Detection Of Disease Research Articles

Detection and molecular characterization of lumpy skin disease and bovine papular stomatitis viruses in lumpy skin disease-suspected outbreaks in Tanzania.

Lumpy Skin Disease (LSD) is endemic in sub-Saharan countries and is currently a global threat to the cattle industry. Information on the circulating Capripoxvirus lumpyskinpox, formerly known as Lumpy Skin Disease Virus (LSDV), and other poxviruses infecting cattle is very scant in Tanzania. The current study aimed to confirm and characterize LSDV and other poxviruses infecting cattle, from LSD suspected outbreaks in Tanzania. A total of 24 samples were collected from four LSD suspected outbreaks reported in Tanzania between February and May 2023. Samples were screened for LSDV genome by real-time PCR and then subjected to a high-resolution multiplex melting (HRM) assay where 10 samples were positive for Capripoxvirus (CaPV) and one sample was Parapoxvirus (PPV) positive. Four LSDV genes; RPO30, GPCR, EEV glycoprotein and B22R and the partial B2L gene of PPVs were analyzed. All targeted LSDV genes from the Tanzanian isolates showed 100% similarity and isolates clustered with commonly circulating LSDV field isolates. Furthermore, the single nucleotide polymorphism (SNP) at position 240 (A-> G) of the EEV gene differentiates the Tanzanian LSDVs from the group of ancient Kenyan LSDV isolates while the B22R sequences of the Tanzanian LSDV isolates differed from the LSDV Neethling and LSDV KSGP-0240 derived vaccines. Sequence analysis of the partial B2L gene of the Tanzanian parapoxvirus bovinestomatitis, formerly known as Bovine papular stomatitis virus (BPSV) showed a different BPSV strain circulating compared to publicly available sequences. These findings confirm the presence of LSDV in Tanzania, which suggesting the need for establishing an effective control program and continuous monitoring. The presence of a typical profile for Tanzania BPSV is an indication that, although never reported before, BPSV is established in the country therefore this virus should be included in the differential diagnosis of LSDV.

Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer.

Image based screening is now routinely available for early detection of cancer and other diseases. Quantitative analysis for effects of risk factors on digital images is important to extract biological insights for modifiable factors in prevention studies and understand pathways for targets in preventive drugs. However, current approaches are restricted to summary measures within the image with the assumption that all relevant features needed to characterize an image can be identified and appropriately quantified. Motivated by data challenges in breast cancer, we propose a nonparametric statistical framework for risk factor screening that uses the whole mammogram image as outcome. The proposed permutation test allows assessment of whether a set of scalar risk factors is associated with the whole image in the presence of correlated residuals across the spatial domain. We provide extensive simulation studies and illustrate an application to the Joanne Knight Breast Health Cohort using the mammogram imaging data.

Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review

Deep Learning-Based Classification Methods for Detection of Diseases in Rice Leaves – A Review

Community-Based Point-of-Care Screening for Hepatitis B Virus and Hepatocellular Carcinoma in Rural Tanzania.

Sub-Saharan Africa has a high burden of hepatitis B virus (HBV) and hepatocellular carcinoma (HCC). The lack of surveillance programs has led to low rates of diagnosis and treatment, particularly in rural areas. We conducted mobile HBV-HCC screening clinics in rural Tanzania between March 2021 and February 2023. After undergoing informed consent, patients completed a questionnaire about HBV. A rapid point-of-care (POC) assay measured HBV surface antigen (HBsAg), and HBsAg-positive patients underwent POC ultrasound to screen for HCC and POC hepatitis C (HCV) antibody testing. The primary outcome was number of HBV diagnoses, and the secondary outcome was prevalence of liver masses in HBsAg-positive individuals. Data were analyzed with descriptive statistics. Five hundred and one patients were screened for HBV; 63% (n = 303) were female with median (interquartile range [IQR]) age of 40 (28-55) years. Only 6% (n = 30) reported being vaccinated against HBV, 92% (n = 453) reported no vaccination, and 2% (n = 12) did not know their vaccination status. Seventy-six percent (n = 340) did not know they should get vaccinated, and 4% (n = 16) reported that vaccination was too expensive. Two percent (n = 11) of patients were positive for HBsAg, with 55% (n = 6) of those being female with median (IQR) age of 36 (34-43) years. None of the HBsAg-positive patients reported being vaccinated against HBV, and all were negative for HCV. On ultrasound, one patient had a liver mass, and another had ascites. We demonstrated that community-based HBV and HCC screening can be implemented in Africa with local partnerships, and this model could be used to promote awareness and improve early detection of disease.

