The Role of Deep Learning-Based Echocardiography in the Diagnosis and Evaluation of the Effects of Routine Anti-Heart-Failure Western Medicines in Elderly Patients with Acute Left Heart Failure

Objective. The role of deep learning-based echocardiography in the diagnosis and evaluation of the effects of routine anti-heart-failure Western medicines was investigated in elderly patients with acute left heart failure (ALHF). Methods. A total of 80 elderly patients with ALHF admitted to Affiliated Hangzhou First People’s Hospital from August 2017 to February 2019 were selected as the research objects, and they were divided randomly into a control group and an observation group, with 40 cases in each group. Then, a deep convolutional neural network (DCNN) algorithm model was established, and image preprocessing was carried out. The binarized threshold segmentation was used for denoising, and the image was for illumination processing to balance the overall brightness of the image and increase the usable data of the model, so as to reduce the interference of subsequent feature extraction. Finally, the detailed module of deep convolutional layer network algorithm was realized. Besides, the patients from the control group were given routine echocardiography, and the observation group underwent echocardiography based on deep learning algorithm. Moreover, the hospitalization status of patients from the two groups was observed and recorded, including mortality rate, rehospitalization rate, average length of hospitalization, and hospitalization expenses. The diagnostic accuracy of the two examination methods was compared, and the electrocardiogram (ECG) and echocardiographic parameters as well as patients’ quality of life were recorded in both groups at the basic state and 5 months after drug treatment. Results. After comparison, the rehospitalization rate and mortality rate of the observation group were lower than the rates of the control group, but the diagnostic accuracy was higher than that of the control group. However, the difference between the two groups of patients was not statistically marked ( P > 0.05 ). The length and expenses of hospitalization of the observation group were both less than those of the control group. The specificity, sensitivity, and accuracy of the examination methods in the observation group were higher than those of the control group, and the differences were statistically marked ( P < 0.05 ). There was a statistically great difference between the interventricular delay (IVD) of the echocardiographic parameters of patients from the two groups at the basic state and the left ventricular electromechanical delay (LVEMD) parameter values after 5 months of treatment ( P < 0.05 ), but there was no significant difference in the other parameters. After treatment, the quality of life of patients from the two groups was improved, while the observation group was more marked than the control group ( P < 0.05 ). Conclusion. Echocardiography based on deep learning algorithm had high diagnostic accuracy and could reduce the possibility of cardiovascular events in patients with heart failure, so as to decrease the mortality rate and diagnosis and treatment costs. Moreover, it had an obvious diagnostic effect, which was conducive to the timely detection and treatment of clinical diseases.

Download Full-text

Effects of Continuous Nursing Intervention on Exercise Tolerance and Rehospitalization Rate in Patients with Chronic Heart Failure

Tobacco Regulatory Science ◽

10.18001/trs.7.4.11 ◽

2021 ◽

Vol 7 (4) ◽

pp. 347-352

Author(s):

Xiao-Li Sun ◽

Zhao-Yun Shi ◽

Na Wang

Keyword(s):

