Diagnosis Of Systemic Diseases Research Articles

The use of genetic algorithm and particle swarm optimization on tiered feature selection method in machine learning-based coronary heart disease diagnosis system

Coronary heart disease (CHD) is a leading global cause of death. Early detection is the right step to reduce mortality rates and treatment costs. Early detection can be developed using machine learning by utilizing patient medical record datasets. Unfortunately, this dataset has excessive features which can reduce machine learning performance. For this reason, it is necessary to reduce the number of redundant features and irrelevant data to improve machine learning performance. Therefore, this research proposes a tiered of feature selection model with genetic algorithm (GA) and particle swarm optimization (PSO) to improve the performance of the diagnosis model. The feature selection model is evaluated using parameters derived from the confusion matrix and using the CatBoost machine learning algorithm. Model testing uses z-Alizadeh Sani, Cleveland, Statlog, and Hungarian datasets. The best results for this model were obtained on the z-Alizadeh Sani dataset with 6 selected features from 54 features and the resulting performance for accuracy parameters was 99.32%, specificity 98.57%, sensitivity 100.00%, area under the curve (AUC) 99.28%, and F1-Score 99.37%. The proposed feature selection model is able to provide machine learning performance in the very good category. The diagnostic model proposed is of excellent standard.

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model

In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient’s condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use.

Neuromuscular disease auxiliary diagnosis using a portable magnetomyographic system.

The measurement of electromyography (EMG) signals with needle electrodes is widely used in clinical settings for diagnosing neuromuscular diseases. Patients experience pain during needle EMG testing. It is significant to develop alternative diagnostic modalities. This paper proposes a portable magnetomyography (MMG) measurement system for neuromuscular disease auxiliary diagnosis. Firstly, the design and operating principle of the system are introduced. The feasibility of using the system for auxiliary diagnosis of neuromuscular diseases is then studied. The magnetic signals and needle EMG signals of thirty subjects were collected and compared. It is found that the amplitude of muscle magnetic field signal increases during mild muscle contraction, and the signal magnitudes of the patients are smaller than those of normal subjects. The diseased muscles tested in the experiment can be distinguished from the normal muscles based on the signal amplitude, using a threshold value of 6 pT. The MMG diagnosis results align well with the needle EMG diagnosis. In addition, the MMG measurement indicates that there is a persistence of spontaneous activity in the diseased muscle. The experimental results demonstrate that it is feasible to auxiliary diagnose neuromuscular diseases using the portable MMG system, which offers the advantages of non-contact and painless measurements. After more in-depth, systematic, and quantitative research, the portable MMG could potentially be used for auxiliary diagnosis of neuromuscular diseases.

Artificial Intelligence-Based System for Retinal Disease Diagnosis

Artificial Intelligence-Based System for Retinal Disease Diagnosis

ESforRPD2: Expert System for Rice Plant Disease Diagnosis.

One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. The system can also provide recommendations for the treatment of identified diseases. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id.

Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis

Plant diseases pose a significant threat to plant and crop health, leading to reduced yields and economic losses. The traditional methods for diagnosing plant diseases are often invasive and time-consuming and may not always provide accurate results. In recent years, there has been growing interest in utilizing Raman microscopy as a non-invasive and label-free technique for plant disease diagnosis. Raman microscopy is a powerful analytical tool that can provide detailed molecular information about samples by analyzing the scattered light from a laser beam. This technique has the potential to revolutionize plant disease diagnosis by offering rapid and accurate detection of various plant pathogens, including bacteria and fungi. One of the key advantages of Raman microscopy/spectroscopy is its ability to provide real-time and in situ analyses of plant samples. By analyzing the unique spectral fingerprints of different pathogens, researchers can quickly identify the presence of specific diseases without the need for complex sample preparation or invasive procedures. This article discusses the development of a Raman microspectroscopy system for disease diagnosis that can accurately detect and identify various plant pathogens, such as bacteria and fungi.

Integrative approach of omics and imaging data to discover new insights for understanding brain diseases.

Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.

Practice of distributed machine learning in clinical modeling for chronic obstructive pulmonary disease

BackgroundThe high prevalence, morbidity and mortality, and disease heterogeneity of chronic obstructive pulmonary disease (COPD) result in the scattered data derived from patient visits in different medical units. The huge cost of integrating the scattered data for analysis and modeling, as well as the legal demand for patient privacy protection lead to the emergence of data island. ObjectivesOn the premise of protecting patient privacy, integrating scattered data of patients from different medical units for high-quality modeling is beneficial to promoting the development of digital health. Based on this, we develop a distributed COPD disease diagnosis system termed COPD average federated learning (COPD_AVG_FL) using FedAvg. MethodsFirst, to build the COPD_AVG_FL, the clinical data of COPD patients from the real world is collected and the data pre-processing is performed to clean the incorrect data, outlier samples and missing values. Then, a classical federated learning architecture is designed as COPD_AVG_FL. Finally, to evaluate the established COPD_AVG_FL system, we develop Centralized Machine Learning (CML). ConclusionsOur results suggest that, with the assistance of COPD_AVG_FL, the absolute improvement rates are 13.4% (accuracy), 13.3% (precision), 12.8% (recall), 13.1% (F1-Score) and 12.9% (AUC) on the test data, respectively. The decoupling between model training and raw training data protects the patients' privacy, and helps to securely integrate more COPD data from different medical units to generate a more comprehensive model COPD_AVG_FL. This approach promotes the landing of wise information technology of medicine for COPD in the real clinical world. Code for our model will be made available at https://github.com/Cczhh/COPD_AVG_FL/tree/master.

Knowledge correction and [formula omitted]-insensitive criterion-leveraged zero-order TSK fuzzy system for rice leaf disease diagnosis

The advent of complex application scenarios introduces new challenges for diagnosing rice leaf diseases using machine learning methods. Two critical requirements are identified: 1) The model must exhibit high interpretability to mitigate the adverse effects of incorrect diagnoses; and 2) practical applications often suffer from insufficient samples and noise in rice leaf disease datasets, which requires the model to have strong generalization ability and robustness. However, existing methods still have certain limitations in practical scenarios due to a lack of comprehensive consideration of interpretability, generalization ability, and robustness. To address this issue, this article proposes a novel knowledge correction and ε-insensitive criterion-leveraged zero-order TSK fuzzy system (0-TSK-FS), named KE-0-TSK-FS. The KE-0-TSK-FS method is developed with 0-TSK-FS as the baseline, enhancing the generalization ability of the model by introducing the knowledge correction method and its iterative learning strategy to extract more information from limited samples. In addition, the objective function based on the ε-insensitive criterion makes KE-0-TSK-FS exhibit robustness when the samples contain noise. On three rice leaf disease datasets and six real-world non-rice leaf disease datasets, experiments were conducted on three metrics, namely accuracy, GM, and rule complexity. The experimental results show that the KE-0-TSK-FS method outperforms other comparative algorithms in terms of generalization ability, interpretability, and robustness in the diagnosis of rice leaf diseases under insufficient samples and noise situations, and its average accuracy on rice leaf disease datasets is nearly 3% higher than that of other comparative algorithms.

Data-Efficient Vision Transformer Models for Robust Classification of Sugarcane

Sugar cane is an important agricultural product that provides 75% of the world's sugar production. As with all plant species, any disease affecting sugarcane can significantly impact yields and planning. Diagnosing diseases in sugarcane leaves using traditional methods is slow, inefficient, and often lacking in accuracy. This study presents a deep learning-based approach for accurate diagnosis of diseases in sugarcane leaves. Specifically, training and evaluation were conducted on the publicly available Sugarcane Leaf Dataset using leading ViT (Vision Transformer) architectures such as DeiT3-Small and DeiT-Tiny. This dataset includes 11 different disease classes and a total of 6748 images. Additionally, these models were compared with popular CNN models. The findings of the study show that there is no direct relationship between model complexity, depth, and accuracy for the 11-class sugarcane dataset. Among the 12 models tested, the DeiT3-Small model showed the highest performance with 93.79% accuracy, 91.27% precision, and 90.96% F1-score. These results highlight that rapid, accurate, and automatic disease diagnosis systems developed using deep learning techniques can significantly improve sugarcane disease management and contribute to increased yields.

