of humans, animals, and the environment. This holistic strategy, supported by advanced technologies and collaborative efforts, promises to enhance the sustainability and productivity of aquaculture systems. Future research directions advocate for continued innovation and interdisciplinary partnerships to overcome the persistent challenges of streptococcal infections in aquaculture.

Georgios Papaoikonomou, Kyriaki Apergi, Olga Malisova

Background: Investigating fluid consumption among children and adolescents poses a challenge due to varying dietary behaviors and hydration needs. This systematic review aims to assess total water intake (TWI) and total fluid intake (TFI) in children and adolescents, focusing on gender differences and adherence to reference values proposed by the European Food Safety Authority (EFSA) and the Institute of Medicine (IOM). Methods: A systematic literature search of PubMed, Scopus, and Scholar was conducted to identify studies published between 2004 and 2024 reporting on TWI and TFI for children and adolescents. Studies were included if they reported fluid intake data in healthy populations, provided quantitative measures of TWI or TFI, and aligned with the EFSA or IOM reference values. From 8731 initial articles, 24 studies met the inclusion criteria. Data were synthesized narratively, and compliance with hydration guidelines was assessed. Results: The review included 24 studies, encompassing 16,254 children and 15,367 adolescents. The majority of participants failed to meet the recommended guidelines. Only one study reported compliance with the recommended TWI values, while four studies in children and four in adolescents showed adherence to the recommended TFI values. Conclusions: The results underscore a widespread inadequacy in achieving the hydration guidelines among children and adolescents, emphasizing the need for targeted interventions to improve fluid intake. Public health interventions are needed to promote adequate fluid intake, particularly in populations at risk of dehydration-related health outcomes. Future research should focus on identifying barriers to adequate hydration and developing targeted strategies to improve fluid intake behaviors.

Jacob Thrasher, Annahita Amireskandari, Prashnna Gyawali

Eye diseases are common in older Americans and can lead to decreased vision and blindness. Recent advancements in imaging technologies allow clinicians to capture high-quality images of the retinal blood vessels via Optical Coherence Tomography Angiography (OCTA), which contain vital information for diagnosing these diseases and expediting preventative measures. OCTA provides detailed vascular imaging as compared to the solely structural information obtained by common OCT imaging. Although there have been considerable studies on OCT imaging, there have been limited to no studies exploring the role of artificial intelligence (AI) and machine learning (ML) approaches for predictive modeling with OCTA images. In this paper, we explore the use of deep learning to identify eye disease in OCTA images. However, due to the lack of labeled data, the straightforward application of deep learning doesn't necessarily yield good generalization. To this end, we utilize active learning to select the most valuable subset of data to train our model. We demonstrate that active learning subset selection greatly outperforms other strategies, such as inverse frequency class weighting, random undersampling, and oversampling, by up to 49% in F1 evaluation.

Lemuel Puglisi, Daniel C. Alexander, Daniele Ravì

In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer's Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: https://github.com/LemuelPuglisi/BrLP.

Sree Rama Vamsidhar S, Bhargava Satya, Rama Krishna Gorthi

Deep Learning (DL) approaches have gained prominence in medical imaging for disease diagnosis. Chest X-ray (CXR) classification has emerged as an effective method for detecting various diseases. Among these methodologies, Chest X-ray (CXR) classification has proven to be an effective approach for detecting and analyzing various diseases. However, the reliable performance of DL classification algorithms is dependent upon access to large and balanced datasets, which pose challenges in medical imaging due to the impracticality of acquiring sufficient data for every disease category. To tackle this problem, we propose an algorithmic-centric approach called Effective-Label Distribution Aware Margin (E-LDAM), which modifies the margin of the widely adopted Label Distribution Aware Margin (LDAM) loss function using an effective number of samples in each class. Experimental evaluations on the COVIDx CXR dataset focus on Normal, Pneumonia, and COVID-19 classification. The experimental results demonstrate the effectiveness of the proposed E-LDAM approach, achieving a remarkable recall score of 97.81% for the minority class (COVID-19) in CXR image prediction. Furthermore, the overall accuracy of the three-class classification task attains an impressive level of 95.26%.

Xiaojie Chen, Yifan Zhao, Jihong Sun et al.

