Detection Of Disease Research Articles

CRISPR/Cas12a-coupled multiplexed strand displacement amplification for miRNA155 one-tube detection: via a dual-cavity PCR tube.

A CRISPR/Cas12a-coupled multiplexed strand displacement amplification (CMSDA) for the detection of miR155 has been developed. Non-specific amplification was avoided by designing a single-stranded DNA template with a hairpin structure. The detection target miR155 was used as a primer to initiate a multiple-strand displacement reaction to produce abundant ssDNA. ssDNA was recognized by the Cas12a/CrRNA binary complex, activating the trans-cleaving activity of Cas12a. The multiple-strand displacement reaction is more efficiently detected compared with a single-strand displacement reaction. The detection range is from 250pM to 1nM, and the limit of the detection is 6.5pM. The proposed method showed a good applicability in complex serum environments, indicating that the method has a broad prospect for disease detection and clinical application. In addition, we designed a dual-cavity PCR tube, which realized one-tube detection of miRNA155 and avoided open-cap contamination.

Retraction Note: Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images

Retraction Note: Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images

Transfer learning by VGG-16 with convolutional neural network for paddy leaf disease classification

ABSTRACT One of the most recent areas of research in agriculture is the detection along with classification of diseases from plant leaf images. Since rice is the most widely consumed staple food worldwide, it is crucial to increase paddy production’s quality and quantity. In the production of paddy, early detection of pests and diseases at various growth stages is very important. Utilising image processing scheme to spot diseases of agricultural plants will lessen the need for farmers to safeguard agricultural products. The use of VGG-16 along with a Convolutional Neural Network (CNN) for the classification and recognition of paddy leaf diseases is proposed in this work. The rice plant leaves images taken from the kaggle dataset is utilised for the purpose of image acquisition. The Gaussian filter is used in pre-processing. The clustering technique is utilised for the segmentation of the diseased part, the normal part, and the surroundings. The proposed model is then utilised for disease classification. This study classifies 10 categories of paddy images. VGG16, InceptionV3, MobileNetV2, and ResNet-152, among other transfer learning methods, are evaluated and contrasted with the experimental results. The outcome accuracy of the proposed model achieves of nearly 99.9%.

Advancing Crayfish Disease Detection: A Comparative Study of Deep Learning and Canonical Machine Learning Techniques

Advancing Crayfish Disease Detection: A Comparative Study of Deep Learning and Canonical Machine Learning Techniques

Lung Sound Classification for Respiratory Disease Identification Using Deep Learning: A Survey

Integrating artificial intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analysing intricate patterns within audio data. This study is driven by the widespread issue of lung diseases, which affect around 500 million people globally. Early detection of respiratory diseases is crucial for delivering timely and effective treatment. Our study consists of a comprehensive survey of lung sound classification methodologies, exploring the advancements made in leveraging AI to identify and classify respiratory diseases. This survey thoroughly investigates lung sound classification models, along with data augmentation, feature extraction, explainable techniques and support tools to improve systems for diagnosing respiratory conditions. Our goal is to provide meaningful insights for healthcare professionals, researchers and technologists who are dedicated to developing methodologies for the early detection of pulmonary diseases. The paper provides a summary of the current status of lung sound classification research, highlighting both advancements and challenges in the use of AI for more accurate and efficient diagnostic methods in respiratory healthcare.

