-
Applied Ergonomics Sep 2024Motion sickness (MS) poses challenges for individuals affected, hindering their activities and travel. This study investigates the effect of a visual dynamic device,... (Review)
Review
Motion sickness (MS) poses challenges for individuals affected, hindering their activities and travel. This study investigates the effect of a visual dynamic device, forming an artificial horizon plane, on symptoms and physiological changes induced by MS. This device consists of vertical light-emitting diodes whose illumination varies according to the boat's movements. Fifteen subjects with moderate-to-severe MS susceptibility were exposed to a seasickness simulator with and without the device. Symptoms were assessed immediately after exposure. Time spent in the simulator, heart rate, and temperature were also recorded. Symptom intensity at the end of the experience did not differ, but the time spent in the simulator was significantly longer with the device (+46%). Variations in heart rate were also observed. The device delays symptom onset and can be used as a tool against MS. Further research is needed to evaluate its effects, for example, during more prolonged exposure to MS-inducing stimuli.
Topics: Humans; Motion Sickness; Male; Adult; Heart Rate; Female; Feedback, Sensory; Young Adult; Body Temperature; Ships; Middle Aged; Time Factors
PubMed: 38797015
DOI: 10.1016/j.apergo.2024.104318 -
Journal of the American Heart... Jun 2024
-
Life (Basel, Switzerland) Jan 2024The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive... (Review)
Review
BACKGROUND
The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive thickness of the maternal abdominal wall, or the presence of post-surgical scars on the maternal abdominal wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes of fetal ultrasound evaluation, and for disease diagnosis, which helps conventional imaging methods. The usage of information, ultrasound scan images, and a machine learning program create an algorithm capable of assisting healthcare providers by reducing the workload, reducing the duration of the examination, and increasing the correct diagnosis capability. The recent remarkable expansion in the use of electronic medical records and diagnostic imaging coincides with the enormous success of machine learning algorithms in image identification tasks.
OBJECTIVES
We aim to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.
PubMed: 38398675
DOI: 10.3390/life14020166 -
CJC Pediatric and Congenital Heart... Dec 2023Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with... (Review)
Review
Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues. To address these challenges, the integration of artificial intelligence and machine learning holds great promise as they have the potential to revolutionize patient management and positively impact lifelong outcomes for individuals with TOF. Innovative applications of artificial intelligence and machine learning have spanned across multiple domains of TOF care, including screening and diagnosis, automated image processing and interpretation, clinical risk stratification, and planning and performing cardiac interventions. By embracing these advancements and incorporating them into routine clinical practice, personalized medicine could be delivered, leading to the best possible outcomes for patients. In this review, we provide an overview of these evolving applications and emphasize the challenges, limitations, and future potential for integrating them into clinical care.
PubMed: 38161675
DOI: 10.1016/j.cjcpc.2023.08.005 -
Journal of Electrocardiology 2024Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular... (Review)
Review
Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.
Topics: Humans; Cardiovascular Diseases; Electrocardiography; Artificial Intelligence; Heart; Heart Failure
PubMed: 38301492
DOI: 10.1016/j.jelectrocard.2024.01.006 -
Nature Communications Nov 2023Direct modulation of cardiac myosin function has emerged as a therapeutic target for both heart disease and heart failure. However, the development of myosin-based...
Direct modulation of cardiac myosin function has emerged as a therapeutic target for both heart disease and heart failure. However, the development of myosin-based therapeutics has been hampered by the lack of targeted in vitro screening assays. In this study we use Artificial Intelligence-based virtual high throughput screening (vHTS) to identify novel small molecule effectors of human β-cardiac myosin. We test the top scoring compounds from vHTS in biochemical counter-screens and identify a novel chemical scaffold called 'F10' as a cardiac-specific low-micromolar myosin inhibitor. Biochemical and biophysical characterization in both isolated proteins and muscle fibers show that F10 stabilizes both the biochemical (i.e. super-relaxed state) and structural (i.e. interacting heads motif) OFF state of cardiac myosin, and reduces force and left ventricular pressure development in isolated myofilaments and Langendorff-perfused hearts, respectively. F10 is a tunable scaffold for the further development of a novel class of myosin modulators.
