Consumer electronics have transformed the way we interact with technology, improving convenience and connectivity in day-to-day lives. In the healthcare sector, recent technologies have resulted in enhanced diagnosis, treatment, and patient care. Wearables, artificial intelligence-based data analytics, and telemedicine transform the way of monitoring and managing health, fostering a proactive approach to well-being. The popularity of ChatGPT is proven great potential for AI-generated content (AIGC) that has formed a major impact on the artificial intelligence (AI) community and accelerates the reconsidering of the prospects of general AI. The AIGC is also exposed as a considerable scope to impulse healthcare electronics (HE). Although generative AI has achieved popularity like the formation of images, it could be employed for producing synthetic tabular information. The production of synthetic electronic health records (EHR) undertakes to increase the utilization of machine learning (ML) methods that commonly function with massive quantities of data. ML will identify non-intuitive classifier patterns that permit a new integration of patient feature predictive ability. Currently, deep learning (DL) techniques are effectively utilized in EHR data from medical domains. DL methods excellently captured the significant and beneficial features and patterns from the comprehensive medical information in EHR data. This study presents AI-generated content for Synthetic Electronic Health Record Generation with a Deep Learning-based Diagnosis (SEHRG-DLD) Model. The focus of the SEHRG-DLD technique is to initially generate the synthetic EHR data and then analyze the medical data for disease diagnosis using the DL model. The SEHRG-DLD technique comprises a two-stage process: synthetic data generation and disease diagnosis. At the initial stage, the SEHRG-DLD technique uses the ChatGPT tool to generate synthetic EHR data. Then, the SEHRG-DLD technique undergoes the disease diagnosis process using three sub-processes namely Harris Hawks Optimization (HHO) based feature selection, deep belief network (DBN) based classification, and Golden Jackal Optimization (GJO) based hyperparameter tuning. The application of the HHO and GJO algorithms helps in accomplishing enhanced diagnostic performance of the SEHRG-DLD technique. The performance analysis of the SEHRG-DLD technique is examined by employing the ChatGPT-generated dataset. The experimental results clearly stated the supremacy of the SEHRG-DLD technique over other recent methods for different measures.

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios.

HomeRadiology: Imaging CancerVol. 6, No. 3 PreviousNext Letters to the EditorClarification of Concerns about the Demographic Composition of The Cancer Imaging ArchiveJanet F. Eary* , Lalitha K. Shankar*, John Freymann†, Justin Kirby†Janet F. Eary* , Lalitha K. Shankar*, John Freymann†, Justin Kirby†Author AffiliationsNCI Cancer Imaging Program, 9609 Medical Center Dr, Bethesda, MD 20892-9725*Cancer Imaging Informatics Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Md†e-mail: [email protected]Janet F. Eary* Lalitha K. Shankar*John Freymann†Justin Kirby†Published Online:May 31 2024https://doi.org/10.1148/rycan.240098MoreSectionsFull textPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookXLinked In References1. Dulaney A, Virostko J. Disparities in the Demographic Composition of The Cancer Imaging Archive. Radiol Imaging Cancer 2024;6(1):e230100. Google Scholar2. Miles RC, Porras AR. Implications of Data Representation in Health Care Innovation. Radiol Imaging Cancer 2024;6(1):e230222. Google ScholarArticle HistoryPublished online: May 31 2024 FiguresReferencesRelatedDetailsRecommended Articles Are Artificial Intelligence Challenges Becoming Radiology's New "Bee's Knees"?Radiology: Artificial Intelligence2021Volume: 3Issue: 3Navigating Generational Differences in RadiologyRadioGraphics2018Volume: 38Issue: 6pp. 1672-1679The Future of AI and Informatics in Radiology: 10 PredictionsRadiology2023Volume: 309Issue: 1Electronic Health Record Closed-Loop Communication Program for Unexpected Nonemergent FindingsRadiology2021Volume: 301Issue: 1pp. 123-130The Impact of the COVID-19 Pandemic on the Radiology Research Enterprise: Radiology Scientific Expert PanelRadiology2020Volume: 296Issue: 3pp. E134-E140See More RSNA Education Exhibits Delayed Diagnostic Evaluation of Symptomatic Breast Cancer in Sub-Saharan Africa: Lessons Learned from TanzaniaDigital Posters2020Putting The "Culture" Back Into Just Culture: Engaging Radiology Departments In The Journey To Safer CareDigital Posters2021A Grassroots Approach To Forming A Diversity, Equity, And Inclusion Committee In An Academic Radiology Department: Early Successes And Lessons LearnedDigital Posters2021 RSNA Case Collection COVID-19 related MIS (multisystem inflammatory syndrome) RSNA Case Collection2020Persistent fetal vasculature (PFV)RSNA Case Collection2020Mucinous Adenocarcinoma in Perianal FistulaRSNA Case Collection2021 Vol. 6, No. 3 Metrics Altmetric Score PDF download

