Artificial Intelligence Algorithms Research Articles

Artificial intelligence-assisted delineation for postoperative radiotherapy in patients with lung cancer: a prospective, multi-center, cohort study

BackgroundPostoperative radiotherapy (PORT) is an important treatment for lung cancer patients with poor prognostic features, but accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) is challenging and time-consuming. Recently, deep learning-based artificial intelligent (AI) algorithms have shown promise in automating this process.ObjectiveTo evaluate the clinical utility of a deep learning-based auto-segmentation model for AI-assisted delineating CTV and OARs in patients undergoing PORT, and to compare its accuracy and efficiency with manual delineation by radiation oncology residents from different levels of medical institutions.MethodsWe previously developed an AI auto-segmentation model in 664 patients and validated its contouring performance in 149 patients. In this multi-center, validation trial, we prospectively involved 55 patients and compared the accuracy and efficiency of 3 contouring methods: (i) unmodified AI auto-segmentation, (ii) fully manual delineation by junior radiation oncology residents from different medical centers, and (iii) manual modifications based on AI segmentation model (AI-assisted delineation). The ground truth of CTV and OARs was delineated by 3 senior radiation oncologists. Contouring accuracy was evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance of agreement (MDA). Inter-observer consistency was assessed by volume and coefficient of variation (CV).ResultsAI-assisted delineation achieved significantly higher accuracy compared to unmodified AI auto-contouring and fully manual delineation by radiation oncologists, with median HD, MDA, and DCS values of 20.03 vs. 21.55 mm, 2.57 vs. 3.06 mm, 0.745 vs. 0.703 (all P<0.05) for CTV, respectively. The results of OARs contours were similar. CV for OARs was reduced by approximately 50%. In addition to better contouring accuracy, the AI-assisted delineation significantly decreased the consuming time and improved the efficiency.ConclusionAI-assisted CTV and OARs delineation for PORT significantly improves the accuracy and efficiency in the real-world setting, compared with pure AI auto-segmentation or fully manual delineation by junior oncologists. AI-assisted approach has promising clinical potential to enhance the quality of radiotherapy planning and further improve treatment outcomes of patients with lung cancer.

Indentity Management System Using Blockchain and Survey

Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity

Artificial intelligence for detection of retinal toxicity in chloroquine and hydroxychloroquine therapy using multifocal electroretinogram waveforms.

Chloroquine and hydroxychloroquine, while effective in rheumatology, pose risks of retinal toxicity, necessitating regular screening to prevent visual disability. The gold standard for screening includes retinal imaging and automated perimetry, with multifocal electroretinography (mfERG) being a recognized but less accessible method. This study explores the efficacy of Artificial Intelligence (AI) algorithms for detecting retinal damage in patients undergoing (hydroxy-)chloroquine therapy. We analyze the mfERG data, comparing the performance of AI models that utilize raw mfERG time-series signals against models using conventional waveform parameters. Our classification models aimed to identify maculopathy, and regression models were developed to predict perimetric sensitivity. The findings reveal that while regression models were more adept at predicting non-disease-related variation, AI-based models, particularly those utilizing full mfERG traces, demonstrated superior predictive power for disease-related changes compared to linear models. This indicates a significant potential to improve diagnostic capabilities, although the unbalanced nature of the dataset may limit some applications.

Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.

Teledentistry in the detection of oral potentially malignant disorders and oral cancer in the Latin American region: a review of literature with current possibilities

Teledentistry has emerged as a promising tool in bridging the gap in healthcare accessibility, particularly in regions like Latin America region, where resources for oral healthcare are often limited. Drawing upon a comprehensive review of literature, this overview assessed the applications and clinical outcomes of teledentistry in diagnosing oral potentially malignant disorders (OPMDs) and oral cancer, highlighting the challenges and opportunities specific to the Latin American context. Moreover, it examined the integration of artificial intelligence algorithms and teledentistry for enhancing diagnostic accuracy, thereby optimizing resource allocation and improving patient outcomes. By elucidating the current landscape and future prospects, this overview provided insights for policymakers, healthcare providers, and researchers, fostering advancements in oral healthcare delivery with the aim of reducing the burden of OPMDs and oral cancer in the Latin American region.