Topics: Humans; Cardiac Myosins; Artificial Intelligence; Myosins; Muscle Fibers, Skeletal; Heart Failure
PubMed: 38001148
DOI: 10.1038/s41467-023-43538-y -
Scientific Reports Nov 2023Mechanical ventilation weaning within intensive care units (ICU) is a difficult process, while crucial when considering its impact on morbidity and mortality. Failed...
Mechanical ventilation weaning within intensive care units (ICU) is a difficult process, while crucial when considering its impact on morbidity and mortality. Failed extubation and prolonged mechanical ventilation both carry a significant risk of adverse events. We aimed to determine predictive factors of extubation success using data-mining and artificial intelligence. A prospective physiological and biomedical signal data warehousing project. A 21-beds medical Intensive Care Unit of a University Hospital. Adult patients undergoing weaning from mechanical ventilation. Hemodynamic and respiratory parameters of mechanically ventilated patients were prospectively collected and combined with clinical outcome data. One hundred and eight patients were included, for 135 spontaneous breathing trials (SBT) allowing to identify physiological parameters either measured before or during the trial and considered as predictive for extubation success. The Early-Warning Score Oxygen (EWSO) enables to discriminate patients deemed to succeed extubation, at 72-h and 7-days. Cut-off values for EWSO2 (AUC = 0.80; Se = 0.75; Sp = 0.76), mean arterial pressure and heart-rate variability parameters were determined. A predictive model for extubation success was established including body-mass index (BMI) on inclusion, occlusion pressure at 0,1 s. (P0.1) and heart-rate analysis parameters (LF/HF) both measured before SBT, and heart rate during SBT (global performance 62%; 83%). The data-mining process enabled to detect independent predictive factors for extubation success and to develop a dynamic predictive model using artificial intelligence. Such predictive tools may help clinicians to better discriminate patients deemed to succeed extubation and thus improve clinical performance.
Topics: Adult; Humans; Ventilator Weaning; Respiration, Artificial; Artificial Intelligence; Prospective Studies; Intensive Care Units
PubMed: 37993526
DOI: 10.1038/s41598-023-47452-7 -
Journal of the American Heart... Dec 2023Acute myocardial infarction complicated by cardiogenic shock (AMI-CS) is associated with significant morbidity and mortality. Mechanical circulatory support (MCS)...
BACKGROUND
Acute myocardial infarction complicated by cardiogenic shock (AMI-CS) is associated with significant morbidity and mortality. Mechanical circulatory support (MCS) devices increase systemic blood pressure and end organ perfusion while reducing cardiac filling pressures.
METHODS AND RESULTS
The National Cardiogenic Shock Initiative (NCT03677180) is a single-arm, multicenter study. The purpose of this study was to assess the feasibility and effectiveness of utilizing early MCS with Impella in patients presenting with AMI-CS. The primary end point was in-hospital mortality. A total of 406 patients were enrolled at 80 sites between 2016 and 2020. Average age was 64±12 years, 24% were female, 17% had a witnessed out-of-hospital cardiac arrest, 27% had in-hospital cardiac arrest, and 9% were under active cardiopulmonary resuscitation during MCS implantation. Patients presented with a mean systolic blood pressure of 77.2±19.2 mm Hg, 85% of patients were on vasopressors or inotropes, mean lactate was 4.8±3.9 mmol/L and cardiac power output was 0.67±0.29 watts. At 24 hours, mean systolic blood pressure improved to 103.9±17.8 mm Hg, lactate to 2.7±2.8 mmol/L, and cardiac power output to 1.0±1.3 watts. Procedural survival, survival to discharge, survival to 30 days, and survival to 1 year were 99%, 71%, 68%, and 53%, respectively.
