Conventional Diagnostic Techniques Research Articles

Early Disease Detection Using AI: A Deep Learning Approach to Predicting Cancer and Neurological Disorders

Early diagnosis of life-threatening illnesses, like cancers and neurological disorders, is crucial for enhancing patient survival rates, minimizing treatment expenses, and the administration of early medical intervention. Yet, conventional diagnostic techniques are usually beset by issues that include high reliance on expert opinion, time consumption, and fluctuating accuracy—particularly during the initial phases of disease progression. The availability of huge medical datasets, coupled with the recent explosion in Artificial Intelligence (AI), specifically in the area of deep learning, has opened up a new avenue for strengthening early diagnostic powers. This study compares and contrasts the performance of five deep learning architectures—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Transformers, and a hybrid CNN-RNN model—against the early detection of cancer and neurological diseases. Public datasets comprising histopathological images, EEG signals, and MRI scans were utilized for training and testing the models. Essential preprocessing methods, including normalization, augmentation, and noise removal, were employed to enhance model performance with regard to both spatial and sequential data types. The performance of models was evaluated using performance measures such as accuracy, precision, recall, F1-score, and ROC-AUC. The results of experiments demonstrate that the Transformer and Hybrid CNN-RNN models surpassed the performance of other models on both disease classifications with detection accuracies of over 92%. The results point to the efficacy of multi-context learning methods, which have the capability of learning both spatial and temporal features in complicated biomedical data simultaneously. The research illustrates that deep learning can play a substantial role in enhancing early disease diagnosis by providing scalable, effective, and precise diagnostic solutions. Furthermore, it lays the foundation for the future incorporation of explainable artificial intelligence, multi-modal data fusion, and implementation of AI models in real-time clinical environments. Through connecting machine learning advances with practical healthcare applications, the research is building intelligent systems to assist clinicians in making life-critical and timely decisions

Integrating Spatial And Sequential Analysis For Lung Cancer Detection

Lung cancer is still one of the most common causes of death globally, and thus early diagnosis is critical to enhance survival. The conventional diagnostic procedures usually identify lung cancer at a late stage, making early detection important. This paper presents a deep learning-based model that combines MobileNet for efficient feature extraction and LSTM for analyzing sequential patterns to enhance early lung cancer detection. MobileNet effectively captures key medical image features, while LSTM identifies temporal dependencies, improving diagnostic accuracy. The model’s lightweight architecture allows for real-time deployment, making it particularly useful for mobile-based diagnostics and low-resource settings. With its high accuracy, this AI-powered solution holds great potential for clinical decision-making, early intervention, and cost-effective diagnosis.

Dermatological Disease Detection Using CNN

Skin diseases are mild infections to serious diseases such as melanoma, and it is necessary to diagnose them at an early stage for proper treatment. This paper proposes a Convolutional Neural Network (CNN)-based classifier to identify skin diseases from images automatically. The model was trained on a huge dermatological database with data augmentation, transfer learning, and hyperparameter optimization. Deep learning architectures like ResNet, EfficientNet, and DenseNet were explored to achieve high accuracy. Testing revealed the model was as accurate as 95%, outperforming conventional diagnostic techniques. The system helps dermatologists by enhancing diagnostic accuracy, minimizing manual labor, and facilitating quicker decision-making. Through the incorporation of artificial intelligence in dermatology, this study improves medical imaging, leading to improved patient outcomes and health efficiency.