Transforming Libraries: The Impact of Artificial Intelligence

Artificial intelligence (AI) is revolutionising library services and enhancing user experience, creating a new era of efficiency, accessibility, and innovation. This editorial explores how AI reshapes the library landscape, ushering in a new generation of efficiency, accessibility, and innovation. The AI’s most notable contribution to libraries is the automation of routine tasks, cataloguing and organising; librarians can now rely on AI algorithms to streamline. It saves librarians valuable time engaging in intellectually stimulating activities and ensures a more accurate and organised library system. AI-driven systems can efficiently analyse vast amounts of data, leading to improved search functionalities and more seamless information retrieval for library patrons. Moreover, AI is instrumental in the personalisation of library services. Through machine learning algorithms, libraries can analyse user behaviour, preferences, and historical data to provide customised recommendations. This enhances the user experience by offering tailored suggestions for reading materials, resources, and services, making libraries more relevant and engaging for diverse user groups. Another essential role of AI in libraries is the development of virtual assistants. These digital assistants, powered by natural language processing and machine learning, can help library patrons navigate the catalogue system, answer queries, and even provide Realtime language translation service. Virtual assistants help libraries become more inclusive and accessible, reaching more audiences with diverse language backgrounds and information needs.

Artificial Intelligence and Omics in Malignant Gliomas.

The availability of large scale multi-omics data requires development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large genomic, transcriptomic, proteomic, and metabolomic data. Machine learning (ML) is the most significant AI algorithm in health sciences have exploded, specifically due to the recent progress made by deep learning. Although the use of AI/ML tools in GBM-omics is still at an early stage, a comprehensive discussion of how AI can be used to unravel various aspects of GBM (intratumor heterogeneity, biomarker discovery, survival prediction, and treatment optimization) would be highly relevant to both researchers and clinicians. Here, we aim to review the different AI-based techniques that have been used to study GBM pathogenesis using multi-omics data over the last decade. We first summarize different types of GBM related omics resources that can be used to develop AI models. We then discuss various AI applications for multi-omics data in order to enhance GBM precision medicine. Finally, we discuss the technical and ethical challenges that limit its application and ways to improve its implementation in clinics.

Deep Neural Network-Based Cigarette Filter Defect Detection System with FPGA Acceleration for Online Recognition

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.

Comparative Analysis of Human and Artificial Intelligence Planning in Production Processes

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.

Does work overload of odd-job platform workers lead to turnover intention? An empirical study on platform workers

PurposeAgainst the background of the digital economy, odd-job platforms rely on artificial intelligence algorithms to efficiently allocate tasks and monitor platform workers’ performance, putting these workers under enormous pressure. This paper explores the relationship between work overload and turnover intention of platform workers on odd-job platforms and the factors that lead to platform workers’ turnover.Design/methodology/approachBased on the job demands–resources model (JD-R), we construct a theoretical model to explain the relationship between work overload and turnover intention of platform workers. We test job burnout as a mediator variable and perceived algorithmic fairness and job autonomy as moderating variables. We conducted a study at food delivery platforms and ride-hailing platforms in China.FindingsThe empirical results show that: (1) work overload increases the turnover intention of platform workers by increasing job burnout and (2) perceived algorithmic fairness and job autonomy moderate the positive relationship between work overload and job burnout.Originality/valueWe provide a theoretical basis to explain the influence of work overload on turnover intention of odd-job platform workers and provide practical recommendations for management of platform workers.

Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings

BackgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.MethodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen’s kappa correlation coefficient (k) was employed to measure radiologists’ inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.ResultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study’s findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.ConclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.

Strategies for Perioperative Anticoagulant Reversal in Orthopedic Surgery: A Review.