In recent times, the Industrial Internet of Things (IIoT) has developed significantly. In the application of automation, and intelligence, industrial digitalization introduced cyber risks, and the varied and complex industrial IoT platform presented a novel attack surface for network invaders. Several Intrusion Detection Systems (IDS) were advanced recently as many computer networks exposure to privacy and security threats. Availability, Data confidentiality, and integrity, the damage will happen in case of IDS prevention failure. Traditional methods were ineffective in dealing with advanced attacks. Advanced deep learning (DL) methods were designed for automatic ID and abnormal behavior detection of networks. Therefore, this article focuses on the design of Improved Reptile Search Optimization with Ensemble Deep Learning based Cybersecurity (IRSO‐EDLCS) technique in the IIoT environment. The major aim of the IRSO‐EDLCS technique lies in the accurate identification of cyberattacks in the IIoT environment. To accomplish this, the presented IRSO‐EDLCS technique performs IRSO algorithm‐based feature selection (IRSO‐FS) technique. In addition, the IRSO‐EDLCS technique performs an ensemble of three DL models namely deep belief network (DBN), bidirectional gated recurrent unit (BiGRU), and autoencoder (AE). The hyperparameter tuning process is performed by a modified gray wolf optimizer (MGWO) to enhance detection process. To exhibit the improved performance of the IRSO‐EDLCS algorithm, a wide range of simulations were performed on the benchmark database. The experimental outcomes depict the betterment of the IRSO‐EDLCS technique over other existing models.

The National Institutes of Health-US Food and Drug Administration Joint Leadership Council Next-Generation Sequencing and Radiomics Working Group was formed by the National Institutes of Health-Food and Drug Administration Joint Leadership Council to promote the development and validation of innovative next-generation sequencing tests, radiomic tools, and associated data analysis and interpretation enhanced by artificial intelligence and machine learning technologies. A 2-day workshop was held on September 29-30, 2021, to convene members of the scientific community to discuss how to overcome the "ground truth" gap that has frequently been acknowledged as 1 of the limiting factors impeding high-quality research, development, validation, and regulatory science in these fields. This report provides a summary of the resource gaps identified by the working group and attendees, highlights existing resources and the ways they can potentially be employed to accelerate growth in these fields, and presents opportunities to support next-generation sequencing and radiomic tool development and validation using technologies such as artificial intelligence and machine learning.

The conventional e-commerce business chain is undergoing a transformation centered on short videos and live streams, giving rise to interest-based e-commerce as a burgeoning trend in the industry. Varied content stimulates the fast growth of interest in e-commerce. By employing wireless sensor networks (WSNs) to collect real-time data on user behavior, preferences, and contextual factors, businesses employ high-tech analytics and predictive modeling systems to evaluate individual purchasing power. This new integration supports E-commerce platforms to offer personalized and targeted product recommendations, pricing strategies, and promotional campaigns, thus optimizing the customer shopping experience. The WSN-assisted predictive abilities not only allow businesses to tailor their offerings to particular user segments for contributing to the overall performances and effectiveness of E-commerce ecosystems in a gradually dynamic market. This study develops a WSN-Assisted Consumer Purchasing Power Prediction via Barracuda Swarm Optimization Algorithm Driven Deep Learning (CP3-BSOADL) for E-Commerce Systems. The major aim of the CP3-BSOADL technique is to precisely forecast the procuring power level with the customer content preferences to offer new concepts for interest e-commerce systems. In the CP3-BSOADL technique, two major processes are involved. For the prediction process, the CP3-BSOADL technique utilizes a stacked auto-encoder (SAE) model which effectually forecasts the purchasing power of the consumers for e-commerce systems. Besides, the BSO algorithm can be applied to effectually fine-tune the hyperparameters related to the SAE model which leads to accomplishing enhanced predictive results. The performance analysis of the CP3-BSOADL technique is tested using an e-commerce dataset. The extensive result analysis stated that the CP3-BSOADL technique gains better performance over other recent state-of-the-art approaches in terms of distinct measures.