PANCREAS CANCER DETECTION USING DEEP LEARNING

Pancreatic cancer maintains to pose a considerable health trouble owing to it’s behind schedule analysis and multiplied mortality rate. Conventional diagnostic procedures, predominantly reliant on imaging strategies, regularly inadequately identify early-stage cancer. A singular category technique employing deep learning is introduced to triumph over this constraint, concentrating on genetic information derived from blood and urine samples as opposed to imaging modalities which includes CT scans. The suggested method makes use of artificial Neural Networks (ANN) to have a look at molecular-stage facts, thereby improving diagnosis accuracy. The dataset includes genetic profiles obtained from patient samples, facilitating a strong type framework. The ANN architecture has an input layer that acquires genetic data, several hidden layers that analyze complicated patterns, and an output layer that affords categorization outcomes. This method exceeds conventional machine learning techniques, like support Vector Machines (SVM) and conventional neural networks, by improving accuracy and adaptability. The approach improves detection performance by utilizing advanced neural networks and incorporating constant values for optimization. The focus on genetic markers enables early and correct prognosis, main to tailored treatment techniques. This methodology represents a sizeable advancement in pancreatic most cancers detection, providing a more dependable and effective alternative to modern methods. “INDEX TERMS: Pancreatic Cancer Diagnostics, Machine Learning, Deep Learning, Cancer Detection, CT scan, ANN

Exploring the Potential of Optical Genome Mapping in the Diagnosis and Prognosis of Soft Tissue and Bone Tumors.

Sarcomas are rare malignant tumors of mesenchymal origin with a high misdiagnosis rate due to their heterogeneity and low incidence. Conventional diagnostic techniques, such as Fluorescence In Situ Hybridization (FISH) and Next-Generation Sequencing (NGS), have limitations in detecting structural variations (SVs), copy number variations (CNVs), and predicting clinical behavior. Optical genome mapping (OGM) provides high-resolution genome-wide analysis, improving sarcoma diagnosis and prognosis assessment. This study analyzed 53 sarcoma samples using OGM. Ultra-high molecular weight (UHMW) DNA was extracted from core and resection biopsies, and data acquisition was performed with the Bionano Saphyr platform. Bioinformatic pipelines identified structural variations, comparing them with known alterations for each sarcoma subtype. OGM successfully analyzed 62.3% of samples. Diagnostic-defining alterations were found in 95.2% of cases, refining diagnoses and revealing novel oncogenic and tumor suppressor gene alterations. The challenges included DNA extraction and quality issues from some tissue samples. Despite these limitations, OGM proved to be a powerful diagnostic and predictive tool for bone and soft tissue sarcomas, surpassing conventional methods in resolution and scope, enhancing the understanding of sarcoma genetics, and enabling better patient stratification and personalized therapies.

Identification of candidate biomarkers correlated with the pathogenesis of breast cancer patients

Breast cancer (BC) is the second leading cause of cancer-related death in females, followed by lung cancer. Disadvantages exist in conventional diagnostic techniques of BC, such as radiation risk. The present study integrated bioinformatics analysis with machine learning to elucidate potential key candidate genes associated with the tumorigenesis of BC. Eleven datasets were downloaded from the Gene Expression Omnibus (GEO) database and were consolidated into two independent cohorts (training cohort and validation cohort) after batch-effect removal. We employed “limma” package to screen differentially expressed genes (DEGs) between BC and adjacent normal breast samples. Subsequently, the most reliable diagnostic indicators were identified utilizing LASSO-Logistic regression, SVM-RFE and multivariate stepwise Logistic regression analysis. Logistic model and nomogram were created based on these hub genes and applied in external validation cohort to verify the robustness of the model. As a result, a total of six hub genes connected with BC pathogenesis were identified, including CD300LG, IGSF10, FAM83D, MAMDC2, COMP and SEMA3G. Then, a diagnostic model of BC on the basis of these genes was established. ROC analysis of the diagnostic model illustrated that AUC of the training cohort was 0.978 (0.962, 0.995). In the validation cohort, AUC of training set and validation set were 0.936 (0.910, 0.961) and 0.921 (0.870, 0.972), respectively. This indicated that the model was reliable in separating BC patients from healthy individuals. The model may assist in early diagnosis of BC with implications for improving the prognosis of BC patients.

Liquid Biopsies in the Early Diagnosis, Prognosis, and Tailored Treatment of Colorectal Cancer.