The prediction of Parkinson's disease based on Pearson coefficient feature screening and machine learning

Parkinson's disease is a common and severe type of brain disease. Its incidence rate is relatively high among brain diseases. At present, there is no very effective treatment for Parkinson's disease. So researchers have focused on diagnosing Parkinson's disease. At present, machine learning methods have been applied in the medical field and have played a very positive role in the diagnosis of diseases. It has been proven that analyzing the patient's voice and trembling condition through machine learning can accurately diagnose Parkinson's disease. In this study, before training the model, we used the Pearson coefficient feature screening method to improve the accuracy of diagnosis. Then, we conducted training on six major models (Random Forest, GBDT, Adaboost, Logistic Regression, Decision Tree, XGboost) in order to find the model with the best performance. In this study, we found that the performance of Random Forest is the best in these models (Accuracy: 91.53%, recall: 100%), then is the GBDT model (Accuracy: 91.53%, recall: 97.78%). The other four models all have a great disparity on accuracy and recall, which are the two most important metrics on the detection of diseases. The research results have demonstrated that the feature selection method based on Pearson's coefficient indeed comprehensively improves the accuracy of diagnosis for Parkinson's disease. And we also found that in the process of diagnosing Parkinson's disease, the performance of the Random Forest and GBDT models is the best.

Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer

Machine learning techniques applied in neuroimaging have prompted researchers to build models for early diagnosis of brain illnesses such as Alzheimer’s disease (AD). Although this task is difficult, advanced deep-learning (DL) approaches can be used. These DL models are effective, but difficult to interpret, time-consuming, and resource-intensive. Therefore, neuroscientists are interested in employing novel, less complex structures such as transformers that have superior pattern-extraction capabilities. In this study, an automated framework for accurate AD diagnosis and precise stage identification was developed by employing vision transformers (ViTs) with fewer computational resources. ViT, which captures the global context as opposed to convolutional neural networks (CNNs) with local receptive fields, is more efficient for brain image processing than CNN because the brain is a highly complex network with connected parts. The self-attention mechanism in the ViT helps to achieve this goal. Magnetic resonance brain images belonging to four stages were utilized to develop the proposed model, which achieved 99.83% detection accuracy, 99.69% sensitivity, 99.88% specificity, and 0.17% misclassification rate. Moreover, to prove the ability of the model to generalize, the mean distances of the transformer blocks and attention heat maps were visualized to understand what the model learned from the MRI input image.

E-LSTM: EfficientNet and Long Short-Term Memory Model for Detection of Glaucoma Diseases

Glaucoma is an eye disease that often has no symptoms until it is advanced. According to the World Health Organization (WHO), after cataracts, glaucoma is the second-leading cause of permanent blindness globally and is expected to affect 111.8 million patients by 2040. Early detection of glaucoma is important to reduce the risk of permanent blindness. Detection is achieved by structural measurement of early thinning of the retinal nerve fiber layer (RNFL). The RNFL is the portion of the retina located outside the optic nerve head (ONH) and can be observed in fundus images of the retina. Analysis of retinal fundus images can be performed with computer assistance using machine learning, especially deep learning. This study proposes a deep learning-based model, a convolutional neural network (CNN) using the EfficientNet architecture combined with long short-term memory (LSTM), for laucoma detection. Using ACRIMA, DRISHTI-GS, and RIM-ONE DL datasets with k-fold cross-validation, the model achieved high performance on the ACRIMA dataset: accuracy 0.9799, loss 0.0596, precision 0.9802, sensitivity 0.9799, specificity 0.9771, and F1score 0.9799. This EfficientNet and LSTM combination (e-LSTM) outperformed previous studies, offering a promising alternative for evaluating retinal fundus images in glaucoma detection.

The impact of preventive behaviors on self-rated health, depression symptoms, and daily functioning among middle-aged and elderly Chinese: An empirical study.

