Abstract:Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. In order to improve and optimize CVD outcomes, artificial intelligence techniques have the potential to radically change the way we practice cardiology, especially in imaging, offering us novel tools to interpret data and make clinical decisions. AI techniques such as machine learning and deep learning can also improve medical knowledge due to … Show more
“…The reported health records have been stored as massive datasets and interpreted with the help of AI. Additionally, AI guides physicians with the help of clinical data in order to make better clinical decisions [ 6 ]. An example is ‘Internet of Things’ (IoT), which has been a game changer in issues revolving around the cardiovascular healthcare system.…”
Section: Application Of Ai In Healthcare Systemmentioning
confidence: 99%
“…By utilizing multiple layers of compressed raw data, DL takes advantage of the multiple algorithms provided in order to produce the desired output [ 5 ]. With these AI mechanisms in mind, they could be applied to various industries, with one of the most important and relevant one being healthcare [ 6 ]. Fig.…”
“…The reported health records have been stored as massive datasets and interpreted with the help of AI. Additionally, AI guides physicians with the help of clinical data in order to make better clinical decisions [ 6 ]. An example is ‘Internet of Things’ (IoT), which has been a game changer in issues revolving around the cardiovascular healthcare system.…”
Section: Application Of Ai In Healthcare Systemmentioning
confidence: 99%
“…By utilizing multiple layers of compressed raw data, DL takes advantage of the multiple algorithms provided in order to produce the desired output [ 5 ]. With these AI mechanisms in mind, they could be applied to various industries, with one of the most important and relevant one being healthcare [ 6 ]. Fig.…”
“…The uptake of AI-based solutions is driven by their capacity to ingest and comprehend vast quantities of data, permitting a more comprehensive assessment of a patient's condition. Included in this is the ability to detect dynamic features that are not apparent in the typical snapshot evaluations that are performed in the clinic (Romiti et al, 2020; e.g., blood pressure and heart rate at a single point in time). We leverage these capabilities of AI systems to dynamically detect and correct pathological cardiovascular events in vivo ( Fig.…”
Section: Ai Enabled Medicines: Opportunities and Challengesmentioning
Myocardial ischemia is spontaneous, usually asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, biological neural networks cannot reliably detect and correct myocardial ischemia on their own. Supplementing biological neural networks may enable reliable detection, and potentially even facilitate correction, of myocardial ischemia. In this study, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements biological neural networks enabling reliable detection and correction of myocardial ischemia (preclinical experiments in rats). This responsive bioelectronic medicine uses an ANN with a long short-term memory layer to decode spontaneous myocardial ischemia and autonomously trigger vagus nerve stimulation (VNS) for reducing chronotropy, afterload, and myocardial oxygen demand. We first used injections of cardiovascular stress inducing agents (dobutamine and norepinephrine) that produce a model of spontaneous myocardial ischemia. Next, ANNs were trained to decode spontaneous cardiovascular stress and myocardial ischemia, with an overall accuracy of ~92%. ANN-controlled VNS reversed the major biomarkers of myocardial ischemia with no side effects. In contrast, open-loop VNS or ANN controlled VNS following a caudal vagotomy essentially failed to reverse correlates of myocardial ischemia. Lastly, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations of stress pathophysiology and unsupervised detection of new emerging stress states. Together, this adaptive architecture provides clinically relevant insights as pathophysiology evolves. Overall, these results provide a first-time demonstration that ANNs can supplement deficient biological neural networks to dynamically detect and help bioelectronically reverse cardiovascular pathophysiology.
“…In this context, we believe that some potential roles of analysis using this AI system include confirmation and support of the data analysis using traditional statistical software [1,8,9]. In addition, AI may prevent us from overlooking important factors [9].…”
mentioning
confidence: 95%
“…In this context, we believe that some potential roles of analysis using this AI system include confirmation and support of the data analysis using traditional statistical software [1,8,9]. In addition, AI may prevent us from overlooking important factors [9]. At present, however, it is unknown whether AI analysis will become the primary method for medical study and deci-sion-making in the healthcare system beyond the traditional analytical methods in the near future.…”
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