Abstract Background Numerous studies indicate that visceral adipose tissue (VAT) significantly contribute to metabolic syndrome (MetS) development. This study aims to assess the distinguishing value of novel obesity markers, specifically lipid accumulation products (LAP) and cardiometabolic index (CMI), in relation to MetS. Considering the gender disparity in MetS prevalence, it is essential to explore whether LAP and CMI exhibit differential distinguishing capabilities by gender. Method The investigation included a total of 11,687 qualified individuals who participated in the NHANES survey spanning a 14-year period from 2005 to 2018. Biochemical analysis of blood and body measurements were utilized to determine LAP and CMI values for each participant. Inclusion of gender as a variable was a key factor in the examination of all data. Restricted cube plots (RCS) were utilized to analyze the strength of the relationship between LAP, CMI, and MetS. The study delved into potential connections between LAP and CMI with MetS, all-cause and cardiovascular mortality using various statistical models such as multivariate logistic regression and Cox regression. Results The findings revealed a significant nonlinear association between CMI, LAP, and MetS (P-non-linear < 0.001), irrespective of gender, with all models exhibiting a J-shaped trend. The multivariable logistic regression analysis considered both LAP and CMI as continuous variables or tertiles, revealing significant associations with MetS in male, female, and general populations (All the P < 0.001). Although males displayed a higher risk of MetS, no gender differences were observed in the area under the curve (AUC) values of LAP and CMI for distinguishing (P > 0.005) MetS. Impressively, LAP and CMI were identified as the primary predictors of MetS in both genders from AUC (P < 0.005). More specifically, the cutoff points for distinguishing MetS in females were LAP = 49.87 or CMI = 0.56, while for males, they were LAP = 52.76 or CMI = 0.70. Additionally, the Cox regression analysis revealed that LAP and CMI were correlated with all-cause mortality in both general population and females (P < 0.005), but not in males. Conclusion In comparison to other measures of obesity, LAP and CMI demonstrated superior diagnostic accuracy for MetS in both males and females. Additionally, LAP and CMI were found to be predictive of all-cause mortality in both general population and females. These markers are cost-effective, easily accessible, and widely applicable for the early identification and screening of MetS in clinical settings.

Xingzhi Guo, Wei Li, Chen Hou et al.

Abstract Background Observational studies have indicated that breakfast skipping and gut microbiome dysbiosis are associated with a higher risk of major depressive disorder (MDD). However, it remains unknown whether the alteration of gut microbes is implicated in the associations between breakfast skipping and MDD. Methods Leveraging genome-wide association studies (GWAS) on breakfast skipping, gut microbes, and MDD, we conducted a two-step Mendelian randomization (MR) study to determine the causal associations between breakfast skipping (N = 193,860) and MDD (N = 1,815,091), and evaluate the role of gut microbes (N = 18,340). Genetic variants with a P-value less than 5E-08 were selected as instrumental variables (IVs). The false discovery rate (FDR) method was employed to correct the P-values for multiple tests in gut microbes. Results Breakfast skipping was associated with an increased risk of MDD (odds ratio [OR] = 1.36, 95%CI = 1.12–1.65, P = 0.002), but no effect of MDD on breakfast skipping was observed (β per doubling odds of MDD =-0.001, 95%CI=-0.024 to 0.023, P = 0.957). After adjusting for multiple comparisons, the MR analysis provided little evidence for an association between breakfast skipping and the abundance of any gut microbes (PFDR>0.05). Among the 21 gut microbes with IVs available, only the abundance of Class Actinobacteria was causally associated with a reduced risk of MDD (OR = 0.85, 95%CI = 0.75–0.97, PFDR=0.015). Conclusions Our findings demonstrated that breakfast skipping was associated with an increased risk of MDD, but provided little evidence supporting the role of the abundance of gut microbes in it. Further efforts with a large sample size are warranted to clarify the findings.

A. V. Enert, D. G. Apalkov, S. R. Pereletova et al.

In this review, information is presented within the triad: obstructive sleep apnea/hypopnea syndrome (OSA), glycemic variability, and cardiac arrhythmias in patients with type 2 diabetes mellitus (DM2). Epidemiological aspects, pathogenetic relationships, possible instrumental and laboratory diagnostic methods, as well as approaches to personalized therapy are analyzed. Research is being actively conducted in certain areas of the designated triad, however, no studies have been found that include simultaneous monitoring of indicators reflecting these disorders in patients with DM2. Many issues are still controversial. Sleep disturbances in patients with DM2 are actively studied, but more often questionnaires are used for diagnosis, rather than instrumental methods. There is insufficient data examining the effect of hypoxia on the progression of complications in patients with DM2. Rhythm disturbances are being actively studied in patients with DM2 in combination with various cardiological problems. Of greatest interest is the study of rhythm disturbances in patients with DM2 without concomitant comorbid conditions of the cardiovascular system, in order to identify early signs of diabetic cardiovascular autonomic neuropathy and cardiomyopathy, as well as additional early risk factors for the development and progression of cardiovascular diseases. Most of the studies are devoted to the study of the association of OSA and various arrhythmias in cardiac patients. However, there is no data on the combined effect of glycemic variability and OSA on the development of cardiac arrhythmias in patients with DM2. Additional studies are needed to identify the features of the effect of OSA on cardiac arrhythmias in patients with DM2.

