Ignite Creation Date:
2024-06-16 @ 11:50 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06444425
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2024-06-14
First Post:
2024-05-31
Brief Title:
Artificial Intelligence in Detecting Cardiac Function
Sponsor:
The Fourth Affiliated Hospital of Zhejiang University School of Medicine
Organization:
The Fourth Affiliated Hospital of Zhejiang University School of Medicine
Study Overview
Official Title:
Korotkoff Sounds Dynamically Reflect Changes in Cardiac Function Based on Deep Learning Methods A Prospective Multicenter Study
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The Korotkoff SoundsKS which have been in use for over a century are widely regarded as the gold standard for measuring blood pressure Furthermore their potential extends beyond diagnosis and treatment of cardiovascular disease however research on the KS remains limited Given the increasing incidence of heart failure HF there is a pressing need for a rapid and convenient prehospital screening method In this study we propose employing deep learning DL techniques to explore the feasibility of utilizing KS methodology in predicting functional changes in cardiac ejection fraction LVEF as an indicator of cardiac dysfunction
Detailed Description:
Blood Pressure MeasurementAround 72 hours before and after the completion of the patientamp39s echocardiogram considering the variability in the patientamp39s blood pressure and ejection fraction at different times blood pressure should be measured in each participant at least twice a day up to a maximum of six times Each patient should be instructed to remain in a quiet state for 10 minutes before blood pressure measurement Blood pressure measurement should be conducted according to the following criteria the cuff used to measure blood pressure should be wrapped around the patientamp39s arm above the elbow joint positioned 2-3 cm above the level of the heart with a snugness that allows one finger to fit underneath Place the stethoscope head at the brachial artery pulse point on the left elbow joint then begin inflation Inflate continuously until the sound of the pulse beat disappears then inflate an additional 20 mmHg before stopping inflation Slowly deflate while listening-the first audible pulse beat is the systolic pressure and the disappearance of the pulse sound is the diastolic pressure Use the Hanhong POPULAR-3 electronic stethoscope to record the aforementioned process with each audio recording lasting 25 seconds uniformly
Data Analysis Overview
In terms of data analysis deep learning models are developed based on Torch version 150 utilizing Transformer network architecture to analyze the collected audio data
Network One Identifying the presence of cardiac functional abnormalities through Korotkoff sounds
Evaluation Metrics Receiver Operating Characteristic AUROC sensitivity specificity and F1 score harmonic mean of sensitivity and specificity to assess model performance on the test dataset
Network Two NYHA classification of Korotkoff sounds Evaluation Metrics Confusion matrix weighted accuracy multi-class ROC curve F1 score
Network Three Heart failure classification of Korotkoff sounds in heart failure patients
Evaluation Metrics Confusion matrix weighted accuracy multi-class ROC curve F1 score
Network Four Left Ventricular Ejection Fraction LVEF prediction from Korotkoff sounds
Evaluation Metrics Root Mean Squared Error RMSE Mean Absolute Error MAE Mean Squared Error MSE R2 Score
Data Analysis Overview
In terms of data analysis deep learning models are developed based on Torch version 150 utilizing Transformer network architecture to analyze the collected audio data
Network One Identifying the presence of cardiac functional abnormalities through Korotkoff sounds
Evaluation Metrics Receiver Operating Characteristic AUROC sensitivity specificity and F1 score harmonic mean of sensitivity and specificity to assess model performance on the test dataset
Network Two NYHA classification of Korotkoff sounds Evaluation Metrics Confusion matrix weighted accuracy multi-class ROC curve F1 score
Network Three Heart failure classification of Korotkoff sounds in heart failure patients
Evaluation Metrics Confusion matrix weighted accuracy multi-class ROC curve F1 score
Network Four Left Ventricular Ejection Fraction LVEF prediction from Korotkoff sounds
Evaluation Metrics Root Mean Squared Error RMSE Mean Absolute Error MAE Mean Squared Error MSE R2 Score
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None