Heart Failure ◽

Chronic Heart Failure ◽

Exercise Tolerance ◽

Nursing Intervention ◽

Walking Distance ◽

Control Group ◽

Observation Group ◽

Rehospitalization Rate ◽

Sf 36 ◽

Significant Difference

Objective To observe the effect of continuous nursing intervention on exercise tolerance and rehospitalization rate in patients with chronic heart failure. Methods 134 patients with chronic heart failure admitted to our hospital were divided into two groups, routine nursing intervention group (control group) and continuous nursing intervention group (observation group), with 67 cases in each group. The resting and peak heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) of the two groups were recorded. The change of 6min walking distance, modified European Heart Failure Self-Care Behavior Scale (EHFSCB-9) and quality of life (SF-36) of the two groups were compared before and after intervention, and moreover, rehospitalization rate of heart failure of two groups 6 months after discharge was compared between the two groups. Results: Before intervention, there was no significant difference between the two groups (P>0.05). After intervention, there was no significant difference in resting and peak HR, SBP and DBP between the two groups and those before intervention (P>0.05). Further comparison between the two groups showed that there was no significant difference in resting and peak HR, SBP and DBP between the observation group and the control group (P>0.05). After intervention, 6min walking distance and SF-36 scale scores (role physical, physiological function, physical pain, energy, health status, social function, mental health and emotional function) were increased in the two groups (P<0.05). Further comparison between the two groups showed that 6min walking distance and SF-36 scale scores (except somatic pain score and role physical score) in the observation group were higher than those in the control group (P<0.05), and the EHFSCB-9 scores in the two groups decreased gradually after intervention (P<0.05). Further comparison between the two groups showed that the EHFSCB-9 scores in the observation group (except low salt diet score and taking medicine based on doctor's advice score) were lower than those in the control group (P<0.05). The rehospitalization rate of heart failure within 6 months after discharge was 11.91% in the observation group, significantly lower than 25.37% in the control group, and the difference was significant (P<0.05). Conclusion: Continuous nursing intervention can strengthen the self-care ability of patients with chronic heart failure, improve exercise tolerance and quality of life, and reduce the rehospitalization rate to heart failure within 6 months.

Download Full-text

Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study (Preprint)

10.2196/preprints.16443 ◽

2019 ◽

Author(s):

Soonil Kwon ◽

Joonki Hong ◽

Eue-Keun Choi ◽

Byunghwan Lee ◽

Changhyun Baik ◽

...

Keyword(s):

Atrial Fibrillation ◽

Deep Learning ◽

Diagnostic Accuracy ◽

Predictive Value ◽

Learning Algorithm ◽

Wearable Device ◽

Control Group ◽

Ring Type ◽

Deep Learning Algorithm ◽

Learning Analysis

BACKGROUND Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). OBJECTIVE We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. METHODS Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). RESULTS In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. CONCLUSIONS A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. CLINICALTRIAL ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188

Download Full-text

Deep Learning Based Coronary Angiography in Diagnosis of Myocardial Ischemia

Scientific Programming ◽

10.1155/2021/8491976 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yiwen Shu ◽

Xiwen Wu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Coronary Angiography ◽

Myocardial Ischemia ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Control Group ◽

Learning Technology ◽

Observation Group ◽

Deep Learning Algorithm

Objective. This study was to explore the diagnostic effect of the coronary angiography (CAG) based on the fully convolutional neural network (FCNN) algorithm for patients with coronary heart disease (CHD) and suspected (not diagnosed) myocardial ischemia. Methods. In this study, 150 patients with undiagnosed CHD with myocardial ischemia in hospital were selected as the research objects. They were divided into an observation group and a control group by random number method. The patients in observation group were examined with CAG with the assistance of convolutional neural network (CNN) algorithm, while patients in the control group received conventional CAG. Results. The Dice coefficient of the segmentation effect evaluation index was 0.89, which showed that the image processing effect of the algorithm was good. There was no statistical difference in positive rates of single/double-vessel lesions between the two groups ( P > 0.05 ), and the positive rates of multivessel lesions and total lesions in the observation group were higher than those in the control group, showing statistically obvious difference ( P < 0.05 ). The examination sensitivity, specificity, accuracy, and Kappa value of the observation group were −90.9%, −60%, −82.7%, and −0.72, which were all higher in contrast to those of the control group. The proportion of positive myocardial ischemia and coronary artery stenosis (CAS) (82%) was higher than other cases (18%), and the comparison was statistically significant ( P < 0.05 ). Conclusion. CAG based on the deep learning algorithm showed a good detection effect and can better display the coronary lesions and reflect the good development prospects of deep learning technology in medical imaging.

Download Full-text

Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study

Journal of Medical Internet Research ◽

10.2196/16443 ◽

2020 ◽

Vol 22 (5) ◽

pp. e16443

Author(s):

Soonil Kwon ◽

Joonki Hong ◽

Eue-Keun Choi ◽

Byunghwan Lee ◽

Changhyun Baik ◽

...