Liquid biopsies provide a less-invasive option to tissue biopsies for the early diagnosis, prognosis, and tailored therapy of colorectal cancer (CRC). CRC is a major cause of cancer-related death, and early identification is essential for improving patient outcomes. Conventional diagnostic techniques, including colonoscopy and tissue biopsy, may be enhanced by liquid biopsies that examine circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), and other indicators present in body fluids. These markers provide significant insights into tumor biology, heterogeneity, and therapeutic response. CTCs detected in early-stage CRC have prognostic significance for disease recurrence and survival, while ctDNA investigation may uncover genetic mutations, epigenetic alterations, and tumor development. The identification of ctDNA in minimal residual disease (MRD) postsurgery correlates with an elevated risk of recurrence and unfavorable prognosis, underscoring its use in assessing treatment effectiveness. Furthermore, non-coding RNAs (ncRNAs) contained inside EVs provide potential prospective biomarkers and therapeutic targets, facilitating diagnosis and treatment assessment. Notwithstanding the potential of liquid biopsies, obstacles persist in assay standardization, sensitivity enhancement, and the management of tumor heterogeneity. Additional extensive research is required to determine their function in clinical practice. Overall, liquid biopsies serve as a potential instrument for real-time monitoring, evaluating therapy responses, and directing individualized therapeutic strategies in CRC patients.

The Role of Artificial Intelligence in Early Detection of Cardiovascular Diseases

Introduction: Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality worldwide. Early diagnosis is crucial for better patient outcomes and effective care. Artificial intelligence (AI) has emerged as a viable method in cardiology that enhances risk assessment and diagnostic accuracy..Objectives: This study compares the accuracy, sensitivity, and specificity of AI-assisted diagnostics with conventional techniques in order to assess the usefulness of AI in the early diagnosis of CVDs.Materials and Methods: From January to June 2024, an observational study was carried out at NICVD Karachi Pakistan. 500 patients' ECG and echocardiogram data were examined using AI algorithms, and the results were compared to evaluations made by cardiologists.Results: AI performed better than conventional diagnostic techniques, showing increased sensitivity (94.2%) and specificity (91.5%). 20.8% of preclinical anomalies were successfully identified by AI, resulting in earlier actions..Conclusion: Early CVD detection is much improved by AI, which also increases diagnostic accuracy. For wider clinical adoption, data bias, ethical issues, and implementation hurdles must be addressed.

Serological markers for enhanced malaria surveillance from an elimination point of view: A narrative review

Malaria continues to pose a significant global health challenge despite a significant achievement in control and elimination in certain areas. Accurate and timely diagnosis is crucial for effective disease management and control, and finally leading to elimination. However, microscopy and rapid diagnostic tests (RDTs) have traditionally been the primary malaria diagnostic tools used globally, with certain shortcomings, including their limited sensitivity, specificity, and inability to identify asymptomatic infections. Serological markers have emerged as promising alternatives in malaria serosurveillance, particularly in countries where targets have already been set for elimination. This review highlights the advantages of serological markers over conventional diagnostic techniques and discusses some of the most promising serological markers against Plasmodium species-specific antigens. The implementation of serosurveillance, coupled with the utilization of these serological markers represents a transformative shift in malaria surveillance. By capitalizing on the immune memory of individuals, serosurveillance also enables the identification of recent and past infections. This approach is particularly valuable in low- transmission settings and for tracking changes in malaria prevalence over time. While recognizing the use of serological markers across various global contexts, this review predominantly emphasizes their significance within the framework of India.

Predicting autism spectrum disorder through machine learning

Since social interaction, speech, and behaviour are all impacted by autism spectrum disorder (ASD), early detection is essential for prompt intervention. Through the analysis of behavioural and demographic data, this study creates a machine learning-based model to predict ASD in children. The system effectively classifies ASD cases using the Random Forest and XGBoost algorithms, making it more accessible than conventional diagnostic techniques. Model training, feature selection, and data preprocessing are all part of the methodology, and accuracy, precision, and recall measures are used to assess performance. In order to improve early diagnosis and intervention for improved cognitive and social development, the model seeks to offer an objective, scalable, and user-friendly screening tool.

