Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
Artificial intelligence (AI) is a growing area of computer science that combines technologies with data science to develop intelligent, highly computation-able systems. Its ability to automatically analyze and query huge sets of data has rendered it essential to many fields such as healthcare. This article introduces you to artificial intelligence, how it works, and what its central role in biomedical engineering is. It brings to light new developments in medical science, why it is being applied in biomedicine, key problems in computer vision and AI, medical applications, diagnostics, and live health monitoring. This paper starts with an introduction to artificial intelligence and its major subfields before moving into how AI is revolutionizing healthcare technology. There is a lot of emphasis on how it will transform biomedical engineering through the use of AI-based devices like biosensors. Not only can these machines detect abnormalities in a patient’s physiology, but they also allow for chronic health tracking. Further, this review also provides an overview of the trends of AI-enabled healthcare technologies and concludes that the adoption of artificial intelligence in healthcare will be very high. The most promising are in diagnostics, with highly accurate, non-invasive diagnostics such as advanced imaging and vocal biomarker analyzers leading medicine into the future....
Standard 2D cultures inadequately mimic the natural microenvironment of mesenchymal stromal cells (MSCs), compromising their properties. This study investigated the impact of 3D cultures in spheroids, alginate microspheres (AMSs), and blood plasma scaffolds on human-adiposederived MSC behavior. The cell morphology, viability/apoptosis (6-CFDA/Annexin-Cy3.18), actin filament development (phalloidin-FITC), and metabolic activity (Alamar Blue) were assessed on the 3rd day of the generated 3D construct cultures. The abilities for adipogenic and osteogenic differentiation were evaluated after 21 days of culture in media with inducers by Nile Red and Alizarin Red staining, respectively. The 3D culture supported closer-to-physiological cell interactions and morphology and resulted in F-actin reduction compared with the 2D culture. While the metabolic activity was elevated in the scaffolds, it was significantly reduced in the spheroids and AMSs, which reflected natural-like quiescence. The differentiation was maintained across all the 3D constructs. These findings highlight the essential influence of 3D construct design on MSC function, underscoring its potential for advancing both in vitro models and cell-based therapies....
Background/Objectives: The methacholine bronchial provocation test (MBPT) is a diagnostic test frequently used to evaluate airway hyper-reactivity. MBPT is essential for diagnosing asthma; however, it can be time-consuming and resource-intensive. This study aimed to develop an artificial intelligence (AI) model to predict the MBPT results using forced expiratory volume in one second (FEV1) and bronchodilator test measurements from spirometry. Methods: a dataset of spirometry measurements, including Pre- and Post-bronchodilator FEV1, was used to train and validate the model. Results: Among the evaluated models, the multilayer perceptron (MLP) achieved the highest area under the curve (AUC) of 0.701 (95% CI: 0.676–0.725), accuracy of 0.758, and an F1-score of 0.853. Logistic regression (LR) and a support vector machine (SVM) demonstrated comparable performance with AUC values of 0.688, while random forest (RF) and extreme gradient boost (XGBoost) achieved slightly lower AUC values of 0.669 and 0.672, respectively. Feature importance analysis of the MLP model identified key contributing features, including Pre-FEF25–75 (%), Pre-FVC (L), Post FEV1/FVC, Change-FEV1 (L), and Change-FEF25–75 (%), providing insight into the interpretability and clinical applicability of the model. Conclusions: These results highlight the potential of the model to utilize readily available spirometry data, particularly FEV1 and bronchodilator responses, to accurately predict MBPT results. Our findings suggest that AI-based prediction can improve asthma diagnostic workflows by minimizing the reliance on MBPT and enabling faster and more accessible assessments....
Optical fiber technology plays a critical role in modern neuroscience towards understanding the complex neuronal dynamics within the nervous system. In this study, we manufactured and characterized amorphous thermally drawn poly D, L- lactic acid (PDLLA) biodegradable optical fibers in different diameters. These optical fibers were then implanted into the lateral posterior region of the mouse brain for four months, allowing us to assess their degradation characteristics. The gradual dissolution of the implanted PDLLA optical fibers in the brain was confirmed by optical, photoacoustic, and scanning electron microscopy (SEM), light propagation characteristics, and molecular weight measurements. The results indicate that the degradation rate of the biodegradable optical fiber was mainly pronounced during the first week. After four months, degradation led to the formation of micropores on the surface of the implanted fiber within the gray matter region of the brain. We believe that the PDLLA biodegradable optical fiber developed in this study constitutes a promising candidate for further functionalization and development of next- generation biocompatible, soft, and biodegradable bi- directional neural interfaces....
Background: Alzheimer’s disease (AD) is a neurodegenerative condition that has no definitive treatment, and its early diagnosis can help to prevent or slow down its progress. Structural magnetic resonance imaging (sMRI) and the progress of artificial intelligence (AI) have significant attention in AD detection. This study aims to differentiate AD from NC and distinguish between LMCI and EMCI from the other two classes. Another goal is the diagnostic performance (accuracy and AUC) of sMRI for predicting AD in its early stages. Methods: In this study, 398 participants were used from the ADNI and OASIS global database of sMRI including 98 individuals with AD, 102 with early mild cognitive impairment (EMCI), 98 with late mild cognitive impairment (LMCI), and 100 normal controls (NC). Results: The proposed model achieved high area under the curve (AUC) values and an accuracy of 99.7%, which is very remarkable for all four classes: NC vs. AD: AUC = [0.985], EMCI vs. NC: AUC = [0.961], LMCI vs. NC: AUC = [0.951], LMCI vs. AD: AUC = [0.989], and EMCI vs. LMCI: AUC = [1.000]. Conclusions: The results reveal that this model incorporates DenseNet169, transfer learning, and class decomposition to classify AD stages, particularly in differentiating EMCI from LMCI. The proposed model performs well with high accuracy and area under the curve for AD diagnostics at early stages. In addition, the accurate diagnosis of EMCI and LMCI can lead to early prediction of AD or prevention and slowing down of AD before its progress....
Loading....