Approach For Diagnosis Research Articles

Machine Learning Approach for Early Diagnosis of Dyslexia Among Primary School Children: A Scoping Review and Model Development

Dyslexia, a prevalent learning disorder among primary school children, often goes undetected until later stages, hindering academic progress and socio-emotional development. Early diagnosis is crucial for effective intervention. Machine Learning (ML) offers promise in developing accurate diagnostic tools. However, there's a scarcity of comprehensive reviews focusing on ML approaches for dyslexia diagnosis in this demographic. In this scoping review, we consolidate existing literature and present the development of a novel ML model that was customized for early dyslexia diagnosis. Utilizing Decision Tree, K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest. The comparative analysis of ML methods for dyslexia detection in elementary school children reveals distinct strengths. Decision Tree shows robust precision: 92.31% for dyslexia-prone, 90.62% for diagnosed dyslexia, and 86.67% for no dyslexia detected, with corresponding high recall values of 90.57%, 87.88%, and 100%, respectively. KNN excels with an overall accuracy of 94.00% and perfect precision for undetected dyslexia (100%), with high precision and recall for dyslexia-prone and diagnosed dyslexia. Logistic Regression highlights significant predictors and achieves precision of 95.38% for dyslexia-prone and 88.24% for diagnosed dyslexia, with recall rates of 93.34% and 90.91%, respectively. Naive Bayes exhibits outstanding precision for no dyslexia and dyslexia-prone categories (100%), with slightly lower precision for diagnosed dyslexia (82.5%), but perfect recall for undetected and diagnosed dyslexia. Random Forest demonstrates balanced performance with precision ranging from 91.18% to 94.23% and recall from 92.31% to 93.94%, achieving an overall accuracy of 93.00%. These results underscore ML's potential in enabling early dyslexia detection, facilitating timely interventions to improve outcomes for affected children and advancing dyslexia diagnosis.

Understanding Galectin-3's Role in Diastolic Dysfunction: A Contemporary Perspective.

Diastolic dysfunction, a prevalent condition characterized by impaired relaxation and filling of the left ventricle, significantly contributes to heart failure with preserved ejection fraction (HFpEF). Galectin-3, a β-galactoside-binding lectin, has garnered attention as a potential biomarker and mediator of fibrosis and inflammation in cardiovascular diseases. This comprehensive review investigates the impact of galectin-3 on diastolic dysfunction. We explore its molecular mechanisms, including its involvement in cellular signaling pathways and interaction with components of the extracellular matrix. Evidence from both animal models and clinical studies elucidates galectin-3's role in cardiac remodeling, inflammation, and fibrosis, shedding light on the underlying pathophysiology of diastolic dysfunction. Additionally, we examine the diagnostic and therapeutic implications of galectin-3 in diastolic dysfunction, emphasizing its potential as both a biomarker and a therapeutic target. This review underscores the significance of comprehending galectin-3's role in diastolic dysfunction and its promise in enhancing diagnosis and treatment approaches for HFpEF patients.

Circulating tumor DNA mutation analysis: advances in its application for early diagnosis of hepatocellular carcinoma and therapeutic efficacy monitoring.

In recent years, the detection and analysis of circulating tumor DNA (ctDNA) have emerged as a new focus in the field of cancer research, particularly in the early diagnosis of hepatocellular carcinoma (HCC) and monitoring of therapeutic efficacy. ctDNA, which refers to cell-free DNA fragments released into the bloodstream from tumor cells upon cell death or shedding, carries tumor-specific genetic and epigenetic alterations, thereby providing a non-invasive approach for cancer diagnosis and prognosis. The concentration of ctDNA in the blood is higher compared to that in healthy individuals or other liquid biopsies from early-stage cancers, which is closely associated with the early diagnosis and comprehensive sequencing studies of HCC. Recent studies have indicated that sequential ctDNA analysis in patients receiving primary or adjuvant therapy for HCC can detect treatment resistance and recurrence before visible morphological changes in the tumor, making it a valuable basis for rapid adjustment of treatment strategies. However, this technology is continuously being optimized and improved. Challenges such as enhancing the accuracy of ctDNA sequencing tests, reducing the burden of high-throughput sequencing on a large number of samples, and controlling variables in the assessment of the relationship between ctDNA concentration and tumor burden, need to be addressed. Overall, despite the existing challenges, the examination and analysis of ctDNA have opened up new avenues for early diagnosis and therapeutic efficacy monitoring in hepatocellular carcinoma, expanding the horizons of this field.