Artificial Intelligence With Neural Network Algorithms in Pediatric Astrocytoma Diagnosis: A Systematic Review

Background: Astrocytoma is a common pediatric brain tumor that poses a significant health burden. Recent advancements in artificial intelligence (AI), particularly neural network algorithms, have been studied for their precision and efficiency in medical diagnostics via effectively analyzing imaging data to identify patterns and anomalies.Objective: To systematically review AI-based diagnostic tools with neural network algorithms’ methodologies, sensitivities, specificities, and potential clinical integration for pediatric astrocytoma, providing a consolidated perspective on their overall performance and impact on clinical decision-making.Methods: As per PRISMA 2020 guidelines, we conducted a comprehensive search in PubMed, Scopus, and ScienceDirect on February 5, 2024. The search strategy was guided by a PECO question focusing on pediatric astrocytoma diagnosis using AI algorithms vs computed tomography or magnetic resonance imaging (MRI). Keywords were terms related to AI and neural network algorithms. We included studies analyzing the diagnostic accuracy of AI-based methods in cases of pediatric astrocytoma (World Health Organization grades 1-3), with no restrictions on a publication year or country. We excluded papers written in languages other than English or Bahasa Indonesia and nonhuman studies. Data was assessed using the Effective Public Health Practice Project tool.Results: Of 454 articles screened, 6 met inclusion criteria. These studies varied in design, location, and sample size, ranging from 10 to 135 subjects. The AI methods showed high sensitivity and specificity, often surpassing traditional radiological techniques. Notably, neural network algorithms using 3-dimensional MRI demonstrated improved accuracy compared with 2-dimensional MRI (96% vs 77%). The AI models exhibited performance levels comparable to or exceeding that of expert radiologists, with metrics such as tumor classification accuracy of 92% and high values of the area under the receiver operating characteristic curve.Conclusions: AI with neural network algorithms shows significant promise in enhancing accuracy of pediatric astrocytoma diagnosis. The studies reviewed indicate that these advanced methods can achieve superior sensitivity and specificity compared with conventional diagnostic techniques. Integrating AI into clinical practice could substantially improve diagnostic precision and patient outcomes.

Integrating NLP and LLMs to discover biomarkers and mechanisms in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurological condition characterized by cognitive decline, memory loss, and aberrant behaviour. It affects millions of people globally and is one of the main causes of dementia. The neurodegenerative condition known as AD has intricate, multifaceted mechanisms that make it difficult to comprehend and identify in its early stages. Conventional diagnostic techniques frequently fail to detect the disease in its early stages. By combining Natural Language Processing (NLP) and Large Language Models (LLMs), this research suggests a novel approach for identifying potential biomarkers and underlying mechanisms of AD. Clinical data is gathered from publicly accessible databases and healthcare facilities, including genetic information, neuroimaging scans, and medical records. The pre-processing of unstructured clinical notes involves tokenization and genetic profiles and neuroimaging data are normalized by Z-score normalization for consistency. Multi-Input Convolutional Neural Networks (MI-CNN) are employed to efficiently fuse diverse data sources, allowing for a thorough analysis. Key biomarkers linked to AD are identified and categorized using the Genetic Algorithm combined with Bidirectional Encoder Representations from Transformers (BERT) (GenBERT). By fine-tuning BERT's hyperparameters using genetic optimization approaches, GenBERT enables the effective analysis of large medical datasets, such as patient histories, genetic data, and clinical notes. The combination strategy increases feature selection and the model's capacity to identify minute genomic and linguistic patterns suggestive of AD. The goal of this integrated strategy is to provide early diagnostic tools and new insights into the pathogenesis of the disease, which could transform methods for detecting and treating AD. As it concerns early AD prediction, the GenBERT model performs better than current techniques, obtaining the highest accuracy (98.30%) and F1-score (0.97), as well as greater precision (0.95) and recall (0.92). Additionally, it demonstrates its capacity to reliably identify both positive and negative AD cases with sensitivity (98.65%) and specificity (99.73%). Overall, GenBERT offers a trustworthy and useful tool for AD early diagnosis.