Nanotechnology in retinal diseases: From disease diagnosis to therapeutic applications.

Nanotechnology has demonstrated tremendous promise in the realm of ocular illnesses, with applications for disease detection and therapeutic interventions. The nanoscale features of nanoparticles enable their precise interactions with retinal tissues, allowing for more efficient and effective treatments. Because biological organs are compatible with diverse nanomaterials, such as nanoparticles, nanowires, nanoscaffolds, and hybrid nanostructures, their usage in biomedical applications, particularly in retinal illnesses, has increased. The use of nanotechnology in medicine is advancing rapidly, and recent advances in nanomedicine-based diagnosis and therapy techniques may provide considerable benefits in addressing the primary causes of blindness related to retinal illnesses. The current state, prospects, and challenges of nanotechnology in monitoring nanostructures or cells in the eye and their application to regenerative ophthalmology have been discussed and thoroughly reviewed. In this review, we build on our previously published review article in 2021, where we discussed the impact of nano-biomaterials in retinal regeneration. However, in this review, we extended our focus to incorporate and discuss the application of nano-biomaterials on all retinal diseases, with a highlight on nanomedicine-based diagnostic and therapeutic research studies.

A Method for Retina Segmentation by Means of U-Net Network

Retinal image segmentation plays a critical role in diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy and age-related macular degeneration. We propose a deep learning-based approach utilizing the U-Net network for the accurate and efficient segmentation of retinal images. U-Net, a convolutional neural network widely used for its performance in medical image segmentation, is employed to segment key retinal structures, including the optic disc and blood vessels. We evaluate the proposed model on a publicly available retinal image dataset, demonstrating interesting performance in automatic retina segmentation, thus showing the effectiveness of the proposed method. Our proposal provides a promising method for automated retinal image analysis, aiding in early disease detection and personalized treatment planning.

A generalised computer vision model for improved glaucoma screening using fundus images.

Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images could enhance early and accurate disease detection. To develop and validate a generalised deep-learning-based algorithm for screening glaucoma using fundus image. The glaucomatous fundus data were collected from 20 publicly accessible databases worldwide, resulting in 18,468 images from multiple clinical settings, of which 10,900 were classified as healthy and 7568 as glaucoma. All the data were evaluated and downsized to fit the model's input requirements. The potential model was selected from 20 pre-trained models and trained on the whole dataset except Drishti-GS. The best-performing model was further trained to classify healthy and glaucomatous fundus images using Fastai and PyTorch libraries. The model's performance was compared against the actual class using the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, precision and the F1-score. The high discriminative ability of the best-performing model was evaluated on a dataset comprising 1364 glaucomatous discs and 2047 healthy discs. The model reflected robust performance metrics, with an AUROC of 0.9920 (95% CI: 0.9920-0.9921) for both the glaucoma and healthy classes. The sensitivity, specificity, accuracy, precision, recall and F1-scores were consistently higher than 0.9530 for both classes. The model performed well on an external validation set of the Drishti-GS dataset, with an AUROC of 0.8751 and an accuracy of 0.8713. This study demonstrated the high efficacy of our classification model in distinguishing between glaucomatous and healthy discs. However, the model's accuracy slightly dropped when evaluated on unseen data, indicating potential inconsistencies among the datasets-the model needs to be refined and validated on larger, more diverse datasets to ensure reliability and generalisability. Despite this, our model can be utilised for screening glaucoma at the population level.

