Artificial Intelligence and Vascular Surgery

Current applications of artificial intelligence in vascular surgery

Chapter 31 - Artificial intelligence in vascular surgery

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

Focus issue: Artificial intelligence in medical physics.

Taming the Complexity: Using Artificial Intelligence in a Cross-Disciplinary Innovative Platform to Redefine Molecular Imaging and Radiopharmaceutical Therapy

To analyze the structural and temporal evolution of artificial intelligence (AI) and digital health applications in vascular surgery over the past two decades, identifying historical development trajectories, research focal points, and emerging frontiers. Publications on AI and digital health applications in vascular surgery were retrieved from WoSCC. Analyzed through CiteSpace and HistCite to track temporal development, thematic shifts, and innovation patterns within the domain. Active themes have emerged over time, with 123 related disciplines, 505 keywords, and 675 outbreak papers cited. Keyword clustering anchors seven emerging research subfields, namely #0 deep learning, #2 machine learning, #3 peripheral arterial disease, #4 renal cell carcinoma, #5 aortic aneurysm, #6 pulmonary embolism, #7nanocarrier. The alluvial map indicates that the most enduring research concepts within the domain include bypass, revascularisation, and others, while emerging keywords consist of chronic limb-threatening ischemia and peripheral vascular intervention, among others. Reference clustering identifies seven recent subfields of research: nephrectomy #0, force #1, artificial intelligence #2, navigation #4, prediction #5, augmented reality #9, and telemedicine #13. This study provides a comprehensive mapping of AI and digital health adoption in vascular surgery, delineating paradigm shifts from traditional surgical techniques to computational prediction models and intelligent intervention systems. The findings establish foundational references for prioritizing research investments and developing standardized evaluation metrics for emerging technologies.

Artificial intelligence (AI) has been applied in many fields, including science, technology, and medicine. However, vascular surgeons face many obstacles and limitations in using and applying AI because of their limited understanding of computer science, programming languages, and complex AI technologies, such as machine learning, deep learning, and artificial neural networks. This article describes the basic knowledge of AI, applications of AI technologies in vascular surgery, and the use of smart wearable devices. Finally, the challenges and limitations of AI are discussed as essential issues hindering its widespread application in vascular surgery.

Background. Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia defined by the presence of a genetic abnormality, namely the PML::RARA gene fusion, as the result of a reciprocal balanced translocation between chromosome 17 and chromosome 15. APL is a veritable emergency in hematology due to the risk of early death and coagulopathy if left untreated; thus, a rapid diagnosis is needed in this hematological malignancy. Needless to say, cytogenetic and molecular biology techniques, i.e., fluorescent in situ hybridization (FISH) and polymerase chain reaction (PCR), are essential in the diagnosis and management of patients diagnosed with APL. In recent years, the use of artificial intelligence (AI) and its brances, machine learning (ML), and deep learning (DL) in the field of medicine, including hematology, has brought to light new avenues for research in the fields of blood cancers. However, to our knowledge, there is no comprehensive evaluation of the potential applications of AI, ML, and DL in APL. Thus, the aim of the current publication was to evaluate the prospective uses of these novel technologies in APL. Methods. We conducted a comprehensive literature search in PubMed/MEDLINE, SCOPUS, and Web of Science and identified 20 manuscripts eligible for the qualitative analysis. Results. The included publications highlight the potential applications of ML, DL, and other AI branches in the diagnosis, evaluation, and management of APL. The examined AI models were based on the use of routine biological parameters, cytomorphology, flow-cytometry and/or OMICS, and demonstrated excellent performance metrics: sensitivity, specificity, accuracy, AUROC, and others. Conclusions. AI can emerge as a relevant tool in the evaluation of APL cases and potentially contribute to more rapid screening and identification of this hematological emergency.