Long non-coding RNA SNHG7 serves as a diagnostic biomarker for acute coronary syndrome and its predictive value for the clinical outcome after percutaneous coronary intervention

BackgroundAcute coronary syndrome (ACS) is one of the common causes of cardiovascular death. The related lncRNAs were novel approaches for early diagnosis and intervention. This paper focused on the clinical function of SNHG7 for patients after PCI.MethodsThe expression of SNHG7 was assessed in ACS patients. The predictive roles of SNHG7 were unveiled by the ROC curve. The relationship between SNHG7 and Gensini scores was judged by Pearson analysis. One-year follow-up was conducted and all patients were catalogued into different groups based on the prognosis. The qRT-PCR, K-M curve, and Cox regression analysis were performed to document the prognostic significance of SNHG7.ResultsSNHG7 was highly expressed in ACS and its three subtypes. SNHG7 showed a certain value in predicting ACS, UA, NSTEMI, and STEMI. Gensini is a closely correlated indicator of SNHG7. The declined expression of SNHG7 was observed in the non-MACE and survival groups. The risk of MACE and death was increased in the group with high expression of SNHG7. SNHG7 was an independent biomarker in patients with ACS after PCI.ConclusionsSNHG7 might be a diagnostic and prognostic tool for ACS patients.

A novel quantitative double antigen sandwich ELISA for detecting total antibodies against Candida albicans enolase 1.

This study aimed to develop a double antigen sandwich ELISA (DAgS-ELISA) method for more efficient, accurate, and quantitative detection of total antibodies against Candida albicans enolase1 (CaEno1) for diagnosing invasive candidiasis (IC). DAgS-ELISA was developed using recombinant CaEno1 and a monoclonal antibody as the standard. Performance evaluation included limit of detection, accuracy, and repeatability. Dynamic changes in antibody levels against CaEno1 in serum from systemic candidiasis mice were analyzed using DAgS-ELISA. Patient serum samples from IC, Candida colonization, bacterial infections, and healthy controls were analyzed with DAgS-ELISA and indirect ELISA. DAgS-ELISA outperformed indirect ELISA in terms of linear range and test background. In systemic candidiasis mice, a distinctive 'double-peak' pattern in dynamic antibody levels was observed. Additionally, there was a high level of consistency in the positive rates of CaEno1 antibodies detected by both DAgS-ELISA and indirect ELISA. While the positivity rates differed among patient groups, no significant variations in antibody levels were detected among the various positive patient groups. DAgS-ELISA offers a reliable novel approach for IC diagnosis, enabling rapid, accurate, and quantitative detection of CaEno1 antibodies. Further validation and optimization are needed for its clinical application and effectiveness.

A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.

A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.

Machine Learning Approaches for Skin Neoplasm Diagnosis.

Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.

Angiomyomatous hamartoma: Unusual presentation of a rare entity

Angiomyomatous hamartoma (AMH) is a rare benign vascular growth primarily affecting inguinal and femoral lymph nodes (LNs). Here, we present a unique case of AMH manifesting as a submental neck mass, a location seldom reported in literature. A 20-year-old male presented with a palpable midline neck mass adjacent to the hyoid bone. Ultrasonography suggested a partially cystic lesion, prompting consideration of thyroglossal duct cyst or necrotic lymph node. Fine-needle aspiration (FNA) hinted at a benign cystic lesion, potentially a thyroglossal duct cyst. Surgical excision via the Sistrunk approach revealed no cyst but characteristic features of AMH upon histopathological examination. This case underscores the importance of considering AMH in the differential diagnosis of subcutaneous nodules in unusual locations and highlights the role of surgical excision for both diagnosis and treatment. Our findings expand the understanding of AMH's clinical presentation and emphasize the necessity of a comprehensive differential diagnosis approach for nodal lesions.