A secure fault detection for digital microfluidic biochips

Abstract Among all the modern technological advances, digital microfluidic biochip has been extending a salient solution to healthcare and bio-laboratories with the pledge of high sensitivity and reconfigurability. Such biochip devices fulfill the requirement of a faster testing kit for the detection of different novel diseases, which is indispensable in the market due to the tremendously disrupted scenario of healthcare systems. To eliminate erroneous testing, the current scope of digital microfluidic biochips is widened as a viable testing method using various bioprotocols with a reduced cost in developing countries. This paper addresses the existing security challenges and operational faults in the identification mechanism of proteins such as severe acute respiratory syndrome coronavirus 2 spike protein in state-of-the-art digital microfluidic biochips. We are the first to propose a safety detection solution along with a fault identification algorithm using an inductive transfer learning model. Experimental results of the proposed model register a threshold accuracy of 98% while applying the own dataset. This work will provide a better security-enabled fault-free safety assurance framework against attack and fault identification with better accuracy in digital microfluidic biochips for the detection of different diseases and many other healthcare diagnoses, without any overhead of completion time for bioprotocols.

Deepincepnet: Disease Detection in Corn or Maize Plant Leaves Using Specim Iq Hyperspectral Imaging and Proposed Dnn Classifiers with Inception Networks

Deepincepnet: Disease Detection in Corn or Maize Plant Leaves Using Specim Iq Hyperspectral Imaging and Proposed Dnn Classifiers with Inception Networks

Concurrently Probing the Mechanical and Electrical Characteristics of Living Cells via an Integrated Microdevice.

The mechanical and electrical properties of cells serve as critical indicators of their physiological and pathological state. Currently, distinct setups are required to measure the electrical and mechanical responses of cells. In addition, most existing methods such as optical trapping (OT) and atomic force microscopy (AFM) are labor-intensive, expensive, and low-throughput. Here, we developed a microdevice that integrates automated cell trapping, deformation, and electric impedance spectroscopy to overcome these limitations. Our device enables parallel aspiration of tens of trapped cells in a highly scalable manner by simply adjusting the applied pressures, allowing for rapid probing of the dynamic viscoelastic properties of cells. Furthermore, embedded microelectrodes enable concurrent investigations of the electrical impedance of the cells. Through testing on different cell types, our platform demonstrated superior capabilities in comprehensive cell characterization and phenotyping, highlighting its great potential as a versatile tool for single cell analysis, drug screening, and disease detection.

Disease Detection in Corn or Maize Plant Leaves Using Specim Iq Hyperspectral Imaging and Proposed Dnn Classifiers with Alexnet

Disease Detection in Corn or Maize Plant Leaves Using Specim Iq Hyperspectral Imaging and Proposed Dnn Classifiers with Alexnet

Advanced deep learning approaches for early detection and localization of ocular diseases

Rece with t advancements in modern technology have significantly enhanced the transmission of information, particularly in image processing, utilizing deep learning algorithms. This study aims to propose a a robust deep-learning strategy for detecting and recognizing eye defects and diseases from medical images. We present a detailed practical simulation of hybrid deep learning techniques designed for medical image classification based on multi-descriptor algorithms. The focus is on the classification of eye diseases by applying an advanced deep-learning algorithm to a dataset comprising various pathological eye conditions. Training operations for the proposed algorithm were conducted following the initialization phase, which included the extraction of multi-specification features. This enables the deep learning model to effectively analyze input eye images and accurately diagnose conditions. Our results demonstrate a diagnostic efficiency of 99%, with an error rate not exceeding 0.015%. The findings underscore the high efficiency and accuracy of deep learning algorithms in classifying and analyzing image data, thereby significantly reducing the workload for healthcare professionals.