With the intensifying issue of an aging population, the health of middle-aged and elderly individuals garners increased attention. Preventive behaviors are pivotal in enhancing life quality and extending healthy living. This study examines the effects of preventive behaviors on self-rated health, depression, and daily functioning among these populations. Drawing on data from the China Health and Retirement Longitudinal Study (CHARLS), this research applies a panel ordered probability model to scrutinize the influence of preventive behaviors on health outcomes among middle-aged and elderly populations. It utilizes self-rated health, depression, and daily functioning as pivotal health indicators to assess the effects. Preventive behaviors exert a significant impact on self-assessed health and daily functioning among middle-aged and elderly populations. Engagement in social activities effectively reduces depression symptoms. Primary preventive measures, including physical and social activities, enhance health outcomes through medical consultations. Conversely, secondary preventive actions, such as undergoing physical examinations, facilitate early detection of diseases, enabling timely intervention and health advisories. It is noteworthy that individuals with higher incomes derive lesser benefits from these physical or social endeavors. Sociodemographic determinants such as age, income, and educational attainment significantly modulate the efficacy of preventive behaviors on the health outcomes of middle-aged and elderly populations. This research underscores the pivotal role of physical examination services within primary healthcare frameworks and advocates for the tailoring of health promotion strategies to the accessible social needs and engagements of economically and educationally disadvantaged seniors.

Empowering Diabetic Eye Disease Detection: Leveraging Differential Evolution for Optimized Convolution Neural Networks

Diabetic eye detection has become a major concern across the globe, which could be effectively addressed by automated detection using a deep convolutional neural network (DCNN). CNN models have better detection and classification accuracy than other state-of-theart models. In this paper, a differential evolution (DE)-optimized CNN has been proposed for the single-step classification of diabetic retinopathy (DR) and glaucoma images. DE has been used to find out the optimized values of four hyper-parameters of CNN, i.e., the number of filters in the first layer, the filter size, the number. of convolution layers, and the number of strides. Simulation has been done using three publicly available datasets, and the accuracy obtained is 87.8%, 92.3%, and 88.7%, respectively, which outperforms other models. No other state-of-the-art model has used DE for hyper-parameter tuning in CNN models. Also, no other additional segmentation approach or handcrafted features have been used. The model has been kept simple to reduce computational costs.

Development of a Robust CNN Model for Mango Leaf Disease Detection and Classification: A Precision Agriculture Approach

Development of a Robust CNN Model for Mango Leaf Disease Detection and Classification: A Precision Agriculture Approach

Hyperspectral Image Analysis and Machine Learning Techniques for Crop Disease Detection and Identification: A Review

Hyperspectral Image Analysis and Machine Learning Techniques for Crop Disease Detection and Identification: A Review

Implementation and Evaluation of Attention Aggregation Technique for Pear Disease Detection

In this study, a novel approach integrating multimodal data processing and attention aggregation techniques is proposed for pear tree disease detection. The focus of the research is to enhance the accuracy and efficiency of disease detection by fusing data from diverse sources, including images and environmental sensors. The experimental results demonstrate that the proposed method outperforms in key performance metrics such as precision, recall, accuracy, and F1-Score. Specifically, the model was tested on the Kaggle dataset and compared with existing advanced models such as RetinaNet, EfficientDet, Detection Transformer (DETR), and the You Only Look Once (YOLO) series. The experimental outcomes indicate that the proposed model achieves a precision of 0.93, a recall of 0.90, an accuracy of 0.92, and an F1-Score of 0.91, surpassing those of the comparative models. Additionally, detailed ablation experiments were conducted on the multimodal weighting module and the dynamic regression loss function to verify their specific contributions to the model performance. These experiments not only validated the effectiveness of the proposed method but also demonstrate its potential application in pear tree disease detection. Through this research, an effective technological solution is provided for the agricultural disease detection domain, offering substantial practical value and broad application prospects.

The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease.

The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments. Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management. The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries. As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.

Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato

AbstractA machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.

Mechanical rheological model on the assessment of elasticity and viscosity in tissue inflammation: A systematic review.

