Applications of Machine Learning in Viral Disease Diagnosis

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

Data Mining Techniques for Extraction and Analysis of Covid-19 Data

Artificial intelligence has played a crucial role in medical disease diagnosis. In this research, data mining techniques that included deep learning with different scenarios are presented for extraction and analysis of covid-19 data. The energy of the features is implemented and calculated from the CT scan images. A modified meta-heuristic algorithm is introduced and then used in the suggested way to determine the best and most useful features, which are based on how ants behave. Different patients with different problems are investigated and analyzed. Also, the results are compared with other studies. The results of the proposed method show that the proposed method has higher accuracy than other methods. It is concluded from the results that the most crucial features can be concentrated on during feature selection, which lowers the error rate when separating sick from healthy individuals. © 2022 IEEE.

Investigating Viral Involvement in Immunocompromised Patients Using Comprehensive Infectious Disease Testing Including FilmArray Respiratory Panel 2.1 on Bronchoscopy: A Retrospective Study.

Introduction Reports are rare on the usefulness of the FilmArray Respiratory Panel 2.1 (FARP) using lower respiratory tract specimens. This retrospective study assessed its use, as part of a comprehensive infectious disease panel, to detect the viral causes of pneumonia using bronchoalveolar lavage samples from immunosuppressed patients. Methods This study included immunocompromised patients who underwent bronchoalveolar lavage or bronchial washing by bronchoscopy between April 1, 2021, and April 30, 2022. The collected samples were submitted for comprehensive testing, including FARP test; reverse transcription polymerase chain reaction (RT-PCR) for cytomegalovirus, varicella-zoster virus DNA, and herpes simplex virus; PCR for Pneumocystis jirovecii DNA; antigen testing for Aspergillus and Cryptococcus neoformans; and loop-mediated isothermal amplification method for Legionella. Results Out of 23 patients, 16 (70%) showed bilateral infiltrative shadows on computed tomography and three (13%) were intubated. The most common causes of immunosuppression were anticancer drug use (n=12, 52%) and hematologic tumors (n=11, 48%). Only two (9%) patients tested positive for severe acute respiratory syndrome coronavirus 2 and adenovirus by FARP. Four patients (17%) tested positive for cytomegalovirus by RT-PCR, but no inclusion bodies were identified cytologically. Nine (39%) patients tested positive for Pneumocystis jirovecii by PCR, but cytology confirmed the organism in only one case. Conclusions Comprehensive infectious disease testing, performed using bronchoalveolar lavage samples collected from lung lesions in immunosuppressed patients, showed low positive detection by FARP. The viruses currently detectable by FARP may be less involved in viral pneumonia diagnosed in immunocompromised patients.

Deep learning based detection of monkeypox virus using skin lesion images.

As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model.

A Shuffling Building Block and Augmentation Parameter Tuning Techniques to Handle Small Medical Dataset

The recent COVID-19 outbreak showed us the importance of faster disease diagnosis using medical image processing as it is considered the most reliable and accurate diagnostic tool. In a CNN architecture, performance improves with the increasing number of trainable parameters at the cost of processing time. We have proposed an innovative approach of combining efficient novel architectures like Inception, ResNet, and ResNet-Xt and created a new CNN architecture that benefits Extreme Cardinal dimensions. We have also created four variations of the same base architecture by varying the position of each building block and used X-Ray, Microscopic, MRI, and pathMNIST datasets to train our architecture. For learning curve optimization, we have applied learning rate changing techniques, tuned image augmentation parameters, and chose the best random states value. For a specific dataset, we reduced the validation loss from 0.22 to 0.18 by interchanging the architecture's building block position. Our results indicate that image augmentation parameters can help to decrease the validation loss. We have also shown rearrangement of the building blocks reduces the number of parameters, in our case, from 5,689,008 to 3,876,528. © 2023 IEEE.

Salivary Analysis for Evidence based Ayurvedic Diagnosis

Ayurveda is called Mother of all medical sciences. It's the oldest therapeutic and medicinal treatment invented in ancient India. Ayurveda or Ayurvedic treatment is bit different from modern medical science. It believes in Nadi Pariksha and many subjective parameters are included to start diagnosis of disease. Whereas modern medical science has different approach of disease diagnosis. It utilizes different tools and testing to diagnose a disease effectively. Saliva analysis is already accepted in modern medical as an important bio-substance, as we see in COVID-19, but not in ayurveda. This paper shows how salivary analysis can act as an evidential proof for diagnosing a disease, in the ayurvedic way. The salivary contents can be analyzed use various biosensors. One of these is Surface Enhanced Raman Spectroscopy (SERS) platform. It allows molecular detection in bio fluids like saliva, sweat, urine, etc. The saliva analysis using SERS technique will help to detect various trace level molecules which is likely to assist the Ayurvedic diagnosis more accurately and dependency on subjective parameters will reduce to evaluate patient's condition. © 2023 IEEE.

