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Gels (Basel, Switzerland) Dec 2023The inherent disadvantages of traditional non-flexible aerogels, such as high fragility and moisture sensitivity, severely restrict their applications. To address these... (Review)
Review
The inherent disadvantages of traditional non-flexible aerogels, such as high fragility and moisture sensitivity, severely restrict their applications. To address these issues and make the aerogels efficient, especially for advanced medical applications, different techniques have been used to incorporate flexibility in aerogel materials. In recent years, a great boom in flexible aerogels has been observed, which has enabled them to be used in high-tech biomedical applications. The current study comprises a comprehensive review of the preparation techniques of pure polymeric-based hybrid and single-component aerogels and their use in biomedical applications. The biomedical applications of these hybrid aerogels will also be reviewed and discussed, where the flexible polymeric components in the aerogels provide the main contribution. The combination of highly controlled porosity, large internal surfaces, flexibility, and the ability to conform into 3D interconnected structures support versatile properties, which are required for numerous potential medical applications such as tissue engineering; drug delivery reservoir systems; biomedical implants like heart stents, pacemakers, and artificial heart valves; disease diagnosis; and the development of antibacterial materials. The present review also explores the different mechanical, chemical, and physical properties in numerical values, which are most wanted for the fabrication of different materials used in the biomedical fields.
PubMed: 38275842
DOI: 10.3390/gels10010004 -
Journal of the American Society of... Jul 2023Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is...
BACKGROUND
Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment.
METHODS
Two-dimensional and Doppler echocardiographic images from patients with normal aortic valves and all degrees of AS were analyzed by an artificial neural network (Us2.ai) with no human input to measure key variables in AS assessment. Trained echocardiographers blinded to AI data performed manual measurements of these variables, and correlation analyses were performed.
RESULTS
Our cohort included 256 patients with an average age of 67.6 ± 9.5 years. Across all AS severities, AI closely matched human measurement of aortic valve peak velocity (r = 0.97, P < .001), mean pressure gradient (r = 0.94, P < .001), aortic valve area by continuity equation (r = 0.88, P < .001), stroke volume index (r = 0.79, P < .001), left ventricular outflow tract velocity-time integral (r = 0.89, P < .001), aortic valve velocity-time integral (r = 0.96, P < .001), and left ventricular outflow tract diameter (r = 0.76, P < .001).
CONCLUSIONS
Artificial neural networks have the capacity to closely mimic human measurement of all relevant parameters in the adjudication of AS severity. Application of this AI technology may minimize interscan variability, improve interpretation and diagnosis of AS, and allow for precise and reproducible identification and management of patients with AS.
Topics: Humans; Middle Aged; Aged; Artificial Intelligence; Aortic Valve Stenosis; Echocardiography; Echocardiography, Doppler; Aortic Valve
PubMed: 36958708
DOI: 10.1016/j.echo.2023.03.008 -
Journal of Cardiology Apr 2024In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide.... (Review)
Review
In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.
Topics: Humans; Stethoscopes; Heart Sounds; Artificial Intelligence; Auscultation; Heart Diseases; Heart Auscultation
PubMed: 37734656
DOI: 10.1016/j.jjcc.2023.09.007 -
Life (Basel, Switzerland) Jan 2024In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work... (Review)
Review
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
PubMed: 38276274
DOI: 10.3390/life14010145 -
Journal of Clinical Medicine Dec 2023In recent years, many studies have analyzed the importance of integrating time, or aging, into the equation that relates genetics and the environment to the development...
In recent years, many studies have analyzed the importance of integrating time, or aging, into the equation that relates genetics and the environment to the development and origin of COPD. Under conditions of daily clinical practice, our study attempts to identify the differences in the clinical profile of patients with COPD according to age and the impact on the global burden of the disease. This study is non-interventional and observational, using artificial intelligence and data captured from electronic medical records. The study population included patients who were diagnosed with COPD between 2011 and 2021. A total of 73,901 patients had a diagnosis of COPD. The mean age was 73 years (95% CI: 72.9-73.1), and 56,763 were men (76.8%). We observed a specific prevalence of obesity, heart failure, depression, and hiatal hernia in women ( < 0.001), and ischemic heart disease and obstructive sleep apnea (OSA) in men ( < 0.001). In the analysis by age ranges, a progressive increase in cardiovascular risk factors was observed with age. In conclusion, in a real-life setting, COPD is a disease that primarily affects older subjects and frequently presents with comorbidities that are decisive in the evolutionary course of the disease.