Diagnostic performance of high and ultra-high-resolution photon counting CT for detection of coronary artery disease in patients evaluated for transcatheter aortic valve implantation.

We assessed the diagnostic performance of both ultra-high-resolution (UHR) and high-resolution (HR) modes of photon-counting detector (PCD)-CT within the confines of standard pre-TAVI CT scans, as well as the performance of UHR mode adjusted specifically for coronary imaging, using quantitative coronary angiography (QCA) as the reference. We included 60 patients undergoing pre-TAVI planning CT scans. Patients were divided into 3 groups: 20 scanned in HR mode, 20 in UHR mode, and 20 in adjusted UHR mode, on a dual-source PCD-CT. The adjusted UHR mode employed a lower tube voltage (90kV vs. 120kV) and a higher image quality level (65 vs. 34) to enhance coronary artery visualization. Patients underwent invasive coronary angiography as part of clinical routine. CCTA and QCA were reviewed to assess CAD presence defined as stenosis ≥ 50% in proximal and middle coronary segments. We included 60 patients (mean age 79 ± 7 years; 39(65%) men). Mean heart rate during scanning was 72 ± 13bpm. Median coronary calcium score was 973[379-2007]. QCA identified significant CAD in 24 patients (40%): 9 patients scanned with HR mode, 10 patients with the UHR mode, and 5 patients with the UHR adjusted mode. Per-patient area under the curves were 0.57 for HR, 0.80 for UHR, and 0.80 for adjusted UHR, with no significant differences between the scan modes, and per-vessel the area under the curves were 0.73 for HR, 0.69 for UHR, and 0.87 for adjusted UHR, with significant differences between UHR and adjusted UHR (p = 0.04). UHR and adjusted UHR modes of dual source PCD-CT show potential for improved sensitivity and negative predictive value for detecting CAD in patients undergoing pre-TAVI scans, however, no statistically significant difference from HR mode was observed.

Enhanced Disease Detection for Apple Leaves with Rotating Feature Extraction

Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards.

Use of ResNets for HLB Disease Detection on Orange Leaves Using Terrestrial Multispectral Images

Abstract. Huanglongbing (HLB) is a bacterial disease transmitted by different vectors of sap-sucking insects. It affects all crops of citrus trees, decreasing the values of those fruits in the market and eventually the decay of orchards. In Brazil, the world's leading orange producer, citriculture faces severe issues with HLB and substantial economic loss. Technical means of scanning the orchards with high-throughput becomes essential for the sustainability of this industry. In this study, we propose to investigate an operational strategy consisting of scanning large portions of foliage (the canopy of one tree or more) in which there can few early foliage symptoms. It is proposed to investigate deep learning tools to solve this complex binary classification problem. The study is based on a dataset comprising 1,297 terrestrial multispectral (14 channels) images captured at high spatial resolution in a commercial orange orchard in Brazil. It is proposed to adapt and retrain standard neural network architectures, namely ResNets18 and ResNets34, to process such images. Our analysis reveals promising results, with both models demonstrating convergence and achieving stable performance. Notably, ResNet18 outperformed ResNet34, achieving an accuracy of 76.45% compared to 66.79% from ResNet34. These findings suggest that deep neural network methods can effectively manage non-radiometrically calibrated data and accurately distinguish images with HLB symptoms from healthy plants . However, with reduced datasets and limited possibilities for transfer learning and fine-tuning, it seems that only reasonable sized networks can be trained. Thus, more advanced state-of-the-art tools of the are still challenging to deploy for agricultural multi- or hyperspectral data.

Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

Nanofluidic Lab-On-A-Chip Systems for Biosensing in Healthcare.