Analytical and biomedical applications of microfluidics in traditional Chinese medicine research

Traditional Chinese medicine (TCM) has significant benefits in the prevention and treatment of diseases due to its unique theoretical system and research techniques. However, there are still key issues to be resolved in the full interpretation and use of TCM, such as vague active compounds and mechanism of action. Therefore, it is promising to promote the research on TCM through innovative strategies and advanced cutting-edge technologies. Microfluidic chips have provided controllable unique platforms for biomedical applications in TCM research with flexible composition and large-scale integration. In this review, the analysis and biomedical applications of microfluidics in the field of TCM are highlighted, including quality control of Chinese herbal medicines (CHMs), delivery of CHMs, evaluation of pharmacological activity as well as disease diagnosis. Finally, potential challenges and prospects of existing microfluidic technologies in the inheritance and innovation of TCM are discussed.Copyright © 2022 Elsevier B.V.

Federated Learning for Medical Image Analysis with Deep Neural Networks.

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.

BACKGROUND: To date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored. OBJECTIVE: The primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. METHODS: In this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence. RESULTS: The ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians' and the model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92. CONCLUSIONS: Our findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis.

A Study on the Bayesian Network Approach with K-Anonymity in Contact Tracing Applications for COVID-19

Due to the rise of severe and acute infections called Coronavirus 19, contact tracing has become a critical subject in medical science. A system for automatically detecting diseases aids medical professionals in disease diagnosis to lessen the death rate of patients. To automatically diagnose COVID-19 from contact tracing, this research seeks to offer a deep learning technique based on integrating a Bayesian Network and K-Anonymity. In this system, data classification is done using the Bayesian Network Model. For privacy concerns, the K-Anonymity algorithm is utilized to prevent malicious users from accessing patients' personal information. The dataset for this system consisted of 114 patients. The researchers proposed methods such as the K-Anonymity model to remove personal information. The age group and occupations were replaced with more extensive categories such as age range and numbers of employed and unemployed. Further, the accuracy score for the Bayesian Network with k-Anonymity is 97.058%, which is an exceptional accuracy score. On the other hand, the Bayesian Network without k-Anonymity has an accuracy score of 97.1429%. These two have a minimal percent difference, indicating that they are both excellent and accurate models. The system produced the desired results on the currently available dataset. The researchers can experiment with other approaches to address the problem statements in the future by utilizing other algorithms besides the Bayesian one, observing how they perform on the dataset, and testing the algorithm with undersampled data to evaluate how it performs. In addition, researchers should also gather more information from various sources to improve the sample size distribution and make the model sufficiently fair to generate accurate predictions. © 2022 IEEE.

EFFICIENT COVID-19 DISEASE DIAGNOSIS BASED ON COUGH SIGNAL PROCESSING AND SUPERVISED MACHINE LEARNING

The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation;second, cough signal extraction;and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM. © 2022 by the Authors.

AI-Based mHealth App for Covid-19 or Cardiac Diseases Diagnosis and Prognosis

Covid-19 has rapidly spread and affected millions of people worldwide. For that reason, the public healthcare system was overwhelmed and underprepared to deal with this pandemic. Covid-19 also interfered with the delivery of standard medical care, causing patients with chronic diseases to receive subpar care. As chronic heart failure becomes more common, new management strategies need to be developed. Mobile health technology can be utilized to monitor patients with chronic conditions, such as chronic heart failure, and detect early signs of Covid-19, for diagnosis and prognosis. Recent breakthroughs in Artificial Intelligence and Machine Learning, have increased the capacity of data analytics, which may now be utilized to remotely conduct a variety of tasks that previously required the physical presence of a medical professional. In this work, we analyze the literature in this domain and propose an AI-based mHealth application, designed to collect clinical data and provide diagnosis and prognosis of diseases such as Covid-19 or chronic cardiac diseases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Explainable COVID-19 Three Classes Severity Classification Using Chest X-Ray Images

COVID-19 has been raging for almost three years ever since its first outbreak. It is without a doubt that it is a common human goal to end the pandemic and how it was before it started. Many efforts have been made to work toward this goal. In computer vision, works have been done to aid medical professionals into faster and more effective procedures when dealing with the disease. For example, disease diagnosis and severity prediction using chest imaging. At the same time, vision transformer is introduced and quickly stormed its way into one of the best deep learning models ever developed due to its ability to achieve good performance while being resources friendly. In this study, we investigated the performance of ViT on COVID19 severity classification using an open-source CXR images dataset. We applied different augmentation and transformation techniques to the dataset to see ViT's ability to learn the features of the different severity levels of the disease. It is concluded that training ViT using the horizontally flipped images added to the original dataset gives the best overall accuracy of 0.862. To achieve explainability, we have also applied Grad-CAM to the best performing model to make sure it is looking at relevant region of the CXR image upon predicting the class label. © 2022 IEEE.

