This technical report investigates the performance of pre-trained audio models on COVID-19 detection tasks using established benchmark datasets. We fine-tuned Audio-MAE and three PANN architectures (CNN6, CNN10, CNN14) on the Coswara and COUGHVID datasets, evaluating both intra-dataset and cross-dataset generalization. We implemented a strict demographic stratification by age and gender to prevent models from exploiting spurious correlations between demographic characteristics and COVID-19 status. Intra-dataset results showed moderate performance, with Audio-MAE achieving the strongest result on Coswara (0.82 AUC, 0.76 F1-score), while all models demonstrated limited performance on Coughvid (AUC 0.58-0.63). Cross-dataset evaluation revealed severe generalization failure across all models (AUC 0.43-0.68), with Audio-MAE showing strong performance degradation (F1-score 0.00-0.08). Our experiments demonstrate that demographic balancing, while reducing apparent model performance, provides more realistic assessment of COVID-19 detection capabilities by eliminating demographic leakage - a confounding factor that inflate performance metrics. Additionally, the limited dataset sizes after balancing (1,219-2,160 samples) proved insufficient for deep learning models that typically require substantially larger training sets. These findings highlight fundamental challenges in developing generalizable audio-based COVID-19 detection systems and underscore the importance of rigorous demographic controls for clinically robust model evaluation.

The COVID-19 pandemic has placed unprecedented strain on healthcare systems and remains a global health concern, especially with the emergence of new variants. Although real-time polymerase chain reaction (RT-PCR) is considered the gold standard for COVID-19 detection, it is expensive, time-consuming, labor-intensive, and sensitive to issues with RNA extraction. In this context, ATR-FTIR spectroscopy analysis of biofluids offers a reagent-free, cost-effective alternative for COVID-19 detection. We propose a novel architecture that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks, referred to as CNN-BiLSTM, to process spectra generated by ATR-FTIR spectroscopy and diagnose COVID-19 from spectral samples. We compare the performance of this architecture against a standalone CNN and other state-of-the-art machine learning techniques. Experimental results demonstrate that our CNN-BiLSTM model outperforms all other models, achieving an average accuracy and F1-score of 0.80 on a challenging real-world COVID-19 dataset. The addition of the BiLSTM layer to the CNN architecture significantly enhances model performance, making CNN-BiLSTM a more accurate and reliable choice for detecting COVID-19 using ATR-FTIR spectra of non-invasive saliva samples.

Background. Infectious diseases, particularly COVID-19, continue to be a significant global health issue. Although many countries have reduced or stopped large-scale testing measures, the detection of such diseases remains a propriety. Objective. This study aims to develop a novel, lightweight deep neural network for efficient, accurate, and cost-effective detection of COVID-19 using a nasal breathing audio data collected via smartphones. Methodology. Nasal breathing audio from 128 patients diagnosed with the Omicron variant was collected. Mel-Frequency Cepstral Coefficients (MFCCs), a widely used feature in speech and sound analysis, were employed for extracting important characteristics from the audio signals. Additional feature selection was performed using Random Forest (RF) and Principal Component Analysis (PCA) for dimensionality reduction. A Dense-ReLU-Dropout model was trained with K-fold cross-validation (K=3), and performance metrics like accuracy, precision, recall, and F1-score were used to evaluate the model. Results. The proposed model achieved 97% accuracy in detecting COVID-19 from nasal breathing sounds, outperforming state-of-the-art methods such as those by [23] and [13]. Our Dense-ReLU-Dropout model, using RF and PCA for feature selection, achieves high accuracy with greater computational efficiency compared to existing methods that require more complex models or larger datasets. Conclusion. The findings suggest that the proposed method holds significant potential for clinical implementation, advancing smartphone-based diagnostics in infectious diseases. The Dense-ReLU-Dropout model, combined with innovative feature processing techniques, offers a promising approach for efficient and accurate COVID-19 detection, showcasing the capabilities of mobile device-based diagnostics

Physicians routinely assess respiratory sounds during the diagnostic process, providing insight into the condition of a patient's airways. In recent years, AI-based diagnostic systems operating on respiratory sounds, have demonstrated success in respiratory disease detection. These systems represent a crucial advancement in early and accessible diagnosis which is essential for timely treatment. However, label and data scarcity remain key challenges, especially for conditions beyond COVID-19, limiting diagnostic performance and reliable evaluation. In this paper, we propose CoughViT, a novel pre-training framework for learning general-purpose cough sound representations, to enhance diagnostic performance in tasks with limited data. To address label scarcity, we employ masked data modelling to train a feature encoder in a self-supervised learning manner. We evaluate our approach against other pre-training strategies on three diagnostically important cough classification tasks. Experimental results show that our representations match or exceed current state-of-the-art supervised audio representations in enhancing performance on downstream tasks.