Intelligent cardiovascular disease diagnosis system combined piezoelectric nanogenerator based on 2D Bi2O2Se with deep learning technique

Two-dimensional (2D) piezoelectric nanogenerators (PENGs) have great potential in capturing weak physiological signals on the body surface due to their superior flexibility and high sensitivity. However, there are few reports on utilizing 2D PENG to capture physiological signals for identifying human health status, and to conduct clinical trials. Here, we reported an intelligent cardiovascular disease diagnosis system that integrates a self-powered pulse sensor based on 2D Bi2O2Se PENG and deep learning (DL) technology to accurately identify nine common cardiovascular diseases. The significant piezoelectricity of Bi2O2Se nanosheets prepared by chemical vapor deposition was experimentally validated via piezoresponse force microscopy. The open-circuit voltage (VOC) and short-circuit current (ISC) of the 2D PENG can reach 60 mV and 2 nA under 0.6 % strain, respectively. The 2D PENG attached to the skin can sense the three characteristic peaks of pulse signals in different body statuses, and then extract some predictive indicators reflecting cardiovascular diseases. Clinical trials show that the intelligent cardiovascular disease diagnosis system can accurately identify nine common diseases with a recognition accuracy of 93.75 %. Our study indicates that 2D PENG has an enormous potential for long-term non-invasive health monitoring, and provides a new strategy for early diagnosis of cardiovascular diseases.

A new clinical diagnosis system for detecting brain tumor using integrated ResNet_Stacking with XGBoost

The cancer disease prediction and detection processes are crucial tasks in this emerging world and it is tough to manage the diseases. Generally, the disease prediction processes are done by using various kinds of inputs including numerical data and Magnetic Resonance Imaging (MRI) images. In the past, the researchers use the segmentation and classification methods on MRI images to enrich performance of the disease prediction system. However, these input images are using for the prediction, detection and diagnosis with reasonable delay and less prediction accuracy. In this work, we propose a new brain tumor disease prediction and diagnosis system that incorporates the segmentation and classification techniques for predicting and detecting the cancer diseases effectively. Here, new data pre-processing methods like mean fusion, automated cropping of the Region of Interest (ROI), and Gaussian filtering are used for performing effective classification. Moreover, the proposed system performs the standardized resizing and rescaling processes over the MRI images and it also automate the cropping process of the ROI to ensure consistent input sizes. In addition, the brain tumor segmentation process is performed by applying the LinkNet architecture with a SEResNeXt101 backbone network. Finally, the system focuses on brain tumor classification using ensemble model which combines the ResNet architectures through stacking, leveraging the strengths of individual models to improve classification accuracy in the first stage, and it incorporates the XGBoost algorithm to enhance the performance further in efficient manner. The proposed disease prediction system is assessed through experiments on MRI images. Finally, the system is proved as superior to other available disease prediction systems with respect to sensitivity, specificity, AUC and obtained 95.84% as overall prediction accuracy.

Machine Learning Based Decision Support System for Coronary Artery Disease Diagnosis

Machine Learning Based Decision Support System for Coronary Artery Disease Diagnosis

Dental Abnormalities in Pediatric Patients Receiving Chemotherapy.

