Machine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction. The aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results. DA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets. The results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging. Rotation techniques are found significant to enhance the low volume of medical imaging datasets.

Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.

The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.

Asthma is a respiratory condition affecting millions of individuals worldwide, often exacerbated by poor management and worsening weather conditions. As healthcare and weather data continue to expand, identifying the most appropriate and sustainable artificial intelligence (AI) models for asthma care has become a challenging task. Additionally, the integration of multi-modal data through advanced pre-processing and feature selection techniques has emerged as a critical innovation in developing more effective and robust models. This study examines the current state and potential of AI methods in respiratory care, utilizing available data sources to enhance outcomes. The novelty of this work highlights the progression from classical to advanced models, including machine learning, deep learning, and ChatGPT, applied to diverse data in asthma analysis, while outlining key challenges and discussing potential solutions and future directions. The aim of the study is to highlight how machine learning, deep learning, and hybrid model architectures contribute to effective asthma classification, while also demonstrating ChatGPT’s potential as a reliable support tool for physicians in asthma management and administration. It is projected that the review’s findings on key challenges and opportunities will provide insights and uncover potential research directions in asthma assessment through the application of AI models.

Artificial intelligence (AI) algorithms have demonstrated remarkable efficiency in analyzing 12-lead clinical electrocardiogram (ECG) signals. This has sparked interest in leveraging cost-effective and user-friendly smart devices based on single-lead ECG (SL-ECG) for diagnosing heart dysfunction. However, the development of reliable AI model is influenced by the limited availability of publicly accessible SL-ECG datasets. To address this challenge, presented study introduces a novel approach that utilizes 12-lead clinical ECG datasets to bridge this gap. We propose a hierarchical model architecture designed to translate SL-ECG data while maintaining compatibility with 12-lead signals, ensuring a more reliable framework for AI-driven diagnostics. The proposed sequential model utilizes a convolutional neural network enhanced with three integrated translational layers, trained on individual 12-lead clinical ECG, to significantly improve classification performance on SL-ECG. The experimental analysis is conducted using three benchmark datasets: Physikalisch-Technische Bundesanstalt (PTB-XL), Computing in Cardiology Challenge 2017, and China Physiological Signal Challenge 2018. This study also evaluates the effects of denoising techniques and lead polarity variations, including biphasic and negative deflections. Results show that the model achieved over 82% test accuracy on unseen SL-ECG signals, with an area under the receiver operating characteristic of 0.81, sensitivity of 76.60%, and specificity of 83.44% when trained on clinical lead I. Additionally, leads II, V4, and V5 demonstrated potential for effective AI model training. The work supports advancement of smart devices by enhancing SL-ECG classification and assists clinicians in assessing heart abnormalities more effectively.