Exploring the complexity of EEG patterns in Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20Hz) and beta 2 (20-30Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.

A rare case of giant peritoneal loose body: A case report

Peritoneal loose bodies (PLBs) are uncommon abdominal lesions, typically asymptomatic and discovered incidentally during surgery. Giant PLBs, exceeding 5 cm in size, are even rarer and can present with clinical symptoms mimicking other abdominal pathologies. We present a 57-year-old male with a giant PLB presenting as intermittent abdominal pain, distention, and rectal bleeding. Imaging studies suggested colo-colic intussusception due to a colonic lipoma. The patient underwent an exploratory laparotomy revealing a segment of the descending colon containing multiple lipomas, one causing intussusception. In addition, a separate giant PLB 6 × 5.5 cm with central calcification was identified. A segmental colectomy with primary anastomosis and resection of the PLB was performed. The patient recovered well and reported complete resolution of symptoms at follow-up. Pathology confirmed a benign lipoma and a giant PLB. This case highlights the potential for giant PLBs to cause clinical symptoms and emphasizes the importance of including them in the differential diagnosis of abdominal complaints, even in the absence of a classic presentation. Exploratory laparotomy remains a definitive approach for diagnosis and management in such cases.

A Comparative Study on Data Balancing Methods for Alzheimer's Disease Classification

Alzheimer's disease is a prevalent neurological disorder affecting millions of people worldwide, often associated with the aging process, leading to the death of nerve cells in the brain and loss of connections. Recently, promising results have been demonstrated in diagnosing Alzheimer's disease using deep learning models, and various approaches for early diagnosis have been proposed. However, the imbalance in health datasets, particularly those containing rare cases, can lead to performance losses and misleading results during model training. This study focuses on these imbalance issues, evaluating the effectiveness of different balancing methods using the Alzheimer's MRI dataset. In this context, the performance of SMOTE, ADASYN, and Weight Balancing methods is compared using a custom model. Experimental results indicate that, compared to the original imbalanced dataset, Weight balancing outperforms in terms of accuracy, precision, recall, and F1 score. While SMOTE and ADASYN show improvement in various metrics, they are considered inferior to the Weight Balancing method. This study contributes to selecting data-balancing methods to enhance the accuracy of deep learning models in Alzheimer's disease classification and emphasizes the importance of addressing class imbalances in health datasets.

Architectural design of national evidence based medicine information system based on electronic health record.

The global implementation of Electronic Health Records has significantly enhanced the quality of medical care and the overall delivery of public health services. The incorporation of Evidence-Based Medicine offers numerous benefits and enhances the efficacy of decision-making in areas such as prevention, prognosis, diagnosis, and therapeutic approaches. The objective of this paper is to propose an architectural design of an Evidence-Based Medicine information system based on the Electronic Health Record, taking into account the existing and future level of interoperability of health information systems in Greece. A study of the suggested evidence-based medicine architectures found in the existing literature was conducted. Moreover, the interoperability architecture of health information systems in Greece was analyzed. The architecture design reviewed by specialized personnel and their recommendations were incorporated into the final design of the proposed architecture. The proposed integrated architecture of an Evidence-Based Medicine system based on the Electronic Health Record integrates and utilizes citizens' health data while leveraging the existing knowledge available in the literature. Taking into consideration the recently established National Interoperability Framework, which aligns with the European Interoperability Framework, the proposed realistic architectural approach contributes to improving the quality of healthcare provided through the ability to make safe, timely and accurate decisions by physicians.

Establishment of a multi-line immunochromatography based on magnetic nanoparticles for simultaneous screening of multiple biomarkers.