David Oniani, Jordan Hilsman, Hang Dong et al.

The emergence of generative Large Language Models (LLMs) emphasizes the need for accurate and efficient prompting approaches. LLMs are often applied in Few-Shot Learning (FSL) contexts, where tasks are executed with minimal training data. FSL has become popular in many Artificial Intelligence (AI) subdomains, including AI for health. Rare diseases affect a small fraction of the population. Rare disease identification from clinical notes inherently requires FSL techniques due to limited data availability. Manual data collection and annotation is both expensive and time-consuming. In this paper, we propose Models-Vote Prompting (MVP), a flexible prompting approach for improving the performance of LLM queries in FSL settings. MVP works by prompting numerous LLMs to perform the same tasks and then conducting a majority vote on the resulting outputs. This method achieves improved results to any one model in the ensemble on one-shot rare disease identification and classification tasks. We also release a novel rare disease dataset for FSL, available to those who signed the MIMIC-IV Data Use Agreement (DUA). Furthermore, in using MVP, each model is prompted multiple times, substantially increasing the time needed for manual annotation, and to address this, we assess the feasibility of using JSON for automating generative LLM evaluation.

Gonzalo Uribarri, Simon Ekman von Huth, Josefine Waldthaler et al.

Eye-tracking is an accessible and non-invasive technology that provides information about a subject's motor and cognitive abilities. As such, it has proven to be a valuable resource in the study of neurodegenerative diseases such as Parkinson's disease. Saccade experiments, in particular, have proven useful in the diagnosis and staging of Parkinson's disease. However, to date, no single eye-movement biomarker has been found to conclusively differentiate patients from healthy controls. In the present work, we investigate the use of state-of-the-art deep learning algorithms to perform Parkinson's disease classification using eye-tracking data from saccade experiments. In contrast to previous work, instead of using hand-crafted features from the saccades, we use raw $\sim1.5\,s$ long fixation intervals recorded during the preparatory phase before each trial. Using these short time series as input we implement two different classification models, InceptionTime and ROCKET. We find that the models are able to learn the classification task and generalize to unseen subjects. InceptionTime achieves $78\%$ accuracy, while ROCKET achieves $88\%$ accuracy. We also employ a novel method for pruning the ROCKET model to improve interpretability and generalizability, achieving an accuracy of $96\%$. Our results suggest that fixation data has low inter-subject variability and potentially carries useful information about brain cognitive and motor conditions, making it suitable for use with machine learning in the discovery of disease-relevant biomarkers.

Ritesh Chandra, Sadhana Tiwari, Satyam Rastogi et al.

Liver diseases pose a significant global health burden, impacting many individuals and having substantial economic and social consequences. Rising liver problems are considered a fatal disease in many countries, such as Egypt and Moldova. This study aims to develop a diagnosis and treatment model for liver disease using Basic Formal Ontology (BFO), Patient Clinical Data (PCD) ontology, and detection rules derived from a decision tree algorithm. For the development of the ontology, the National Viral Hepatitis Control Program (NVHCP) guidelines were used, which made the ontology more accurate and reliable. The Apache Jena framework uses batch processing to detect events based on these rules. Based on the event detected, queries can be directly processed using SPARQL. We convert these Decision Tree (DT) and medical guidelines-based rules into Semantic Web Rule Language (SWRL) to operationalize the ontology. Using this SWRL in the ontology to predict different types of liver disease with the help of the Pellet and Drools inference engines in Protege Tools, a total of 615 records were taken from different liver diseases. After inferring the rules, the result can be generated for the patient according to the rules, and other patient-related details, along with different precautionary suggestions, can be obtained based on these results. These rules can make suggestions more accurate with the help of Explainable Artificial Intelligence (XAI) with open API-based suggestions. When the patient has prescribed a medical test, the model accommodates this result using optical character recognition (OCR), and the same process applies when the patient has prescribed a further medical suggestion according to the test report. These models combine to form a comprehensive Decision Support System (DSS) for the diagnosis of liver disease.