PubMed: 38137664
DOI: 10.3390/jcm12247595 -
Journal of Cardiothoracic Surgery Feb 2024Artificial intelligence (AI) is a transformative technology with many benefits, but also risks when applied to healthcare and cardiac surgery in particular. Surgeons... (Review)
Review
Artificial intelligence (AI) is a transformative technology with many benefits, but also risks when applied to healthcare and cardiac surgery in particular. Surgeons must be aware of AI and its application through generative pre-trained transformers (GPT/ChatGPT) to fully understand what this offers to clinical care, decision making, training, research and education. Clinicians must appreciate that the advantages and potential for transformative change in practice is balanced by risks typified by validation, ethical challenges and medicolegal concerns. ChatGPT should be seen as a tool to support and enhance the skills of surgeons, rather than a replacement for their experience and judgment. Human oversight and intervention will always be necessary to ensure patient safety and to make complex decisions that may require a refined understanding of individual patient circumstances.
Topics: Humans; Artificial Intelligence; Cardiac Surgical Procedures; Heart Transplantation; Educational Status; Patient Safety
PubMed: 38409178
DOI: 10.1186/s13019-024-02541-0 -
The Journal of Heart and Lung... Jul 2024Hemodynamic derangements are defining features of cardiogenic shock. Randomized clinical trials have examined the efficacy of various therapeutic interventions, from... (Review)
Review
Hemodynamic derangements are defining features of cardiogenic shock. Randomized clinical trials have examined the efficacy of various therapeutic interventions, from percutaneous coronary intervention to inotropes and mechanical circulatory support (MCS). However, hemodynamic management in cardiogenic shock has not been well-studied. This State-of-the-Art review will provide a framework for hemodynamic management in cardiogenic shock, including a description of the 4 therapeutic phases from initial 'Rescue' to 'Optimization', 'Stabilization' and 'de-Escalation or Exit therapy' (R-O-S-E), phenotyping and phenotype-guided tailoring of pharmacological and MCS support, to achieve hemodynamic and therapeutic goals. Finally, the premises that form the basis for clinical management and the hypotheses for randomized controlled trials will be discussed, with a view to the future direction of cardiogenic shock.
Topics: Shock, Cardiogenic; Humans; Hemodynamics; Intensive Care Units; Heart-Assist Devices
PubMed: 38518863
DOI: 10.1016/j.healun.2024.03.009 -
Open Heart Nov 2023The advent of conversational artificial intelligence (AI) systems employing large language models such as ChatGPT has sparked public, professional and academic debates... (Review)
Review
OBJECTIVES
The advent of conversational artificial intelligence (AI) systems employing large language models such as ChatGPT has sparked public, professional and academic debates on the capabilities of such technologies. This mixed-methods study sets out to review and systematically explore the capabilities of ChatGPT to adequately provide health advice to patients when prompted regarding four topics from the field of cardiovascular diseases.
METHODS
As of 30 May 2023, 528 items on PubMed contained the term ChatGPT in their title and/or abstract, with 258 being classified as journal articles and included in our thematic state-of-the-art review. For the experimental part, we systematically developed and assessed 123 prompts across the four topics based on three classes of users and two languages. Medical and communications experts scored ChatGPT's responses according to the 4Cs of language model evaluation proposed in this article: correct, concise, comprehensive and comprehensible.
RESULTS
The articles reviewed were fairly evenly distributed across discussing how ChatGPT could be used for medical publishing, in clinical practice and for education of medical personnel and/or patients. Quantitatively and qualitatively assessing the capability of ChatGPT on the 123 prompts demonstrated that, while the responses generally received above-average scores, they occupy a spectrum from the concise and correct via the absurd to what only can be described as hazardously incorrect and incomplete. Prompts formulated at higher levels of health literacy generally yielded higher-quality answers. Counterintuitively, responses in a lower-resource language were often of higher quality.
CONCLUSIONS
The results emphasise the relationship between prompt and response quality and hint at potentially concerning futures in personalised medicine. The widespread use of large language models for health advice might amplify existing health inequalities and will increase the pressure on healthcare systems by providing easy access to many seemingly likely differential diagnoses and recommendations for seeing a doctor for even harmless ailments.
Topics: Humans; Artificial Intelligence; Cardiovascular Diseases; Heart; Patients; Referral and Consultation
PubMed: 37945282
DOI: 10.1136/openhrt-2023-002455 -
Cureus May 2024Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The... (Review)
Review
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
PubMed: 38836155
DOI: 10.7759/cureus.59661 -
Circulation Feb 2024Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved... (Review)
Review
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
Topics: Humans; Artificial Intelligence; American Heart Association; Machine Learning; Heart; Magnetic Resonance Imaging
PubMed: 38193315
DOI: 10.1161/CIR.0000000000001202