scholarly journals Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jia Zhou ◽  
Meng Du ◽  
Shuai Chang ◽  
Zhiyi Chen
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Diseases ◽  
Clinical Diagnosis ◽  
Future Development ◽  
Disease Diagnosis ◽  
Beating Heart ◽  
Accurate Diagnosis ◽  
Diagnosis And Treatment ◽  
Functional Evaluation ◽  
Current Application

AbstractUltrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiography poses a challenge for sonographers to diagnose accurately. However, artificial intelligence for detection, functional evaluation, and disease diagnosis has gradually become an alternative for accurate diagnosis and treatment using echocardiography. This work discusses the current application of artificial intelligence in echocardiography technology, its limitations, and future development directions.

scholarly journals Clinical Manifestations of Ultrasonic Virtual Reality in the Diagnosis and Treatment of Cardiovascular Diseases

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hairong Chen ◽  
Qiuguo Zou ◽  
Qi Wang
Keyword(s):  
Cardiovascular Disease ◽  
Virtual Reality ◽  
Cardiovascular Diseases ◽  
Diagnostic Accuracy ◽  
Research Methods ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Control Group ◽  
Accuracy Rate ◽  
Virtual Reality Technology

On a global scale, cardiovascular disease has become one of the most serious diseases that endangers human health and causes death and seriously threatens human life and health. If we can make accurate, timely, and effective judgments on cardiovascular-related parameters and take corresponding effective measures, the incidence of cardiovascular diseases can be reduced to a large extent. Based on this, this paper proposes the clinical application research of ultrasound virtual reality technology in the diagnosis and treatment of cardiovascular diseases. This article uses literature methods, experimental research methods, mathematical statistical analysis methods, and other research methods and in-depth study of virtual reality technology, cardiovascular disease, and other theoretical knowledge and briefly introduces ultrasound image denoising algorithms, such as bilateral filtering and PM model. And on this basis, it establishes clinical trials of ultrasound virtual reality technology in the diagnosis and treatment of cardiovascular diseases. This article mainly analyzes the application of virtual reality technology, technology comparison, and the experimental results carried out in this article. From the survey results, the total prevalence of hypertension was 25.1%, and the prevalence of males and females was 25.9% and 24.4%, respectively; the diagnostic accuracy rate of the experimental group reached 85.39%, while the diagnostic accuracy rate of the control group was 76.8%. It shows that the use of ultrasound virtual reality technology for disease diagnosis can effectively improve the accuracy of cardiovascular disease diagnosis and reduce the proportion of misdiagnosis and missed detection.

Deceptive Giant Dedifferentiated Liposarcoma of the Esophagus: An Extremely Rare Surgical Case

2019 ◽  
Vol 28 (2) ◽  
pp. 200-205
Author(s):  
Xiao-Xin Shi ◽  
Meng-Ying Liao ◽  
Li-Li Tao ◽  
Xin-Gen Wang ◽  
Wei-Hua Yin
Keyword(s):  
Clinical Diagnosis ◽  
Poor Prognosis ◽  
Clinical Manifestations ◽  
Accurate Diagnosis ◽  
Diagnosis And Treatment ◽  
Dedifferentiated Liposarcoma ◽  
Surgical Case ◽  
Pathologic Features

Dedifferentiated liposarcoma rarely occurs in the esophagus. It always has atypical clinical manifestations and different pathologic features, which usually lead to misdiagnosis and mistreatment. Given its poor prognosis, early and accurate diagnosis is of the utmost importance. The accumulation of similar cases is critical for surgeons and pathologists to raise awareness of such tumors. This report aims to discuss the diagnosis and provide a reference for the clinical diagnosis and treatment for pathologists and clinicians.

scholarly journals Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1438
Author(s):  
Biljana Stankovic ◽  
Nikola Kotur ◽  
Gordana Nikcevic ◽  
Vladimir Gasic ◽  
Branka Zukic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Disease Diagnosis ◽  
Omics Data ◽  
Current Application ◽  
Inflammatory Bowel ◽  
Future Outcomes ◽  
Clinical Records

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

scholarly journals Nanomaterials as Promising Theranostic Tools in Nanomedicine and Their Applications in Clinical Disease Diagnosis and Treatment

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3346
Author(s):  
Wei Zhu ◽  
Zhanqi Wei ◽  
Chang Han ◽  
Xisheng Weng
Keyword(s):  
Cardiovascular Diseases ◽  
Polymeric Nanoparticles ◽  
Human Life ◽  
Rapid Development ◽  
Tumor Therapy ◽  
Chemical Properties ◽  
Surface Characteristics ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Low Sensitivity

In recent decades, with the rapid development of nanotechnology, nanomaterials have been widely used in the medical field, showing great potential due to their unique physical and chemical properties including minimal size and functionalized surface characteristics. Nanomaterials such as metal nanoparticles and polymeric nanoparticles have been extensively studied in the diagnosis and treatment of diseases that seriously threaten human life and health, and are regarded to significantly improve the disadvantages of traditional diagnosis and treatment platforms, such as poor effectiveness, low sensitivity, weak security and low economy. In this review, we report and discuss the development and application of nanomaterials in the diagnosis and treatment of diseases based mainly on published research in the last five years. We first briefly introduce the improvement of several nanomaterials in imaging diagnosis and genomic sequencing. We then focus on the application of nanomaterials in the treatment of diseases, and select three diseases that people are most concerned about and that do the most harm: tumor, COVID-19 and cardiovascular diseases. First, we introduce the characteristics of nanoparticles according to the excellent effect of nanoparticles as delivery carriers of anti-tumor drugs. We then review the application of various nanoparticles in tumor therapy according to the classification of nanoparticles, and emphasize the importance of functionalization of nanomaterials. Second, COVID-19 has been the hottest issue in the health field in the past two years, and nanomaterials have also appeared in the relevant treatment. We enumerate the application of nanomaterials in various stages of viral pathogenesis according to the molecular mechanism of the complete pathway of viral infection, pathogenesis and transmission, and predict the application prospect of nanomaterials in the treatment of COVID-19. Third, aiming at the most important causes of human death, we focus on atherosclerosis, aneurysms and myocardial infarction, three of the most common and most harmful cardiovascular diseases, and prove that nanomaterials could be involved in a variety of therapeutic approaches and significantly improve the therapeutic effect in cardiovascular diseases. Therefore, we believe nanotechnology will become more widely involved in the diagnosis and treatment of diseases in the future, potentially helping to overcome bottlenecks under existing medical methods.

scholarly journals Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

2021 ◽  
Vol 12 ◽  
Author(s):  
Shaohui Wang ◽  
Ya Hou ◽  
Xuanhao Li ◽  
Xianli Meng ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Artificial Intelligence ◽  
Cloud Computing ◽  
Deep Learning ◽  
Traditional Medicine ◽  
Western Medicine ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Unknown Etiology ◽  
Drug Mechanism

Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

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