Medical image analysis is an essential part of modern healthcare, helping to identify, manage, and track a wide range of medical issues. The increasing volume and complexity of medical imaging data necessitate advanced tools and technologies to automate and improve the diagnostic process. Deep learning frameworks, such as 3D Slicer, MONAI, SAM, and YOLOv8, have emerged as powerful solutions that utilize artificial intelligence to analyze and interpret medical images accurately and efficiently. This study focuses on integrating these four prominent deep learning frameworks into automated medical image diagnosis, offering capabilities ranging from image segmentation to real-time visualization and object detection. By leveraging the computational power of Deep Neural Networks (DNN), the healthcare professionals can extract valuable insights from complex medical imaging datasets, leading to better decision-making and patient outcomes. Evaluating and comparing these frameworks is crucial for understanding their strengths and limitations in medical image analysis. 3D Slicer excels in interactive 3D visualization and segmentation, while MONAI provides an end-to-end solution for medical data processing. SAM Modal is known for its adaptability in managing segmentation tasks, especially in liver lesion identification. YOLOv8 shows promise in detecting abnormalities and tumors through its object detection capabilities.

The growth of the Internet of Things (IoT) has intensely enlarged the number of related devices creating and consuming data. To handle this ever-growing data flow, Next-Generation networks are developing near a hybrid architecture, weaving organized edge computing power (Fog) with the cloud’s vast resources. However, orchestrating and scheduling jobs across this dissimilar landscape presents a difficult task. Scheduling in Next-Generation IoT-Fog-Cloud Networks is a dangerous facet in attaching the full potential of the organized landscape of IoT, fog computing, and cloud infrastructure. By authorizing effectual scheduling, metaheuristic algorithms donate to improved survivability in Next-Generation systems. They guarantee on-time task implementation, diminish resource bottlenecks, and allocate computational loads efficiently, decreasing the effect of potential failures. With strong scheduling, these networks can adjust to unpredictable states, ensuring seamless data flow and constant service for both real-time and non-real-time uses. This manuscript offers the design of a Metaheuristic Mountain Gazelle Optimization Algorithm based task scheduling approach (MMGOA-TSA) in the Next-Generation IoT Fog-Cloud Networks. The foremost intention of the MMGOA-TSA technique is to optimally plan the IoT demands in the IoT fog-cloud network. The MMGOA-TSA technique follows the concept of MGOA, which is stimulated by the social life and wild mountain gazelles (MG) hierarchy. Meanwhile, the MMGOA-TSA technique determines the optimal candidate solutions from the fog or cloud nodes for offloading any IoT demands which can be executed in such a method that the effective trade-off among response time and energy utilization in the method can be accomplished. The experimental validation of the MMGOA-TSA technique is verified by employing a set of simulations. The comparative result analysis stated that the MMGOA-TSA technique gains better performance over other techniques in terms of distinct actions.

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

Brain-computer interface BCI) is a technology that assists in straight link among the human brain as well as external devices like computers or robotic systems, without including muscles and peripheral nerves. BCI allows individuals with motor disabilities to manage external devices with the aid of brain signals such as motor imagery detected from electroencephalography (EEG) signals. An EEG Motor Imagery Classification for BCI is a specific application of EEG in which brain signals directly related to motor imagery tasks are analyzed and classified to control external devices or applications, namely robotic systems or computers. In this regard, the study introduces a Jellyfish Optimization with Fuzzy Logic Enabled EEG Motor Imagery Classification for Brain Computer Interface (JFOFL-MICBCI) technique. The JFOFL-MICBCI technique aims to exploit the fuzzy logic system with metaheuristics for classifying EEC motor imagery signals. It initially executes Continuous Wavelet Transform (CWT) for transforming 1D-EEG signals into 2D time-frequency amplitude ones. For feature extraction, the JFOFL-MICBCI technique uses the SqueezeNet method, and its hyperparameters can be adjusted by the employ of the JFO system. The JFOFL-MICBCI method exploits the adaptive neuro-fuzzy inference system (ANFIS) approach for performing the classification process. A comprehensive range of experiments has been accompanied to demonstrate the higher efficiency of the JFOFL-MICBCI technique. The obtained results inferred the better of the JFOFL-MICBCI technique with other recent systems.