CONCLUSIONS
Early use of MCS in AMI-CS is feasible across varying health care settings and resulted in improvements to early hemodynamics and perfusion. Survival rates to hospital discharge were high. Given the encouraging results from our analysis, randomized clinical trials are warranted to assess the role of utilizing early MCS, using a standardized, multidisciplinary approach.
Topics: Aged; Female; Humans; Male; Middle Aged; Heart-Assist Devices; Lactic Acid; Myocardial Infarction; Shock, Cardiogenic; Treatment Outcome
PubMed: 38014676
DOI: 10.1161/JAHA.123.031401 -
Journal of the American College of... Aug 2023In advanced heart failure patients implanted with a fully magnetically levitated HeartMate 3 (HM3, Abbott) left ventricular assist device (LVAD), it is unknown how... (Clinical Trial)
Clinical Trial
BACKGROUND
In advanced heart failure patients implanted with a fully magnetically levitated HeartMate 3 (HM3, Abbott) left ventricular assist device (LVAD), it is unknown how preimplant factors and postimplant index hospitalization events influence 5-year mortality in those able to be discharged.
OBJECTIVES
The goal was to identify risk predictors of mortality through 5 years among HM3 LVAD recipients conditional on discharge from index hospitalization in the MOMENTUM 3 pivotal trial.
METHODS
This analysis evaluated 485 of 515 (94%) patients discharged after implantation of the HM3 LVAD. Preimplant (baseline), implant surgery, and index hospitalization characteristics were analyzed individually, and as multivariable predictors for mortality risk through 5 years.
RESULTS
Cumulative 5-year mortality in the cohort (median age: 62 years, 80% male, 65% White, 61% destination therapy due to transplant ineligibility) was 38%. Two preimplant characteristics (elevated blood urea nitrogen and prior coronary artery bypass graft or valve procedure) and 3 postimplant characteristics (hemocompatibility-related adverse events, ventricular arrhythmias, and estimated glomerular filtration rate <60 mL/min/1.73 m at discharge) were predictors of 5-year mortality. In 171 of 485 patients (35.3%) without any risk predictors, 5-year mortality was reduced to 22.6% (95% CI: 15.4%-32.7%). Even among those with 1 or more predictors, mortality was <50% at 5 years (45.7% [95% CI: 39.0%-52.8%]).
CONCLUSIONS
Long-term survival in successfully discharged HM3 LVAD recipients is largely influenced by clinical events experienced during the index surgical hospitalization in tandem with baseline factors, with mortality of <50% at 5 years. In patients without identified predictors of risk, long-term 5-year mortality is low and rivals that achieved with heart transplantation, even though most were implanted with destination therapy intent. (MOMENTUM 3 IDE Clinical Study Protocol, NCT02224755; MOMENTUM 3 Pivotal Cohort Extended Follow-up PAS, NCT03982979).
Topics: Female; Humans; Male; Middle Aged; Coronary Artery Bypass; Heart Failure; Heart-Assist Devices; Hospitalization; Patient Discharge
PubMed: 37612008
DOI: 10.1016/j.jacc.2023.05.066 -
Cardiology Journal 2024Since the arrival of leadless pacemakers (LPs), they have become a cornerstone in remedial treatment of bradycardia and atrioventricular (AV) conduction disorders, as an... (Review)
Review
Since the arrival of leadless pacemakers (LPs), they have become a cornerstone in remedial treatment of bradycardia and atrioventricular (AV) conduction disorders, as an alternative to transvenous pacemakers. Even though clinical trials and case reports show indisputable benefits of LP therapy, they also bring some doubts. Together with the positive results of the MARVEL trials, AV synchronization has become widely available in LPs, presenting a significant development in leadless technology. This review presents the Micra AV (MAV), describes major clinical trials, and introduces the basics of AV synchronicity obtained with the MAV and its unique programming options.
Topics: Humans; Lipopolysaccharides; Equipment Design; Pacemaker, Artificial; Bradycardia; Cardiac Pacing, Artificial
PubMed: 37246458
DOI: 10.5603/CJ.a2023.0035