Keyword(s):

Atrial Fibrillation ◽

Deep Learning ◽

Diagnostic Accuracy ◽

Predictive Value ◽

Learning Algorithm ◽

Wearable Device ◽

Control Group ◽

Ring Type ◽

Deep Learning Algorithm ◽

Learning Analysis

Background Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188

Download Full-text

Effects of Continuous Nursing Intervention on Exercise Tolerance and Rehospitalization Rate in Patients with Chronic Heart Failure

Tobacco Regulatory Science ◽

10.18001/trs.7.5.71 ◽

2021 ◽

Vol 7 (5) ◽

pp. 1509-1515

Author(s):

Xiao-li Sun ◽

Zhao-yun Shi ◽

Na Wang

Keyword(s):

Heart Failure ◽

Chronic Heart Failure ◽

Exercise Tolerance ◽

Nursing Intervention ◽

Walking Distance ◽

Control Group ◽

Observation Group ◽

Rehospitalization Rate ◽

Sf 36 ◽

Significant Difference

To observe the effect of continuous nursing intervention on exercise tolerance and rehospitalization rate in patients with chronic heart failure. Methods 134 patients with chronic heart failure admitted to our hospital were divided into two groups, routine nursing intervention group (control group) and continuous nursing intervention group (observation group), with 67 cases in each group. The resting and peak heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) of the two groups were recorded. The change of 6min walking distance, modified European Heart Failure Self-Care Behavior Scale (EHFSCB-9) and quality of life (SF-36) of the two groups were compared before and after intervention, and moreover, rehospitalization rate of heart failure of two groups 6 months after discharge was compared between the two groups. Results : Before intervention, there was no significant difference between the two groups (P>0.05). After intervention, there was no significant difference in resting and peak HR, SBP and DBP between the two groups and those before intervention (P>0.05). Further comparison between the two groups showed that there was no significant difference in resting and peak HR, SBP and DBP between the observation group and the control group (P>0.05). After intervention, 6min walking distance and SF-36 scale scores (role physical, physiological function, physical pain, energy, health status, social function, mental health and emotional function) were increased in the two groups (P<0.05). Further comparison between the two groups showed that 6min walking distance and SF-36 scale scores (except somatic pain score and role physical score) in the observation group were higher than those in the control group (P<0.05), and the EHFSCB-9 scores in the two groups decreased gradually after intervention (P<0.05). Further comparison between the two groups showed that the EHFSCB-9 scores in the observation group (except low salt diet score and taking medicine based on doctor’s advice score) were lower than those in the control group (P<0.05). The rehospitalization rate of heart failure within 6 months after discharge was 11.91% in the observation group, significantly lower than 25.37% in the control group, and the difference was significant (P<0.05). Conclusion : Continuous nursing intervention can strengthen the self-care ability of patients with chronic heart failure, improve exercise tolerance and quality of life, and reduce the rehospitalization rate to heart failure within 6 months.

Download Full-text

Value of Ultrasonic Image Features in Diagnosis of Perinatal Outcomes of Severe Preeclampsia on account of Deep Learning Algorithm

Computational and Mathematical Methods in Medicine ◽

10.1155/2022/4010339 ◽

2022 ◽

Vol 2022 ◽

pp. 1-10

Author(s):

Qiang Wang ◽

Dong Liu ◽

Guangheng Liu

Keyword(s):