The Role of Quantitative Real-Time PCR in the Invasive Pulmonary Aspergillosis Diagnosis: A Retrospective Study.

Invasive pulmonary aspergillosis (IPA) reports significant mortality rates among critically ill patients. A prompt microbiological diagnosis is essential to establish a coherent antifungal treatment. Despite its low sensitivity and prolonged turn-around time, culture represents the conventional diagnostic technique. Additionally, galactomannan detection may support the diagnostic process. Ultimate generation methods, such as the real-time polymerase chain reaction (Real-Time PCR), integrated the diagnostic procedure to improve the overall laboratory effectiveness, especially regarding a quantitative Aspergillus spp. DNA detection. Herein, we propose a retrospective analysis where a quantitative real-time PCR was performed on respiratory samples belonging to patients with or without probable pulmonary aspergillosis. The study enrolled 62 samples, whose PCR results were compared to culture and galactomannan indexes. Additionally, clinical and general data were collected for all the patients. The qPCR assay reported 100% sensitivity and negative predictive value, while specificity reached 59.2% and the positive predictive value was 76.1%. Moreover, IPA patients reported fungal DNA loads higher than 103 in a logarithmic scale, while non-aspergillosis episodes reported a maximum level of 103. We hypothesized a future possibility to define a specific cut-off in distinguishing colonization from infection cases, requiring further investigations and speculations about IPA patients and respiratory samples.

Dual-Mode Immunosensor for Antibody Detection: Harnessing the Versatility of Antibody-Based Nanozymes across Optical and Electrochemical Platforms.

In the early years of the 21st century, numerous viral infectious diseases have proliferated, prompting intensified efforts to devise more effective diagnostic methods. In response, various biosensors have emerged with the aim of overcoming the constraints of conventional diagnostic techniques. Nanomaterial-based biosensors have revolutionized conventional approaches, significantly enhancing biosensor performance and effectively tackling these challenges. A diverse array of nanoparticles and nanomaterials has been systematically synthesized, engineered, and employed to augment the functionalities of biosensors. This work capitalizes on the properties of gold-platinum bimetallic nanoclusters (NCs) embedded in the structure of an immunoglobulin (IgG) (Au/Pt NCs-IgG), unveiling a novel double strategy for the detection of antibodies that leverages both their catalytic NC scaffold and the biorecognition element. The detection mechanism revealed the unique oxidase-like properties of Au/Pt NCs-IgG. This distinctive property, in addition to previously reported peroxidase-like activity, positions Au/Pt NCs-IgG as an effective probe in both optical and electrochemical sandwich enzyme-linked immunosorbent assays, facilitating their incorporation in different sensor frameworks and their utilization across various applications. As a study case, anti-SARS-CoV-2 antibodies (anti-RBD IgG antibodies) were employed as the target analyte. A linear detection range was found between 0.5 and 100 ng/mL for optical immunosensors and 50-300 ng/mL for electrochemical immunosensors. The validation of the immunosensor in clinical samples demonstrated its promising diagnostic value. The significantly differential signal obtained between positive and negative clinical samples underscores the suitability of both sensors for point-of-care diagnostic applications.

AI-augmented Biophysical modeling in thermoplasmonics for real-time monitoring and diagnosis of human tissue infections.

AI-augmented Biophysical modeling in thermoplasmonics for real-time monitoring and diagnosis of human tissue infections.

Research on failure diagnosis analysis of plunger gas lift system using convolutional neural network with multi-scale channel attention mechanism based on wavelet transform

Research on failure diagnosis analysis of plunger gas lift system using convolutional neural network with multi-scale channel attention mechanism based on wavelet transform

Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals.

Congenital heart disease (CHD), impacting around 1% of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60-650Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56%, precision of 98.56%, F1 score of 98.55%, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment.