Biomarkers screening is a benefit approach for early diagnosis of major diseases. In this study, magnetic nanoparticles (MNPs) have been utilized as labels to establish a multi-line immunochromatography (MNP-MLIC) for simultaneous detection of carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA 19-9), and alpha-fetoprotein (AFP) in a single serum sample. Under the optimal parameters, the three biomarkers can be rapidly and simultaneously qualitative screening within 15 min by naked eye. As for quantitative detection, the MNP-MLIC test strips were precisely positioned and captured by a smartphone, and signals on the test and control lines were extracted by ImageJ software. The signal ratio of test and control lines has been calculated and used to plot quantitative standard curves with the logarithmic concentration, of which the correlation coefficients are more than 0.99, and the limit of detection for CEA, CA 19-9, and AFP were 0.60 ng/mL, 1.21 U/mL, and 0.93 ng/mL, respectively. The recoveries of blank serum were 75.0 ~ 112.5% with the relative standard deviation ranging from 2.5 to 15.3%, and the specificity investigation demonstrated that the MNP-MLIC is highly specific to the three biomarkers. In conclusion, the developed MNP-MLIC offers a rapid, simple, accurate, and highly specific method for simultaneously detecting multiple biomarkers in serum samples, which provides an efficient and accurate approach for the early diagnosis of diseases.

Comprehensive considerations for dermatologists: the application of FDG-PET in evaluating cutaneous lesions in pediatric Langerhans cell histiocytosis.

Langerhans cell histiocytosis (LCH) is a complex disorder characterized by the clonal proliferation of Langerhans cells, primarily affecting children and adolescents. This condition exhibits a wide spectrum of clinical presentations, necessitating a multidisciplinary approach for diagnosis, treatment, and follow-up. Cutaneous manifestations of LCH are significant, mimicking common dermatoses and posing diagnostic challenges. [18F]Fluorodeoxyglucose-Positron Emission Tomography (FDG-PET) has emerged as an important tool in the evaluation of pediatric LCH, offering insights into disease activity, extent, and therapeutic response. Moreover, FDG-PET provides a non-invasive means to distinguish between active LCH skin lesions and other dermatological conditions with similar clinical appearances, enhancing diagnostic accuracy and aiding in disease monitoring. This educational review summarizes the utility of nuclear imaging techniques, with a focus on PET scans, in the diagnosis and management of cutaneous pediatric LCH. A comprehensive literature search identified seven relevant articles, including retrospective studies and case reports. These studies highlight the efficacy of FDG-PET in localizing active LCH skin lesions, monitoring disease activity, and guiding treatment decisions. FDG-PET represents a valuable imaging modality for dermatologists, oncologists, and pediatricians managing pediatric LCH patients with cutaneous involvement. This non-invasive technique contributes to improved diagnostic accuracy and facilitates early intervention, ultimately enhancing patient care and outcomes.

Online meta-learning approach for sensor fault diagnosis using limited data

The accurate and timely diagnosis of sensor faults plays a critical role in ensuring the reliability and performance of structural health monitoring (SHM) systems. However, the challenge is detecting, locating, and estimating sensor faults in an online manner using limited training data. To resolve this problem, a novel approach for online sensor fault diagnosis is proposed for SHM. The proposed approach is based on meta-learning, which enables superior model generalization capabilities using limited data. The detection, localization, and estimation of typical sensor faults in an online manner can be achieved efficiently by the proposed approach. First, a one-dimensional convolutional neural network (1D CNN) is designed to detect and locate faulty sensors. The initial model parameters of the 1D CNN are optimized using a model-agnostic meta-learning training strategy. This strategy allows the acquisition of transferable prior knowledge, which can speed up the learning process on new sensor fault detection and localization tasks. The meta-learning strategy also enables efficient and accurate detection and localization of potential faulty sensors with limited data. After detecting and locating the faulty sensors, an online updating algorithm based on a dual Kalman filter is used to estimate the severity of sensor faults and structural states simultaneously. The proposed approach is demonstrated with simulated sensor faults that cover a numerical example and field measurements from the Canton Tower. The results show that the proposed approach is applicable for online sensor fault diagnosis in SHM.