Early detection of COVID-19 is crucial for effective treatment and controlling its spread. This study proposes a novel hybrid deep learning model for detecting COVID-19 from CT scan images, designed to assist overburdened medical professionals. Our proposed model leverages the strengths of VGG16, DenseNet121, and MobileNetV2 to extract features, followed by Principal Component Analysis (PCA) for dimensionality reduction, after which the features are stacked and classified using a Support Vector Classifier (SVC). We conducted comparative analysis between the proposed hybrid model and individual pre-trained CNN models, using a dataset of 2,108 training images and 373 test images comprising both COVID-positive and non-COVID images. Our proposed hybrid model achieved an accuracy of 98.93%, outperforming the individual models in terms of precision, recall, F1 scores, and ROC curve performance.

The COVID-19 pandemic has profoundly impacted billions globally. It challenges public health and healthcare systems due to its rapid spread and severe respiratory effects. An effective strategy to mitigate the COVID-19 pandemic involves integrating testing to identify infected individuals. While RT-PCR is considered the gold standard for diagnosing COVID-19, it has some limitations such as the risk of false negatives. To address this problem, this paper introduces a novel Deep Learning Diagnosis System that integrates pre-trained Deep Convolutional Neural Networks (DCNNs) within an ensemble learning framework to achieve precise identification of COVID-19 cases from Chest X-ray (CXR) images. We combine feature vectors from the final hidden layers of pre-trained DCNNs using the Choquet integral to capture interactions between different DCNNs that a linear approach cannot. We employed Sugeno-$\lambda$ measure theory to derive fuzzy measures for subsets of networks to enable aggregation. We utilized Differential Evolution to estimate fuzzy densities. We developed a TensorFlow-based layer for Choquet operation to facilitate efficient aggregation, due to the intricacies involved in aggregating feature vectors. Experimental results on the COVIDx dataset show that our ensemble model achieved 98\% accuracy in three-class classification and 99.50\% in binary classification, outperforming its components-DenseNet-201 (97\% for three-class, 98.75\% for binary), Inception-v3 (96.25\% for three-class, 98.50\% for binary), and Xception (94.50\% for three-class, 98\% for binary)-and surpassing many previous methods.

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

In a hyperconnected environment, medical institutions are particularly concerned with data privacy when sharing and transmitting sensitive patient information due to the risk of data breaches, where malicious actors could intercept sensitive information. A collaborative learning framework, including transfer, federated, and incremental learning, can generate efficient, secure, and scalable models while requiring less computation, maintaining patient data privacy, and ensuring an up-to-date model. This study aims to address the detection of COVID-19 using chest X-ray images through a proposed collaborative learning framework called CL3. Initially, transfer learning is employed, leveraging knowledge from a pre-trained model as the starting global model. Local models from different medical institutes are then integrated, and a new global model is constructed to adapt to any data drift observed in the local models. Additionally, incremental learning is considered, allowing continuous adaptation to new medical data without forgetting previously learned information. Experimental results demonstrate that the CL3 framework achieved a global accuracy of 89.99% when using Xception with a batch size of 16 after being trained for six federated communication rounds. A demo of the CL3 framework is available at https://github.com/zavidparvez/CL3-Collaborative-Approach to ensure reproducibility.

The ongoing COVID-19 pandemic continues to pose significant challenges to global public health, despite the widespread availability of vaccines. Early detection of the disease remains paramount in curbing its transmission and mitigating its impact on public health systems. In response, this study delves into the application of advanced machine learning (ML) techniques for predicting COVID-19 infection probability. We conducted a rigorous investigation into the efficacy of various ML models, including XGBoost, LGBM, AdaBoost, Logistic Regression, Decision Tree, RandomForest, CatBoost, KNN, and Deep Neural Networks (DNN). Leveraging a dataset comprising 4000 samples, with 3200 allocated for training and 800 for testing, our experiment offers comprehensive insights into the performance of these models in COVID-19 prediction. Our findings reveal that Deep Neural Networks (DNN) emerge as the top-performing model, exhibiting superior accuracy and recall metrics. With an impressive accuracy rate of 89%, DNN demonstrates remarkable potential in early COVID-19 detection. This underscores the efficacy of deep learning approaches in leveraging complex data patterns to identify COVID-19 infections accurately. This study underscores the critical role of machine learning, particularly deep learning methodologies, in augmenting early detection efforts amidst the ongoing pandemic. The success of DNN in accurately predicting COVID-19 infection probability highlights the importance of continued research and development in leveraging advanced technologies to combat infectious diseases. I. INTRODUCTION Health represents the cornerstone of any society, yet the world currently grapples with a profound health crisis due to the widespread dissemination of the coronavirus. The global COVID-19 pandemic has inflicted extensive loss of life and has profoundly impacted individuals, both directly and indirectly.

In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manyfold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection, and the clinical validation of the developed solution by expert radiologists. The proposed detection model is based on a two-step approach that, paired with state-of-the-art debiasing, provides reliable results. Most importantly, our investigation includes the actual usage of the diagnosis aid tool by radiologists, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/