A Transfer Learning Method for Covid-19 and Pneumonia Diagnosis Based on Chest Radiograph Classification

Pneumonia has been a tough and dangerous human illness for a history-long time, notably since the COVID-19 pandemic outbreak. Many pathogens, including bacteria or viruses like COVID-19, can cause pneumonia, leading to inflammation in patients' alveoli. A corresponding symptom is the appearance of lung opacities, which are vague white clouds in the lungs' darkness in chest radiographs. Modern medicine has indicated that pneumonia-associated opacities are distinguishable and can be seen as fine-grained labels, which make it possible to use deep learning to classify chest radiographs as a supplementary aid for disease diagnosis and performing pre-screening. However, deep learning-based medical imaging solutions, including convolutional neural networks, often encounter a performance bottleneck when encountering a new disease due to the dataset's limited size or class imbalance. This study proposes a deep learning-based approach using transfer learning and weighted loss to overcome this problem. The contributions of it are three-fold. First, we propose an image classification model based on pre-trained Densely Connected Convolutional Networks using Weighted Cross Entropy. Second, we test the effect of masking non-lung regions on the classification performance of chest radiographs. Finally, we summarize a generic practical paradigm for medical image classification based on transfer learning. Using our method, we demonstrate that pre-training on the COVID-19 dataset effectively improves the model's performance on the non-COVID Pneumonia dataset. Overall, the proposed model achieves excellent performance with 95.75% testing accuracy on a multiclass classification for the COVID-19 dataset and 98.29% on a binary classification for the Pneumonia dataset. © 2023 IEEE.

Delayed IBD diagnosis leads to worse outcomes over years, with multiple implications on service delivery

Background: Delays in diagnosis can be patient and health-system related. Such delays have been reported to increase overall complications in Inflammatory Bowel Diseases (IBD). The aim of our study was to report on the impact of delays on IBD-related adverse outcomes (AOs), as hospitals currently face challenges with long waiting lists in the post-COVID-19 era. Method(s): New patients referred for suspected IBD to a single tertiary care centre between Jan 2013 to Dec 2017 were identified using EMR. A cut-off time was set for each delay-type based on best average hospital waiting times. Reasons for delays until start of treatment and data on pre-defined AOs (steroid & other rescue therapies, hospitalisations, surgery) were recorded for each patient until end of June 2021. Data was analysed using multiple Pearson correlations and Cox proportional Hazard model to determine if there was a difference in survival without AOs between patients with and without delay. Result(s): 105 patients were identified using strict criteria (M=58;median age=32y) with a median follow-up of 55 months. The most frequent presenting complaints were abdominal pain (44, 41.9%), loose stools (40, 38.1%), bloody diarrhoea (37, 35.2%) and bleeding perrectum (33, 31.4%). 65, 27 and 13 patients had a final diagnosis of Ulcerative colitis, Crohn's disease and Unclassified colitis respectively, and were analysed jointly. The longest delay-types noted: Patients seeking medical attention (median= 4 months;range 1 to 84 months);arranging gastroenterology clinic review after GP referral (median=5 weeks;1 to 30 weeks);and waiting for index endoscopy (median=3 weeks;1 to 36 weeks). Patient stratification based on delay-type, using specific cut-off times for each showed a statistically significant difference in survival without AOs for all (when comparing delay vs no delay). - delay in seeking medical attention (cut-off=1m;p=0.004) (Fig 1A) - delay in GP referral to specialty review (cut-off=1w;p=0.048) - delay in index endoscopy (cut-off=4w;p=0.01) (Fig 1B) - delay in starting treatment (cut-off=4w;p=0.03) Conclusion(s): Several bottlenecks of delays increase AOs in IBD over the follow-up period. A delay as short as a week, between GP referral to specialty review, is significant in determining AOs, relevant for specialist IBD centres particularly in the post-Covid period. Endoscopy units should prioritise suspected IBD patients to reduce AOs, which is likely to have implications on service delivery and planning. Long delays observed in patients seeking medical attention highlights the need for better patient education in the community.