The use of artificial intelligence (AI) technology and machine learning (ML) is growing exponentially and is moving from AI to applied intelligence. Pharma industry is actively exploring the potential use of AI tools in new product discovery and clinical development. Some of the practical applications of AI in clinical development are for improving the efficiency of enrollment, selection and stratification of participants, optimizing study treatment, enhancing compliance, data analysis, and pharmacovigilance. AI applications have been used for outcome prediction; covariate selection/confounding adjustment; anomaly detection; real-world data phenotyping; imaging, video, and voice analysis; endpoint assessment; and pharmacometric modeling in regulatory submissions. However, widespread applications of novel yet difficult-to-understand AI technology in clinical development would need balancing the benefits and risks and resolving issues of scientific validity, technical quality, and ethics. The article discusses the potential benefits and emerging concerns of applying AI in clinical drug development.

Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients’ survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients’ exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies’ role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.

Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines.

The introduction of artificial intelligence (AI) technologies, the construction industry is on track for a technological revolution. In order to investigate the potential of artificial intelligence (AI) to improve sustainability, safety, and efficiency in the construction industry, this research paper offers a thorough examination of these applications. The study looks at several AI methods, including robots, computer vision, machine learning, and natural language processing, and how they are used in the design, planning, scheduling, monitoring, and maintenance phases of the building lifespan. In order to show the concrete advantages of AI in maximizing resource allocation, cutting project delays, enhancing quality control, and minimizing risks, it also looks at case studies and real-world applications. The study also discusses ethical issues and addresses issues like security of data, workforce upskilling, and interaction with current systems. This report offers useful insights for practitioners, policymakers, and researchers interested in maximizing the revolutionary potential of artificial intelligence (AI) in the construction industry by integrating existing research and industry trends. In the construction industry, any error, miscalculation, or misinterpretation can result in claims, delays in projects, and large cost overruns. The documentation and construction contracting processes are very complex and time-consuming. This research is done to make the process of documentation easy using the AI tools. The respondent’s opinion is consistent (Cronbach alpha is greater than 0.80). Educational qualification is influencing application of AI in construction industry by stating that construction industry gets benefitted from AI-powered construction simulation tools helps in accurate 3D modelling for monitoring the progress of the project and also influencing the application of AI in construction industry through Workers are resistant to adopt AI technology due to their lack of skill & awareness in using this technology as a barrier/challenge and also influencing application of AI in construction industry by proving the phenomenal level of acceptance for Collaboration with AI technology developers helps in adopting the AI technologies in construction industry as an enabler to the challenges of application of AI in construction industry.

Ophthalmology is well suited for the integration of artificial intelligence (AI) owing to its reliance on various imaging modalities, such as anterior segment photography, fundus photography, and optical coherence tomography (OCT), which generate large volumes of high-resolution digital images. These images provide rich datasets for training AI algorithms, which enables precise diagnosis and monitoring of various ocular conditions. Retinal disease management heavily relies on image recognition. Limited access to ophthalmologists in underdeveloped areas and high image volumes in developed countries make AI a promising, cost-effective solution for screening and diagnosis. In corneal diseases, differential diagnosis is critical yet challenging because of the wide range of potential etiologies. AI and diagnostic technologies offer promise for improving the accuracy and speed of these diagnoses, including the differentiation between infectious and noninfectious conditions. Smartphone imaging coupled with AI technology can advance the diagnosis of anterior segment diseases, democratizing access to eye care and providing rapid and reliable diagnostic results. Other potential areas for AI applications include cataract and vitreous surgeries as well as the use of generative AI in training ophthalmologists. While AI offers substantial benefits, challenges remain, including the need for high-quality images, accurate manual annotations, patient heterogeneity considerations, and the "black-box phenomenon". Addressing these issues is crucial for enhancing the effectiveness of AI and ensuring its successful integration into clinical practice. AI is poised to transform ophthalmology by increasing diagnostic accuracy, optimizing treatment strategies, and improving patient care, particularly in high-risk or underserved populations.