Yuzhe Han, Qimin Cheng, Wenjin Wu et al.

A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition.

Caitlin E Caspi, Maria Fernanda Gombi-Vaca, Julian Wolfson et al.

Abstract Objective: The current study presents results of a midpoint analysis of an ongoing natural experiment evaluating the diet-related effects of the Minneapolis Minimum Wage Ordinance, which incrementally increases the minimum wage to $15/h. Design: A difference-in-difference (DiD) analysis of measures collected among low-wage workers in two U.S. cities (one city with a wage increase policy and one comparison city). Measures included employment-related variables (hourly wage, hours worked and non-employment assessed by survey questions with wages verified by paystubs), BMI measured by study scales and stadiometers and diet-related mediators (food insecurity, Supplemental Nutrition Assistance Program (SNAP) participation and daily servings of fruits and vegetables, whole-grain rich foods and foods high in added sugars measured by survey questions). Setting: Minneapolis, Minnesota and Raleigh, North Carolina. Participants: A cohort of 580 low-wage workers (268 in Minneapolis and 312 in Raleigh) who completed three annual study visits between 2018 and 2020. Results: In DiD models adjusted for time-varying and non-time-varying confounders, there were no statistically significant differences in variables of interest in Minneapolis compared with Raleigh. Trends across both cities were evident, showing a steady increase in hourly wage, stable BMI, an overall decrease in food insecurity and non-linear trends in employment, hours worked, SNAP participation and dietary outcomes. Conclusion: There was no evidence of a beneficial or adverse effect of the Minimum Wage Ordinance on health-related variables during a period of economic and social change. The COVID-19 pandemic and other contextual factors likely contributed to the observed trends in both cities.

Adam Mielke

With the covid-19 pandemic still ongoing and an enormous amount of test data available, the lessons learned over the last two years need to be developed to a point where they can provide understanding for tackling new variants and future diseases. The SIR-model commonly used to model disease spread, predicts exponential initial growth, which helps establish the infectiousness of a disease in the early days of an outbreak. Unfortunately, the exponential growth becomes muddied by spatial, finite-size, and non-equilibrium effects in realistic systems, and robust estimates that may be used in prediction and description are still lacking. I here establish a second quantization framework that allows introduction of arbitrarily complicated spatial behavior, and I show that a simplified version of this model is in good agreement with both the growth of different covid-19 variants in Denmark and analytical results from the theory of branched polymers. Denmark is well-suited for comparison, because the number of tests with variant information in early December 2021 is very high, so the spread of a single variant can be followed. I expect this model to build bridges between the epidemic modeling and solid state communities. The long-term goal of the particular analysis in this paper is to establish priors that allow better early estimates for the infectiousness of a new disease.

Giovanna Lomolino, Matteo Marangon, Simone Vincenzi et al.

Cider is a beverage belonging to the tradition of many European rural areas. Pairing beverages and cheeses, even if it is part of an ancient tradition, is gaining more and more interest from the consumer. For this reason, in this research, we wanted to conduct a preliminary study on the combination of cider and cheese. In particular, six Italian sparkling ciders were selected, obtained through the Charmat and Champenoise method, and four types of Italian cheeses, from the Veneto region: <i>Casatella Trevigiana</i>, <i>Fienil</i>, <i>Morlacco</i> and <i>Ubriaco</i>, with very different sensory characteristics. The cider-cheese pairing test, conducted by a panel of experts, revealed how some cider parameters are reduced in intensity, such as astringency, while others are enhanced, such as fruitiness and persistence taste aroma. The hedonic test, conducted on the matching by 90 consumers, promoted some combinations while others were rejected. The sensory parameters associated with liking were fruity and taste aroma persistence, particularly expressed in some cider-cheese pairings.

Jaafar Hammoud, Aleksandra Vatian, Natalia Dobrenko et al.

The Arabic language suffers from a great shortage of datasets suitable for training deep learning models, and the existing ones include general non-specialized classifications. In this work, we introduce a new Arab medical dataset, which includes two thousand medical documents collected from several Arabic medical websites, in addition to the Arab Medical Encyclopedia. The dataset was built for the task of classifying texts and includes 10 classes (Blood, Bone, Cardiovascular, Ear, Endocrine, Eye, Gastrointestinal, Immune, Liver and Nephrological) diseases. Experiments on the dataset were performed by fine-tuning three pre-trained models: BERT from Google, Arabert that based on BERT with large Arabic corpus, and AraBioNER that based on Arabert with Arabic medical corpus.