Automation of COVID-19 Disease Diagnosis from Radiograph

The coronavirus disease (COVID-19) makes humans suffer from mild to moderate respiratory problems, with severe cases requiring special treatment. In many severe cases, elderly individuals and people with pre-existing medical issues like lung-related disease, insulin-dependent disease, and carcinoma, are more prone to difficulty breathing and developing a severe illness. To detect the coronavirus here, X-ray radiograph images are considered. The main motive for using X-ray radiograph images is their being cost-effective and being able to give considerable accuracy compared to its counterpart, computed tomography (CT) scans. In this study, the deep learning model Visual Geometry Group (VGG)16 using the transfer learning method and image augmentation techniques was employed for automatic COVID-19 diagnosis. These two techniques will assist the deep learning model to learn the target task by improving the baseline performance by using fewer X-ray radiograph images in the training phase and showing improvements in the model development time by utilising knowledge gained from a source model. Many deep learning methods have been published in the literature to solve the same cases, but the proposed method uses a simple VGG16 model with transfer learning, which takes less processing time and gives satisfactory results even by using fewer training samples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

ECG-CCNet: Cardiovascular(Cardiac) and COVID19 Disease Classification Using Deep Convolutional Neural Network Learning Pipeline Approaches from Electrocardiography(ECG)- A Study

Experimental studies demonstrate that COVID-19 illness affects the cardiovascular as well as the pulmonary / lung tract. The limits of existing COVID-19 diagnostic procedures have been revealed. In contrast, to present diagnoses, such as low-sensitivity conventional RT-PCR testing and costly healthcare scanning equipment, implementing additional approaches for COVID-19 illness assessment would be advantageous for COVID-19 epidemic management. Furthermore, problems generated by COVID-19 on the cardiovascular tract must be detected rapidly and precisely using ECG. Considering the numerous advantages of electrocardiogram (ECG) functionalities, the proposed study offers a novel pipeline termed ECG-CCNet for examining the feasibility of employing ECG pulses to diagnose COVID-19. This study is a two-phase transfer learning (TL) approach is suggested for the prognosis of COVID-19 disorder, which includes feature mining utilizing DCNNs models and ensemble pipelining using ECG tracing imageries generated from ECG signals of COVID-19 diseased sufferers relying on the anomalies induced by COVID-19 pathogen on cardiovascular structures. A complete classification performance of 93.5% accuracy, 87% recall, 87.03% F1-score, 95.66% specificity, 87.16% precision, and 95.33% AUC attained by abnormal heartbeats, COVID-19, myocardial, and normal/healthy classification. This experiment is considered a high possibility for speeding up the diagnostic and treatments of COVID-19 individuals, reducing practitioners' efforts, and improving epidemic containment by utilizing ECG data. © 2022 IEEE.

Artificial Intelligence in Pharmaceutical and Healthcare Research

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. This review elaborates on the opportunities and challenges of AI in healthcare and pharmaceutical research. The literature was collected from domains such as PubMed, Science Direct and Google scholar using specific keywords and phrases such as ‘Artificial intelligence', ‘Pharmaceutical research', ‘drug discovery', ‘clinical trial', ‘disease diagnosis', etc. to select the research and review articles published within the last five years. The application of AI in disease diagnosis, digital therapy, personalized treatment, drug discovery and forecasting epidemics or pandemics was extensively reviewed in this article. Deep learning and neural networks are the most used AI technologies;Bayesian nonparametric models are the potential technologies for clinical trial design;natural language processing and wearable devices are used in patient identification and clinical trial monitoring. Deep learning and neural networks were applied in predicting the outbreak of seasonal influenza, Zika, Ebola, Tuberculosis and COVID-19. With the advancement of AI technologies, the scientific community may witness rapid and cost-effective healthcare and pharmaceutical research as well as provide improved service to the general public. © 2023 by the authors.

Optimization of CNN for Diagnosis on Lung Disease by Lung Segmentation and Rib Suppression

The discrimination of lung diseases by chest X- ray images is a clinically important tool. How to use artificial intelligence to accurately and quickly help doctors to diagnose different lung diseases is very important in the context of the current COVID-19 global pandemic. In this paper, we propose a model structure, including two U-Net, which implement lung segmentation and rib suppression for chest X-ray images respectively, image enhancement techniques such as histogram equalization, which enhances images contrast, and a Xception- based CNN, which classifies the processed images finally. The model can effectively avoid the interference of regions outside the lung to CNN for feature recognition and the influence of environmental factors such as X-ray machines on the quality of X-ray images and thus on the classification. The experimental results show that the classification accuracy of the model is higher than that of the direct use of the Xception model for classification. © 2022 IEEE.

iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence

Conjunctivitis is one of the common and contagious ocular diseases which affects the conjunctiva of the human eye. Both the bacterial and viral types of it can be treated with eye drops and other medicines. It is important to diagnose the disease at its early stage to realise the connection between it and other diseases, especially COVID-19. Mobile applications like iConDet is such a solution that performs well for the initial screening of Conjunctivitis. In this work, we present with iConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released iConDet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.