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In the realm of intelligent healthcare, there is a growing ambition to reshape medical services through the integration of artificial intelligence (AI). However, conventional machine learning faces inherent challenges such as privacy issues, delayed updates, and protracted training times, particularly due to the hesitance of medical institutions to directly share sensitive data, with possible noises. In response to these concerns, a Quantum-Assisted Federated Intelligent Diagnosis Algorithm ( β -QuAFIDA) is proposed, applied into real medical data. Leveraging the capabilities of the 5G mobile network, this approach works the connection between Internet of Medical Things (IoMT) devices through the 5G, synchronizing training and updating the server model without disrupting their real-world applications. In our quest to safeguard patient data and enhance training efficiency, our study employs an innovative heuristic approach marked by a nested loop structure. Specifically, the inner loop is dedicated to training the beta-variational quantum eigensolver ( β -VQE) to approximate the expectation values of the proposed algorithm; the outer loop trains the β -QuAFIDA to reduce the relative entropy towards the target. This approach involves a balance between privacy considerations and the urgency of training. Results demonstrate that representations with low-rank attained through β -QuAFIDA offer an effective approach for acquiring low-rank states. This research signifies a step forward in the synergy between AI and 5G technologies, presenting a novel avenue for the advancement of intelligent healthcare., (© 2024. The Author(s).)

The characterization of pulmonary nodules (PN) is a primary indication for [ 18 F]-FDG PET/CT. However, respiratory movements hinder this characterization, especially for PN located in the lower lobes. Various methods have been developed to improve image resolution., Objective: Our objective was to compare the diagnostic efficacy of [ 18 F]-FDG PET/CT in deep inspiration breath-hold (DIBH) versus free-breathing corrected by software, in the evaluation of PN., Methods: We prospectively analyzed 51 patients to assess PN using [ 18 F]-FDG PET/CT in DIBH and free-breathing corrected by software. A total of 84 nodules with an average size of 10 mm were analyzed, with pathological anatomy or medical treatment decide by a multidisciplinary tumor board used as reference., Results: A total of 84 PN were evaluated, comparing those in DIBH versus free-breathing, finding statistically significant differences in SUVmax values P(< 0.05) (mean SUVmax 3.7 in free-breathing vs. 5.33 in DIBH). When analyzed by location in lobes, we did not find statistically significant differences, though there was a trend towards higher SUVmax values in the lower lobes. [ 18 F]-FDG PET/CT in DIBH showed high sensitivity (95%) and negative predictive value (NPV) (92%), indicating it may be a promising tool for PN characterization., Conclusions: The acquisition of [ 18 F]-FDG PET/CT in DIBH significantly improves the sensitivity and diagnostic efficacy in the assessment of PN. Although no statistically significant differences were found based on location, there is a potential benefit for the lower lobes. These findings could support its use in clinical practice., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2024. Published by Elsevier España, S.L.U.)

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.)

The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning present advantages using that network data. Nevertheless, in a routing algorithm based on reinforcement learning (RL) technique, additional control message headers could be required. In this context, this research presents an enhanced routing protocol based on RL, named e-RLRP, in which the overhead is reduced. Specifically, a dynamic adjustment in the Hello message interval is implemented to compensate the overhead generated by the use of RL. Different network scenarios with variable number of nodes, routes, traffic flows and degree of mobility are implemented, in which network parameters, such as packet loss, delay, throughput and overhead are obtained. Additionally, a Voice-over-IP (VoIP) communication scenario is implemented, in which the E-model algorithm is used to predict the communication quality. For performance comparison, the OLSR, BATMAN and RLRP protocols are used. Experimental results show that the e-RLRP reduces network overhead compared to RLRP, and overcomes in most cases all of these protocols, considering both network parameters and VoIP quality.