A Semi-Supervised Adaptive Matrix Machine Approach for Fault Diagnosis in Railway Switch Machine.

The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model's optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.

Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion

Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both the instantaneous and long-term features of the sensor signals for the coupling signals of hydraulic system sensors. Lastly, to address the challenge of feature fusion with multisample rate sensor data, where direct merging of features through maximum or average pooling might dilute crucial information, a feature fusion and decoupling method based on a probabilistic sparse self-attention mechanism is designed, avoiding the issue of long-tail distribution in multisample rate sensor data. Experimental validation showed that the proposed model can effectively utilize samples to achieve accurate fault decoupling and classification for different components, achieving a diagnostic accuracy exceeding 97% and demonstrating robust performance in hydraulic system fault diagnosis under noise conditions.

Programmable Lanthanide Metal-Organic Framework for Ultra-Efficient Nucleic Acids Extraction and Interaction Analysis.

Efficient, mild, and reversible adsorption of nucleic acids onto nanomaterials represents a promising analytical approach for medical diagnosis. However, there is a scarcity of efficient and reversible nucleic acid adsorption nanomaterials. Additionally, the lack of comprehension of the molecular mechanisms governing their interactions poses significant challenges. These issues hinder the rational design and analytical applications of the nanomaterials. Herein, we propose an ultra-efficient nucleic acid affinity nanomaterial based on programmable lanthanide metal-organic frameworks (Ln-MOFs). Through experiments and density functional theory calculations, a rational design guideline for nucleic acid affinity of Ln-MOF was proposed, and a modular and flexible preparation scheme was provided. Then, Er-TPA (terephthalic acid) MOF emerged as the optimal candidate due to its pore size-independent adsorption and desorption capabilities for nucleic acids, enabling ultra-efficient adsorption (about 150% mass ratio) within 1 min. Furthermore, we elucidate the molecular-level mechanisms underlying the Ln-MOF adsorption of single- and double-stranded DNA and G4 structures. The affinity nanomaterial based on Ln-MOF exhibits robust nucleic acid extraction capability (4-fold higher than commercial reagent kits) and enables mild and reversible CRISPR/Cas9 functional regulation. This method holds significant promise for broad application in DNA/RNA liquid biopsy and gene editing, facilitating breakthroughs in analytical chemistry, pharmacy, and medical research.

Molecular Characterization of Lineage-IV Peste Des Petits Ruminants Virus and the Development of In-House Indirect Enzyme-Linked Immunosorbent Assay (IELISA) for its Rapid Detection”

Peste des petits ruminants (PPRV), a highly contagious viral disease, causes significant economic losses concerning sheep and goats. Recently, PPR viruses (PPRVs), have adopted new hosts and lineage IV of PPRVs represents genetic diversity within the same lineage. 350 samples, including blood, swabs, and tissues from sheep/goats, were collected during the 2020–2021 disease outbreaks in Pakistan. These samples were analysed through RT-PCR and three isolates of PPRV with accession numbers, MW600920, MW600921, and MW600922, were submitted to GenBank, based on the partial N-gene sequencing. This analysis provides a better understanding of genetic characterizations and a targeted RT-PCR approach for rapid PPRV diagnosis. An IELISA test was developed using the semi-purified antigen MW600922 isolate grown in Vero cells. The PPRV isolates currently present high divergence with the Turkish strain; conversely, similarities equivalent to 99.73% were observed for isolates collected from Pakistan. The developed indirect ELISA (IELISA) test demonstrated antibody detection rates at dilutions of 1:200 for antibodies (serum) and 1:32 for antigens. In comparison to cELISA, high specificity (85.23%) and sensitivity (90.60%) rates were observed. In contrast to the virus neutralization test (VNT), IELISA was observed to be 100% specific and 82.14% sensitive in its results. Based on these results, serological surveys conducted for PPR antibodies using IELISA can be a more effective strategy on a larger scale. Furthermore, our results demonstrate a significant breakthrough in the research in terms of cost-effectiveness and storage efficiency, and the developed IELISA test is highly recommended for use in developing countries.