Deep Learning ◽

Pregnant Women ◽

Uterine Artery ◽

Learning Algorithm ◽

Perinatal Outcomes ◽

Image Features ◽

Severe Preeclampsia ◽

Control Group ◽

Observation Group ◽

Deep Learning Algorithm

This study is aimed at discussing the value of ultrasonic image features in diagnosis of perinatal outcomes of severe preeclampsia on account of deep learning algorithm. 140 pregnant women singleton with severe preeclampsia were selected as the observation group. At the same time, 140 normal singleton pregnant women were selected as the control group. The hemodynamic indexes were detected by color Doppler ultrasound. The CNN algorithm was used to classify ultrasound images of two groups of pregnant women. The differential scanning calorimetry (DSC), mean pixel accuracy (MPA), and mean intersection of union (MIOU) values of CNN algorithm were 0.9410, 0.9228, and 0.8968, respectively. Accuracy, precision, recall, and F 1 -score were 93.44%, 95.13%, 95.09%, and 94.87%, respectively. The differences were statistically significant ( P < 0.05 ). Compared with the normal control group, the umbilical artery (UA), uterine artery-systolic/diastolic (UTA-S/D), uterine artery (UTA), and digital video (DV) of pregnant women in the observation group were remarkably increased; the minimum alveolar effective concentration (MCA) of the observation group was obviously lower than the MCA of the control group, and the differences between groups were statistically valid ( P < 0.05 ). Logistic regression analysis showed that UA-S/D, UA-resistance index (UA-RI), UTA-S/D, UTA-pulsatility index (UTA-PI), DV-peak velocity index for veins (DV-PVIV), and MCA-S/D were independent risk factors for the outcome of perinatal children with severe preeclampsia. In the perinatal management of severe epilepsy, the combination of the above blood flow indexes to select the appropriate delivery time had positive significance to improve the pregnancy outcome and reduce the perinatal mortality.

Download Full-text

Enalapril Combined with Hydrochlorothiazide and Indapamide in the Treatment of Hypertension with Heart Failure

Journal of Clinical and Nursing Research ◽

10.26689/jcnr.v4i4.1330 ◽

2020 ◽

Vol 4 (4) ◽

Author(s):

Xueting Sun

Keyword(s):

Heart Failure ◽

Blood Pressure ◽

Diastolic Blood Pressure ◽

Ventricular Remodeling ◽

Effective Rate ◽

Control Group ◽

Observation Group ◽

Research Subjects ◽

Treatment Regimens ◽

Treatment Of Hypertension

Objective: To explore the effect of enalapril combined with hydrochlorothiazide and indapamide on hypertension and heart failure. Methods: 80 patients with hypertension and heart failure admitted to our hospital from January 2019 to January 2020 were selected as the research subjects, and they were divided into two groups with random number table method, 40 cases each. The control group was given conventional treatment regimens, including enalapril and hydrochlorothiazide; the observation group replaced hydrochlorothiazide with indapamide based on the above therapies. The efficacy and systolic blood pressure, diastolic blood pressure and left heart ejection fraction (LVEF) of the two groups were compared. Results: After treatment, the effective rate of the observation group was 92.50% (37/40) higher than that of the control group 75.00% (30/40). The systolic and diastolic blood pressure were lower than those of the control group, and the LVEF was higher than that of the control group. The difference was statistically significant (P<0.05). Conclusion: Enalapril combined with indapamide is effective in the treatment of hypertension with heart failure, which can help lower blood pressure, reduce heart load, increase cardiac output, reverse ventricular remodeling, and delay disease progression.

Download Full-text

Insulin Glargine and Acarbose in the treatment of elderly patients with diabetes

Pakistan Journal of Medical Sciences ◽

10.12669/pjms.35.3.86 ◽

2019 ◽

Vol 35 (3) ◽

Author(s):

Jing Li ◽

Jinzhi Ji ◽

Fuyan Liu ◽

Lingling Wang

Keyword(s):