Consumer product recognition involves utilizing computer vision (CV) and machine learning (ML) systems for identifying and recognizing numerous consumer goods in video or image frames. This technology determines applications in retail, e-commerce, security systems, and inventory management. The purpose is to automate the detection of certain products or objects in visual data, streamlining several procedures and improving efficacy. Deep Learning (DL), implementing structures such as convolutional neural networks (CNNs), automates the detection and classification of consumer products in images or video frames. Trained on diverse datasets, these techniques learn intricate features, benefiting retail and e-commerce by enhancing inventory management and improving the shopping experience through automated product detection. This study develops a honey badger algorithm with ensemble learning for consumer product identification and classification (HBAEL-CPIC) technique. The goal of the HBAEL-CPIC technique is to detect and classify various kinds of consumer products accurately. To accomplish this, the HBAEL-CPIC technique employs a non-local means (NLM) filter to eradicate the occurrence of the noise. Next, the HBAEL-CPIC technique follows the capsule network (CapsNet) model for the feature extractor process. Meanwhile, the hyperparameter tuning of the CapsNet approach has been carried out using the HBA. At last, the ensemble of three models namely extreme learning machine (ELM), descriptive back propagated: radial basis function (DBRF), and extreme gradient boosting (XGBoost) are used for classification. To highlight the improved detection solution of the HBAEL-CPIC technique, a series of experiments were performed. The comprehensive comparison study of the HBAEL-CPIC technique exhibited a superior accuracy value of 96.56% over other DL approaches.

<p>Detailed overview of 510(k) cleared imaging devices by the FDA.</p>

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

Deep learning and the Web of Things (WoT) have become powerful tools for web engineering, leading to increased investigation and publication of research related to deep learning in web engineering. Therefore, this workshop is titled "The 3rd International Workshop on Deep Learning for the Web of Things" for the Web Conference 2023 (WWW’23). This workshop features five research articles: (1) "Multiple-Agent Deep Reinforcement Learning for Avatar Migration in Vehicular Metaverses", (2) "Web 3.0: Future of the Internet", (3) "Weighted Statistically Significant Pattern Mining", (4) "DSNet: Efficient Lightweight Model for Video Salient Object Detection for IoT and WoT Applications", and (5) "The Human-Centric Metaverse: A Survey".

Significance: This third biennial intraoperative molecular imaging (IMI) conference shows how optical contrast agents have been applied to develop clinically significant endpoints that improve precision cancer surgery. Aim: National and international experts on IMI presented ongoing clinical trials in cancer surgery and preclinical work. Previously known dyes (with broader applications), new dyes, novel nonfluorescence-based imaging techniques, pediatric dyes, and normal tissue dyes were discussed. Approach: Principal investigators presenting at the Perelman School of Medicine Abramson Cancer Center's third clinical trials update on IMI were selected to discuss their clinical trials and endpoints. Results: Dyes that are FDA-approved or currently under clinical investigation in phase 1, 2, and 3 trials were discussed. Sections on how to move benchwork research to the bedside were also included. There was also a dedicated section for pediatric dyes and nonfluorescence-based dyes that have been newly developed. Conclusions: IMI is a valuable adjunct in precision cancer surgery and has broad applications in multiple subspecialties. It has been reliably used to alter the surgical course of patients and in clinical decision making. There remain gaps in the utilization of IMI in certain subspecialties and potential for developing newer and improved dyes and imaging techniques.

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.

As the immuno-oncology field continues the rapid growth witnessed over the past decade, optimising patient outcomes requires an evolution in the current response-assessment guidelines for phase 2 and 3 immunotherapy clinical trials and clinical care. Additionally, investigational tools-including image analysis of standard-of-care scans (such as CT, magnetic resonance, and PET) with analytics, such as radiomics, functional magnetic resonance agents, and novel molecular-imaging PET agents-offer promising advancements for assessment of immunotherapy. To document current challenges and opportunities and identify next steps in immunotherapy diagnostic imaging, the National Cancer Institute Clinical Imaging Steering Committee convened a meeting with diverse representation among imaging experts and oncologists to generate a comprehensive review of the state of the field.