Understanding the extent of inflammation is crucial for early disease detection, monitoring disease progression, and evaluating treatment responses. Over the past decade, researchers have demonstrated the need to understand the extent of inflammation through qualitative or quantitative characterization of tissue viscoelasticity using different techniques. In this scientific review, an examination of research on the association between elasticity and Viscosity in diseases, particularly as tissue inflammation progresses, is conducted. A review of utilizing mechanical rheological models to characterize quantitative viscoelastic parameters of normal and inflamed tissues is also undertaken. Based on inclusion and exclusion criteria, we identified 14 full-text studies suitable for review out of 290 articles published from January 2000 to January 2024. We used PRISMA guidelines for the systematic review. In the review, three studies demonstrated the criterion used by the researchers in identifying the best rheological model. Eleven studies showed the clinical application of the rheological model in quantifying the viscoelastic properties of normal and pathological tissue. The review quantified viscoelastic parameters for normal and pathological tissue across various soft tissues. It evaluated the effectiveness of each viscoelastic property in distinguishing between normal and pathological tissue stiffness. Furthermore, the review outlined additional viscoelastic-related parameters for researchers to consider in future stiffness classification studies.

Advancing Microfluidic Immunity Testing Systems: New Trends for Microbial Pathogen Detection

Pathogenic microorganisms play a crucial role in the global disease burden due to their ability to cause various diseases and spread through multiple transmission routes. Immunity tests identify antigens related to these pathogens, thereby confirming past infections and monitoring the host’s immune response. Traditional pathogen detection methods, including enzyme-linked immunosorbent assays (ELISAs) and chemiluminescent immunoassays (CLIAs), are often labor-intensive, slow, and reliant on sophisticated equipment and skilled personnel, which can be limiting in resource-poor settings. In contrast, the development of microfluidic technologies presents a promising alternative, offering automation, miniaturization, and cost efficiency. These advanced methods are poised to replace traditional assays by streamlining processes and enabling rapid, high-throughput immunity testing for pathogens. This review highlights the latest advancements in microfluidic systems designed for rapid and high-throughput immunity testing, incorporating immunosensors, single molecule arrays (Simoas), a lateral flow assay (LFA), and smartphone integration. It focuses on key pathogenic microorganisms such as SARS-CoV-2, influenza, and the ZIKA virus (ZIKV). Additionally, the review discusses the challenges, commercialization prospects, and future directions to advance microfluidic systems for infectious disease detection.

MS-YOLOv8-Based Object Detection Method for Pavement Diseases

Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network’s neck performs Spatially Weighted Dilated Convolution (SWDA) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection.

Semi-Supervised One-Stage Object Detection for Maize Leaf Disease

Maize is one of the most important crops globally, and accurate diagnosis of leaf diseases is crucial for ensuring increased yields. Despite the continuous progress in computer vision technology, detecting maize leaf diseases based on deep learning still relies on a large amount of manually labeled data, and the labeling process is time-consuming and labor-intensive. Moreover, the detectors currently used for identifying maize leaf diseases have relatively low accuracy in complex experimental fields. Therefore, the proposed Agronomic Teacher, an object detection algorithm that utilizes limited labeled and abundant unlabeled data, is applied to maize leaf disease recognition. In this work, a semi-supervised object detection framework is built based on a single-stage detector, integrating the Weighted Average Pseudo-labeling Assignment (WAP) strategy and AgroYOLO detector combining Agro-Backbone network with Agro-Neck network. The WAP strategy uses weight adjustments to set objectness and classification scores as evaluation criteria for pseudo-labels reliability assignment. Agro-Backbone network accurately extracts features of maize leaf diseases and obtains richer semantic information. Agro-Neck network enhances feature fusion by utilizing multi-layer features for collaborative combinations. The effectiveness of the proposed method is validated on the MaizeData and PascalVOC datasets at different annotation ratios. Compared to the baseline model, Agronomic Teacher leverages abundant unlabeled data to achieve a 6.5% increase in mAP (0.5) on the 30% labeled MaizeData. On the 30% labeled PascalVOC dataset, the mAP (0.5) improved by 8.2%, demonstrating the method’s potential for generalization.

AI AND ANDROID APP BASED TOMATO PLANT DISEASE PREDICTION SYSTEM

Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species. Keywords: plant leaf diseases; agriculture; mobile app; convolutional neural networks (CNN); deep learning