Background: Chemotherapy is a common treatment for pediatric cancer. Although life prognosis is improving because of advances in medical science, it is important to deal with late effects such as dental abnormalities. We investigated the association between dental abnormalities and chemotherapy by age and tooth type. Methods: Among the 568 patients referred to the pediatric dentistry department of our hospital, we selected 32 patients (21 male and 11 female) who received chemotherapy between the ages of 0 and 6 and underwent panoramic examination after the age of 7. We recorded the age of chemotherapy commencement, diagnosis of systemic disease, and dental abnormalities such as congenital absence, microdonts, and short-rooted teeth. Results: Almost half of the patients had dental abnormalities such as congenital absence, microdonts, and short-rooted teeth, but there were no significant differences in the incidence of these abnormalities by age. When we analyzed the incidence of abnormal teeth by tooth type, the incidence of congenital absence was significantly higher in premolars (5.5%) and second molars (3.9%) than in incisor or canine or 1st molar (0.4%) (p < 0.01). The incidence of microdonts was significantly higher in premolars (3.9%) than in incisor or canine or 1st molar (0.2%) and second molars (0.0%) (p < 0.05). Conclusions: Patients who received chemotherapy had a high prevalence of dental abnormalities, and the incidence of abnormalities varied by tooth type. It is important to maintain long-term oral care for patients who have undergone chemotherapy even after the treatment is completed.

Febrile disease modeling and diagnosis system for optimizing medical decisions in resource-scarce settings

Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high temperatures. These regions, mainly comprising low- and middle-income countries, often face challenges related to inadequate medical infrastructure and a lack of skilled personnel for accurately diagnosing febrile diseases. Distinguishing one febrile illness from another posed a significant challenge, adding to the complexity of accurate diagnoses. This study developed a multi-symptom multi-disease model to address this challenge, leveraging exploratory data analysis of patient datasets from field studies and the expertise of medical practitioners specializing in tropical diseases. The research investigated the most effective modeling approach for differentiating among 11 febrile illnesses that are prevalent in Nigeria using three intelligent techniques: Extreme Gradient Boost (XGBoost), Fuzzy Cognitive Map (FCM), and Analytic Hierarchy Process (AHP). Comparative analysis demonstrates that AHP surpassed the others, achieving a precision of 84%, recall of 83%, and an F1-score of 84%. Consequently, the AHP technique was integrated into the development of “Febra Diagnostica,” an app aimed at enhancing febrile disease diagnosis in resource-constrained settings. The app was then deployed and utilized in select Nigerian states, offering scalability and empowering frontline health workers in primary health facilities. Febra Diagnostica featured user-friendly interfaces, automated diagnosis and treatment suggestions, streamlined referrals, and provisions for further investigations. Encryption, access control, and multi-factor authentication were some of the security and privacy considerations in the app which gained acceptance from medical experts and adapted to regulatory and ethical policies for smart healthcare systems.

Establishment of an automatic diagnosis system for corneal endothelium diseases using artificial intelligence

PurposeTo use artificial intelligence to establish an automatic diagnosis system for corneal endothelium diseases (CEDs).MethodsWe develop an automatic system for detecting multiple common CEDs involving an enhanced compact convolutional transformer (ECCT). Specifically, we introduce a cross-head relative position encoding scheme into a standard self-attention module to capture contextual information among different regions and employ a token-attention feed-forward network to place greater focus on valuable abnormal regions.ResultsA total of 2723 images from CED patients are used to train our system. It achieves an accuracy of 89.53%, and the area under the receiver operating characteristic curve (AUC) is 0.958 (95% CI 0.943–0.971) on images from multiple centres.ConclusionsOur system is the first artificial intelligence-based system for diagnosing CEDs worldwide. Images can be uploaded to a specified website, and automatic diagnoses can be obtained; this system can be particularly helpful under pandemic conditions, such as those seen during the recent COVID-19 pandemic.

Intelligent preliminary diagnosis system for diseases with similar clinical presentation

Accurately identifying diseases with similar symptoms, especially in resource-limited medical settings, is a key challenge to diagnostic accuracy. This paper presents a preliminary diagnostic system to address the challenge of diagnosing diseases with similar symptoms. The system has been implemented using the PyQt5 library and employs a unique symptom identification algorithm developed in Python. Furthermore, to carry out the diagnosis, it uses comma-separated values (CSV) and excel files as databases where the diseases and their respective symptoms are stored. The results show that the system has a precision of 95%, a sensitivity of 90%, and a specificity of 93% after evaluating 35 clinical cases covering seven diseases with similar initial symptoms, namely: dengue, zika, chikungunya, COVID-19, influenza, monkey-pox, and the common cold. Furthermore, the positive evaluation of the technical performance of the system by experts supports its practical feasibility and its potential as a valuable tool in medical practice. In conclusion, the system diagnoses diseases by analyzing the symptoms of the information file, highlighting its usefulness in improving the diagnostic accuracy of similar cases and optimizing medical care for the benefit of patients.