The administration of anticoagulation therapy during major orthopedic surgeries is a clinical challenge due to the risk of thrombotic events and bleeding complications. This review aims to evaluate the current strategies and emerging developments in perioperative anticoagulation reversal. We conducted a literature review on the management of perioperative anticoagulant therapy, which included the current status of anticoagulant reversal agents, as well as personalized medicine, pharmacogenomics, artificial intelligence (AI), and novel drug delivery systems. The review indicates that reversal agents such as idarucizumab and andexanet alfa are crucial in managing bleeding associated with direct oral anticoagulants (DOACs). Personalized medicine, guided by pharmacogenomics, allows for tailored anticoagulation regimens. AI and machine learning (ML) algorithms can enhance the predictive capabilities for bleeding and thrombotic risks. Additionally, nanotechnology and biomarkers offer innovative approaches to drug delivery and personalized treatment. Integrating evidence-based guidelines with innovative reversal agents, personalized medicine, AI and nanotechnology opens a new era in perioperative anticoagulation management. These advancements can ensure patient safety, minimize bleeding risks, and improve surgical outcomes. Future research should focus on the clinical validation of these strategies to ensure their effectiveness across diverse patient populations.

A Summative Review of Advances in Sensor Technology for Precision Agriculture

Sensor technology has become a cornerstone of precision agriculture, offering a wide array of applications that enhance farming efficiency, productivity, and sustainability. By providing real-time data on soil health, crop growth, and environmental conditions, sensors enable farmers to optimize inputs such as water, fertilizers, and pesticides, reducing waste and improving yields. The development of advanced sensors, such as multi-spectral and hyper-spectral imaging, combined with artificial intelligence (AI) and machine learning (ML) algorithms, has revolutionized crop monitoring and disease detection, allowing for timely interventions that prevent significant losses. Additionally, the integration of wireless sensor networks (WSNs) and the Internet of Things (IoT) facilitates the seamless collection and transmission of data, enabling remote and automated farm management. This technology has proven instrumental in addressing the challenges of food security by increasing agricultural productivity and reducing input costs. Sensor technologies also promote sustainable land and water management by improving irrigation efficiency and reducing chemical runoff, thus minimizing environmental impact. Economic benefits are substantial, with farmers experiencing cost savings, increased yields, and higher profitability. Moreover, sensor technology contributes to climate-smart agriculture by improving resource use efficiency and reducing greenhouse gas emissions. However, challenges remain, including the high cost of sensor installation and maintenance, limited accessibility in remote regions, data privacy concerns, and the lack of interoperability between different sensor platforms. The future of sensor technology lies in the development of low-cost, biodegradable sensors, the increased use of autonomous and robotic platforms, and enhanced AI integration for more accurate data interpretation. Expanding the use of sensors in smallholder and developing world agriculture will be crucial for global food security and environmental sustainability, offering a promising path toward a more resilient and efficient agricultural system.

Using Generative Artificial Intelligence in Exams: A Research on KPSS with ChatGPT

ChatGPT is a language model developed by OpenAI that uses generative artificial intelligence (GenAI) algorithms for natural language processing (NLP) tasks. It is a popular Chatbot due to its impressive ability to respond consistently to users' various prompts. In this study, the Public Personnel Selection Examination (KPSS) questions, organized by ÖSYM (Turkish Student Selection and Placement Center) every year, were asked to ChatGPT, and its performance was evaluated. The evaluation was made based on fundamental text questions asked in the last three years (2021-2023) in Biology, History, Geography, Turkish Language Literature, and Social Studies in KPSS. This study also aims to investigate the accuracy and reliability of the questions asked to ChatGPT in the field of KPSS educational sciences. According to the research results, ChatGPT's KPSS knowledge and interpretation skills are significantly lower than the university graduate candidates who took the exam. Some suggestions are included for better Chatbot performance, more effective prompt engineering and ethical use of these tools.

Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.

To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias. Sensor-enabled strategies are critical for the detection, prediction and management of arrhythmias. In the last several years, great innovation has occurred in the types of devices (implanted and wearable) that are available and the data they collect. The integration of artificial intelligence solutions into sensor-enabled strategies has set the stage for a new generation of smart devices that augment the human clinician. Smart devices enhanced by new sensor technologies and Artificial Intelligence (AI) algorithms promise to reshape the care of cardiac arrhythmias.