Quality Of Life ◽

Blood Glucose ◽

Elderly Patients ◽

Insulin Glargine ◽

Adverse Reactions ◽

Postprandial Blood Glucose ◽

Control Group ◽

Observation Group ◽

Patients With Diabetes

Objective: To investigate the clinical efficacy of insulin glargine combined with acarbose in the treatment of elderly patients with diabetes. Methods: One hundred and forty-four elderly patients with diabetes who received treatment between December 2016 and December 2017 in Binzhou People’s Hospital, China, were selected and divided into a control group and an observation group, 72 each, using random number table. The control group was treated with insulin glargine, while the observation group was treated with insulin glargine combined with acarbose. The therapeutic effect, improvement of quality of life and adverse reactions were compared between the two groups. Results: After treatment, fasting blood glucose (FBG), 2h postprandial blood glucose (PBG) and glycosylated hemoglobin (Hb Alc) of the two groups were lower than those before treatment, and the decrease degree of the observation group was significantly larger than that of the control group (P<0.05). The time needed for blood glucose reaching the standard level and daily insulin dosage of the observation group were significantly lower than that of the control group, and the differences were statistically significant (P<0.05). SF-36 scale score of the observation group was significantly better than the control group, and the difference was statistically significant (P<0.05). There was no significant difference in the incidence of adverse reactions between the two groups (P>0.05). Conclusion: The combination of insulin Glargine and Acarbose can significantly control the blood glucose level of elderly patients with diabetes, improve the biochemical indicators, and enhance the quality of life. It is worth promotion in clinical practice. doi: https://doi.org/10.12669/pjms.35.3.86 How to cite this:Li J, Ji J, Liu F, Wang L. Insulin Glargine and Acarbose in the treatment of elderly patients with diabetes. Pak J Med Sci. 2019;35(3):---------. doi: https://doi.org/10.12669/pjms.35.3.86 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download Full-text

Clinical Treatment of Elderly Patients with Chronic Atrial Fibrillation and Heart Failure

Advanced Emergency Medicine ◽

10.18686/aem.v4i1.3 ◽

2015 ◽

Vol 4 (1) ◽

pp. 8

Author(s):

Haifeng Liu

Keyword(s):

Heart Failure ◽

Atrial Fibrillation ◽

Treatment Group ◽

Elderly Patients ◽

Recovery Time ◽

Chronic Atrial Fibrillation ◽

Clinical Treatment ◽

Neurosecretory System ◽

Control Group ◽

Average Recovery

Objective: To study the clinical treatment of chronic atrial fibrillation and heart failure in elderly patients. Method: In our hospital, 120 patients with chronic atrial fibrillation complicated with heart failure were selected from March 2011 to March 2014 as the study subject. The clinical treatment of chronic atrial fibrillation with heart failure was discussed by comparing with the control group and the treatment group. Results: After 1 months of treatment, the total effective rate was 90% in the treatment group and 70% in the control group, the average recovery time of the treatment group was (4.45 + 0.88) day, and the average recovery time of the control group was (7.76 + 1.34) day. Conclusion: To improve cardiac function and ventricular remodeling, heart rate control, blocking neurosecretory system in the treatment for elderly patients with chronic atrial fibrillation and heart failure patients affect significantly, has very important clinical value.

Download Full-text

Diagnostic Performance of Deep LearnIng AlgoriThm for Analysis of Computed Tomography Myocardial PerfusION

10.21203/rs.3.rs-1037278/v1 ◽

2021 ◽

Author(s):

Giuseppe Muscogiuri ◽

Mattia Chiesa ◽

Andrea Baggiano ◽

Pierino Spadafora ◽

Rossella De Santis ◽

...

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Diagnostic Accuracy ◽

Learning Algorithm ◽

Significant Coronary Artery Disease ◽

Patient Specific ◽

Fractional Flow ◽

Flow Reserve ◽

Specific Sensitivity ◽

Sensitivity Specificity

Abstract Purpose: Artificial intelligence could play a key role in cardiac imaging analysis. To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation. Methods: One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30% and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA+CTPStress, CCTA+CTP-DLrest, and CCTA+CTP-DLstress were measured and compared. The time of analysis for CTPStress, CTP-DLrest and CTP-DLStress were recorded. Results: Patient-specific sensitivity, specificity, NPV, PPV, accuracy and area under the curve (AUC) of CCTA alone and CCTA+CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy and AUC of CCTA+DLrest and CCTA+DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%,88%,98%, respectively. All CCTA+CTPStress, CCTA+CTP-DLRest and CCTA+CTP-DLStress significantly improved detection of hemodynamically significant CAD (p<0.01).Time of CTP-DL was significantly lower as compared to human analysis (39.2±3.2 vs. 379.6±68.0 seconds, p<0.001).Conclusion: Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTPStress.

Download Full-text