Biosensing plays a vital role in healthcare monitoring, disease detection, and treatment planning. In recent years, nanofluidic technology has been increasingly explored to be developed into lab-on-a-chip biosensing systems. Given now the possibility of fabricating geometrically defined nanometric channels that are commensurate with the size of many biomolecules, nanofluidic-based devices are likely to become a key technology for the analysis of various clinical biomarkers, including DNA (deoxyribonucleic acid) and proteins in liquid biopsies. This review summarizes the fundamentals and technological advances of nanofluidics from the purview of single-molecule analysis, detection of low-abundance molecules, and single-cell analysis at the subcellular level. The extreme confinement and dominant surface charge effects in nanochannels provide unique advantages to nanofluidic devices for the manipulation and transport of target biomarkers. When coupled to a microfluidic network to facilitate sample introduction, integrated micro-nanofluidic biosensing devices are proving to be more sensitive and specific in molecular analysis compared to conventional assays in many cases. Based on recent progress in nanofluidics and current clinical trends, the review concludes with a discussion of near-term challenges and future directions for the development of nanofluidic-based biosensing systems toward enabling a new wave of lab-on-a-chip technology for personalized and preventive medicine.

Potato disease identification using transfer learning approaches

Potato crop is one of the prominent consumed foods by human beings. When potato crops are infected by diseases it affects farmers negatively and to run in a loss. Therefore, early detection of the potato crop disease can play a vital role in minimizing the loss of the farmers. Nowadays, artificial intelligence technologies, more specifically deep learning techniques, provide solutions to many crops disease-related problems. However, training deep learning models requires a high computational power and huge amount of data as they are data hungry models. Also, designing a custom CNN models a difficult task and there are some variations to be considered. To avoid these difficulties, we adopted two pretrained models of DenseNet121 and VGG19 through transfer learning approaches. The achieved accuracy for DenseNet121 and VGG19 models are 82.6% and 98.56% respectively. DenseNet121 model obtained the average precision, sensitivity, and F1-score of 88.19%, 82.53, and 82.04%, respectively. Whereas VGG19 yields 98.39% of precision, 98.39% of sensitivity, and 97.26% of F1-score in ternary 3-class classification (early blight vs healthy vs late blight).

An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection

Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. The model employs ResNet34 as its backbone and introduces an efficient spatial attention mechanism (effective spatial attention, ESA) to focus on key regions of the images. By replacing the standard convolutions in ResNet34 with depthwise separable convolutions, the model reduces its parameter count by 85.37% and its computational load by 84.51%. Additionally, Dropout is used to mitigate overfitting, and data augmentation techniques such as center cropping and horizontal flipping are employed to enhance the model’s robustness. The experimental results show that the improved algorithm achieves an accuracy, precision, and F1 score of 87.09%, 87.14%, and 86.91%, respectively, outperforming several benchmark models (including AlexNet, VGG16, MobileNet, DenseNet, and various ResNet variants). These findings demonstrate that the proposed ESA-ResNet34 model significantly enhances crop pest and disease detection.

Precision Agriculture Technologies for Early Detection of Crop Pests and Diseases

Crop pests and diseases pose significant challenges to agricultural productivity and food security worldwide. Traditional methods for detecting and managing these threats often rely on manual scouting and blanket pesticide applications, which can be labor-intensive, time-consuming, and environmentally harmful. Precision agriculture technologies offer promising solutions for early detection and targeted management of crop pests and diseases. This review article provides a comprehensive overview of the latest precision agriculture tools and techniques for monitoring crop health, detecting pests and diseases, and guiding site-specific interventions. Key technologies discussed include remote sensing, proximal sensing, machine learning, robotics, and Internet of Things (IoT) sensors. The article highlights the potential of these technologies to improve the timeliness, accuracy, and efficiency of pest and disease detection while reducing reliance on chemical inputs. It also discusses the challenges and opportunities for integrating these technologies into current agricultural practices and extension services. The review concludes with recommendations for future research and development to advance precision agriculture solutions for sustainable crop protection.