Artificial Intelligence in Cardiac Imaging

Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders

Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology

Surgery patients’ perception about anesthesia and role of anesthesiologist: surgery patients' perception about anesthesia and role of anesthesiologist

Introduction: Anesthesiologists play an important role in management of perioperative risk and perception of patient towards anesthesia and role of anesthesiologist is often underestimated. Newer initiatives are required to increase communication with patients and educating them about the importance of anesthesia during surgery. The study aims to assess perception in relation to education level and patients’ previous exposure to anesthesia about anesthesia and role of anesthesiologist. Methods: A cross sectional, predesigned questionnaire based study was conducted after approval from Ethical Committee. A total of 386 above 14 years old hospital admitted patients undergoing elective surgery in different departments of Patan Hospital were included. Data was collected during the pre-anesthetic evaluation one day before surgery. Perception of patients in relation to educational level and previous exposure to surgery and chi-square test was done at p<0.05 statistically significant.  Results: Among 386 participants, 63.4% participants had no previous anesthetic exposure and showed significant difference on perception about the meaning of anaesthesia and administration of anesthetic medicine among patient with previous exposure of anaesthesia and without. 9.8% were uneducated; awareness about anesthesia and different techniques of anesthesia was poor. 50% participants knew that anesthetic medicine is given by anesthesiologist and 33.9% knew that anaesthesiologist were responsible for controlling vital signs during surgery. Study revealed poor knowledge regarding the role of anesthesiologist and showed statistically significant difference in educational level of patient. Conclusion: Perception on anesthesia and role of anaesthesiologist was less in relation to educational level and previous exposure to anesthetic among study participants. &nbsp

Dexmedetomidine as an adjunct to bupivacaine and xylocaine with adrenaline in ultrasound guided supraclavicular brachial plexus block in upper limb surgeries: Dexmedetomidine added to supraclavicular block

Introduction: Supraclavicular brachial plexus blocks are widely used for perioperative anesthesia and analgesia. Dexmedetomidine is a highly selective alpha-2 receptor agonist that provides analgesia, sedation, and anxiolysis. Our study aims to evaluate the effect of the addition of dexmedetomidine with bupivacaine and xylocaine with adrenaline in supraclavicular block in upper limb surgeries. Method: This was a comparative study conducted at Patan Hospital, Nepal among 44 patients randomly assigned in Group-I (N=22, bupivacaine and xylocaine with adrenaline 28 ml + dexmedetomidine 2 ml 1 mcg/kg), and Group-II (N=22, without dexmedetomidine) for ultrasound-guided supraclavicular block for upper limb surgeries. The study was approved by the institutional review committee. Onset of sensory and motor block, duration of analgesia, demographics, hemodynamic parameters, and side effects of drugs were compared. The Pin-prick test and the modified Bromage scale were used to evaluate sensory and motor blockades and the visual analogue scale for the severity of pain. Statistical analysis was performed with SPSS v16. Result: The median time for the onsets of sensory and motor blocks was significantly shorter in GI (1 m and 3 m) than in GII (5 and 10 m). The duration of analgesia was longer in group I (720 m) than in group II (360 m). Two patients had bradycardia and one had hypertension in the dexmedetomidine group, which were managed successfully. Conclusion: Dexmedetomidine added to local anesthetics significantly prolongs the effect of supraclavicular block in upper limb surgeries. Keywords: Bupivacaine, dexmedetomidine, supraclavicular block, ultrasound guided, upper limb surgery, xylocaine with adrenalin

Leveraging Synthetic Data to Advance Organizational Science

The importance of data sharing in organizational science is well-acknowledged, yet the field faces hurdles that prevent this, including concerns around privacy, proprietary information, and data integrity. We propose that synthetic data generated using machine learning (ML) could offer one promising solution to surmount at least some of these hurdles. Although this technology has been widely researched in the field of computer science, most organizational scientists are not familiar with it. To address the lack of available information for organizational scientists, we propose a systematic framework for the generation and evaluation of synthetic data. This framework is designed to guide researchers and practitioners through the intricacies of applying ML technologies to create robust, privacy-preserving synthetic data. Additionally, we present two empirical demonstrations using the ML method of Generative Adversarial Networks (GANs) to illustrate the practical application and potential of synthetic data in organizational science. Through this exploration, we aim to furnish the community with a foundational understanding of synthetic data generation and encourage further investigation and adoption of these methodologies. By doing so, we hope to foster scientific advancement by enhancing data-sharing initiatives within the field

A machine-learning framework to identify distinct phenotypes of aortic stenosis severity

OBJECTIVES : The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity. BACKGROUND : In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty. METHODS : Using echocardiography (ECHO) measurements (ECHO cohort, n ¼ 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT cohort, n ¼ 752) and cardiovascular magnetic resonance (CMR) imaging (CMR cohort, n ¼ 160). The classifier’s prognostic value was further validated using clinical outcomes (aortic valve replacement [AVR] and death) observed in the ECHO and CMR cohorts. RESULTS : In 1,964 patients from the 3 multi-institutional cohorts, 1,346 (68%) subjects had either nonsevere or discordant AS severity. Machine learning identified 1,117 (57%) patients as having high-severity and 847 (43%) as having low-severity AS. High-severity patients in CT and CMR cohorts had higher valve calcium scores and left ventricular mass and fibrosis, respectively than the low-severity group. In the ECHO cohort, progression to AVR and progression to death in patients who did not receive AVR was faster in the high-severity group. Compared with the conventional classification of disease severity, machine-learning–based severity classification improved discrimination (integrated discrimination improvement: 0.07; 95% confidence interval: 0.02 to 0.12) and reclassification (net reclassification improvement: 0.17; 95% confidence interval: 0.11 to 0.23) for the outcome of AVR at 5 years. For both ECHO and CMR cohorts, we observed prognostic value of the machine-learning classifications for subgroups with asymptomatic, nonsevere or discordant AS. CONCLUSIONS : Machine learning can integrate ECHO measurements to augment the classification of disease severity in most patients with AS, with major potential to optimize the timing of AVR

Proposed Requirements for Cardiovascular Imaging Related Machine Learning Evaluation (PRIME) Checklist

International audienc

Proposed Requirements for Cardiovascular Imaging Related Machine Learning Evaluation (PRIME) Checklist

status: accepte