A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment

A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment

Characterizing autoimmune uveitis to systemic diseases: a retrospective study from a Syrian tertiary reference center.

Uveitis, a notable cause of severe visual impairment, is frequently characterized as infectious or noninfectious autoimmune uveitis (AU), the latter of which is commonly associated with younger individuals and systemic diseases. Despite the condition's widespread impact, there are substantial gaps in the comprehension of its pathogenesis, clinical presentation, and therapeutic response, particularly concerning systemic disease-associated uveitis. The current study aims to bridge these gaps through an extensive examination of demographic and clinical features in AU patients, thereby informing future research, and therapeutic strategies, and improving patient outcomes. This retrospective observational study analyzed 261 patients with systemic disease-associated uveitis from January 2018 to December 2022 in Damascus, Syria. With diagnoses made using the Standardization of Uveitis Nomenclature Working Group Criteria, the study evaluated tailored treatment efficacy at the 24-month post-treatment mark, alongside comprehensive ophthalmic examinations, laboratory evaluations, and radiographic assessments. In our study, included 87 patients with Systemic Disease-Associated Autoimmune Uveitis (SDA-AU). Women represented 64.36% of this group, and the mean age at diagnosis was 39.8±17.9 years (range 7-71) for men and 43.8±15.4 years (range 11-69). The most reported symptom was a painful red eye (52.87%). The onset of symptoms was sudden for 32.18% of patients, while 67.81% reported gradual development. Complications occurred in 33.33% of patients, including cataracts (41.37% of those with complications) and glaucoma (17.24%). Laboratory evaluations showed elevated inflammation markers in 66.66% of patients. Upon the 24-month assessment, 48.27% of patients achieved complete remission, 37.93% showed significant improvement, while disease worsened in 13.79% of cases. Our findings demonstrated that the presentation of AU in this cohort frequently precedes the diagnosis of systemic diseases, affirming the vital role of an early and accurate diagnosis of uveitis for the detection of underlying systemic conditions. In conclusion, our study underlines the significance of a comprehensive and multidisciplinary approach in the management of SD-AU, leading to improved prognosis and quality of life for patients.

Distributed U-net model-based image segmentation for lung cancer detection

Until now, in the wake of the COVID-19 pandemic in 2019, lung diseases, especially diseases such as lung cancer and Chronic Obstructive Pulmonary Disease (COPD), have become an urgent global health issue. In order to mitigate the goal problem, early detection and accurate diagnosis of these conditions are critical for effective treatment and improved patient outcomes. To further research and reduce the error rate of hospital diagnoses, this comprehensive study explored the potential of Computer-Aided Design (CAD) systems, especially utilizing advanced deep learning models such as U-Net. And compared with the literature content of other authors, this study explores the capabilities of U-Net in detail and enhances the ability to simulate CAD systems through the VGG16 algorithm. An extensive dataset consisting of lung CT images and corresponding segmentation masks, curated collaboratively by multiple academic institutions, serves as the basis for empirical validation. In this paper, the efficiency of U-Net model is evaluated rigorously and precisely under multiple hardware configurations, such as single CPU, single GPU, distributed GPU and federated learning, and the effectiveness and development of the method in the segmentation task of lung disease are demonstrated. Empirical results clearly affirm the robust performance of the U-Net model, most effectively utilizing four GPUs for distributed learning, and these results highlight the potential of U-Net-based CAD systems for accurate and timely lung disease detection and diagnosis huge potential.