Applications and analyses of ammonia slip control based on precise ammonia injection technologies guided by big data

Abstract After the implementation of ultra-low emission policies, the pollutant emissions from the coal-fired power generation systems in China have been further reduced, which creates more critical requirements for the control accuracies of denitrification systems using selective catalytic reduction technology and controls of ammonia slip. This article presents big data-based technologies for controlling ammonia slip, through precise ammonia injections, which were applied and demonstrated for a flue gas denitrification system of a Chinese coal-fired power plant. Through examinations and analyses of the basic operating conditions of the unit and parameters, such as NOx emission control efficiency, ammonia injection amount of the denitrification system, and non-uniformity of NOx concentrations in the denitrification zone were compared before and after the implementation of these technologies, the outcome proves that this artificial intelligence algorithm based on big data can effectively solve the automatic control problem of denitrification system under complex working conditions such as varied unit loads, day and night operation changed coal source. In addition to the effective controls of the ammonia slip of the system, the controls of NOx emission of the system become more stabilized, creating a successful example for wider applications of these precise ammonia injection control technologies in the future. Analyses show that NOx non-uniformities can be reduced by more than 50% under both stable and variable load conditions. NOx fluctuations at the unit outlet are tightly controlled within ±10 mg/Nm3 under variable load conditions and within ±5 mg/Nm3 under stable conditions. The average ammonia injection amounts under various load conditions have decreased by 15.7%.

A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application

Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations use data from various medical facilities to train models for BCa, which may pose a privacy risk. Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. One emerging machine learning paradigm that addresses this concern is federated learning (FL). FL enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multicenter BCa magnetic resonance imaging (MRI) dataset. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

AI in Healthcare: Breaking New Ground in the Management and Treatment of Cancer

Artificial intelligence (AI) is revolutionizing cancer treatment by enhancing patient management, diagnosis, and therapy. Through advanced AI algorithms, early diagnosis of tumors is made more accurate by detecting subtle abnormalities that might be missed by the human eye. This early detection is critical for successful treatment and better patient outcomes. The objective of this research is to evaluate the impact of AI on improving cancer diagnosis, treatment customization, and drug development processes. The findings reveal that AI significantly accelerates the drug discovery process by analyzing large datasets to identify potential candidates, thus reducing the time and costs associated with developing new treatments. Furthermore, AI enhances clinical trials by improving patient recruitment and optimizing trial design, leading to higher success rates. AI-driven telemedicine solutions also help bridge healthcare gaps in underserved areas, ensuring more equitable access to cancer treatment. The research’s implications suggest that, despite challenges such as data security and the need for improved infrastructure, AI holds great potential to further individualize, streamline, and expand the availability of cancer care. Future advancements are likely to make cancer treatment even more precise, effective, and accessible worldwide.

Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm.

Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques.

Exploring bias risks in artificial intelligence and targeted medicines manufacturing

BackgroundThough artificial intelligence holds great value for healthcare, it may also amplify health inequalities through risks of bias. In this paper, we explore bias risks in targeted medicines manufacturing. Targeted medicines manufacturing refers to the act of making medicines targeted to individual patients or to subpopulations of patients within a general group, which can be achieved, for example, by means of cell and gene therapies. These manufacturing processes are increasingly reliant on digitalised systems which can be controlled by artificial intelligence algorithms. Whether and how bias might turn up in the process, however, is uncertain due to the novelty of the development.MethodsExamining stakeholder views across bioethics, precision medicine, and artificial intelligence, we document a range of opinions from eleven semi-structured interviews about the possibility of bias in AI-driven targeted therapies manufacturing.ResultFindings show that bias can emerge in upstream (research and development) and downstream (medicine production) processes when manufacturing targeted medicines. However, interviewees emphasized that downstream processes, particularly those not relying on patient or population data, may have lower bias risks. The study also identified a spectrum of bias meanings ranging from negative and ambivalent to positive and productive. Notably, some participants highlighted the potential for certain biases to have productive moral value in correcting health inequalities. This idea of “corrective bias” problematizes the conventional understanding of bias as primarily a negative concept defined by systematic error or unfair outcomes and suggests potential value in capitalizing on biases to help address health inequalities. Our analysis also indicates, however, that the concept of “corrective bias” requires further critical reflection before they can be used to this end.