Deep Learning and Hyperspectral Imaging for Liver Cancer Staging and Cirrhosis Differentiation.

Liver malignancies, particularly hepatocellular carcinoma (HCC), pose a formidable global health challenge. Conventional diagnostic techniques frequently fall short in precision, especially at advanced HCC stages. In response, we have developed a novel diagnostic strategy that integrates hyperspectral imaging with deep learning. This innovative approach captures detailed spectral data from tissue samples, pinpointing subtle cellular differences that elude traditional methods. A sophisticated deep convolutional neural network processes this data, effectively distinguishing high-grade liver cancer from cirrhosis with an accuracy of 89.45%, a sensitivity of 90.29%, and a specificity of 88.64%. For HCC differentiation specifically, it achieves an impressive accuracy of 93.73%, sensitivity of 92.53%, and specificity of 90.07%. Our results underscore the potential of this technique as a precise, rapid, and non-invasive diagnostic tool that surpasses existing clinical methods in staging liver cancer and differentiating cirrhosis.

Distinct relative abundances in pathogens detected in mechanically ventilated patients with suspected pneumonia in the intensive care unit at King Abdulaziz University Hospital

In this study, we present for the first time the landscape of the lung microbiota in patients with ventilator-associated pneumonia in Intensive Care Units in Saudi Arabia. DNA from 83 deep endotracheal aspirate lung samples was subjected to PacBio sequencing to identify pathogens in comparison with conventional diagnostic techniques. Patients on ventilation with pneumonia presented with similar lung flora to those of patients on ventilation without pneumonia. Proteobacteria, Firmicutes, and Bacteroidetes were detected in the majority of the samples. Samples treated with different antibiotics exhibited similar abundances of phyla and families. In order, the ten most common species detected in 16 clusters were Klebsiella pneumoniae, Stenotrophomonas maltophilia, Haemophilus influenzae, Pseudomonas aeruginosa, Metamycoplasma salivarium, Elizabethkingia anophilis, Staphylococcus aureus, Acinetobacter baumannii, Prevotella oris and Klebsiella africana. Of 51 on ventilation with pneumonia, the pathogens identified through sequencing corresponded with the findings from culture-dependent tests in 26 patients (50.98%), whereas the results differed in 30 patients (58.82%). Of 32 patients on ventilation without pneumonia, the pathogens identified through sequencing matched the conventional diagnostics results in only two patients (6.25%) but differed in 25 patients (78.13%). In summary, patients on mechanical ventilation with pneumonia did not display notable phenotypic traits. K. pneumoniae and S. maltophilia were the most common taxa detected in the samples, although some variations in microbial composition were observed. We conclude that Intensive Care Units exhibit distinct patterns of microbial colonization.

AI-based monkeypox detection model using Raspberry Pi 5 AI Kit

Monkeypox is a zoonotic disease that originated from monkeys and then spread to humans; this disease recently popped up globally with increased risks of spreading from human to human and clinical presentation similar to other pox-like diseases. Quick and right identification is fundamental for containment and treatment that will minimize the spread of the disease. The current conventional diagnostic techniques include PCR which takes time, and money, and often needs sophisticated laboratories that cannot be easily accessed in developing countries. This work describes the creation and application of a monkeypox detection algorithm orchestrated on the Raspberry Pi 5 AI Kit. Developed based on convolutional neural networks (CNNs), the model enables one to distinguish actual monkeypox lesions in the images. The Raspberry Pi 5 AI Kit allows for edge computing solutions to be implemented, making the entire solution mobile, affordable, and perfect for locations with low connectivity. Extensive data collection and data preprocessing were performed, and the final dataset with monkeypox and skin lesion images consisted of more than 5000 verified images. 94% accuracy was obtained by the model, making it superior to the model available in literature. The implementation proves that powerful AI technologies can be applied to low-cost hardware to become a valuable weapon in the monkeypox frontline workers’ arsenal and advance the efforts against monkeypox infections.