Scientific Evidence and Clinical Observations
The advantage of Cardisiography lies in the early detection of heart disease in people who are still completely asymptomatic.
Studies show results that demonstrate the clear benefits of the method for early detection of reduced blood flow to the heart muscle.
A comprehensive summary of the scientific background and study results can be found here:
Cardisio at PubMed
Current and important
A mathematical approach to demonstrate R to T wave concordance of the human ECG
R-to-T-wave concordance within the same lead of the human electrocardiogram (ECG) has been under discussion for decades, as the QRS complex with its R-wave represent depolarization and the T-wave repolarization. Extracellular recorded monophasic action potential (MAP) of the human heart muscle fibre resembles the first derivation of the intracellular MAP over time, showing R-to-T-wave discordance. While a single fibre monophasic electrophysiology lacks many aspects of the ECG, bipolar registration for the different layers of the ventricular wall (transmural gradient) gives more detailed information about the local MAP, as endo-, meso- and epicardium show a MAP time difference (voltage gradient) dependent positioning of the T-wave, within a simultaneously recorded epicardial ECG.
Without an integrated consideration of the heterogenous (endo-, meso- and epimyocardial) MAP, T-wave concordance cannot be explained, as it would provide a homogenous model like the single heart muscle fibre MAP. A closed form representation of the potential difference to explain concordance was found. We developed a 3-dimensional, time dependent setup simulating the transmural gradient. The time-dependent spatial integrals, which account for the extracellular loading-density (which is inversely related to MAP) along all layers show concordance.
These functions allow to calculate the electric potential at any point in space at any given time within the QT-Interval. The closed form solution of the electric potential enables to identify the corresponding T-wave morphologies. Inversely, pathological patterns in the action potential can then be identified from the ECG. Our approach is an attempt to overcome the inverse problem and to reduce empiricism, as change of T-wave morphology can be assigned.
Community-based cardiovascular risk assessment using the CardisioTM AI test: a prospective cohort study
Background: Cardiovascular disease (CVD) accounts for significant morbidity and mortality disproportionately affecting hard-to-reach individuals. New technology that enables community testing rather than attending hospital may address health inequalities and facilitate new care pathways.
Aim: To explore whether the Cardisio test, which interprets three-dimensional vectorcardiography activity using a cloud-based artificial intelligence (AI) algorithm, can identify asymptomatic CVD.
Design & setting: Prospective cohort study in three settings: general practice, pharmacy, and a community health centre. Recruitment targeted asymptomatic adults aged ≥18 years, with a QRISK3 score ≥10% or CVD risk factors.
Method: A 10-minute test using five electrodes (four chest, one back). The Cardisio results are classified into red, amber, or green based on the Cardisio test’s perfusion (P), structure (S), and arrhythmia (A) parameters. Pre- and post-test questionnaires provided feedback on participants’ experiences. Results reviewed by a chief investigator ([CI] independent consultant cardiologist) and dealt with according to the study participants’ results and medical profile.
Results: In total, 628 tests were performed, 51% male (n = 320), 49% (n = 308) female, with a mean age of 54 years (18-75 years). In the opinion of the CI, there was a strong association between one or more Cardisio red test results and referral to cardiology clinic being indicated (P<0.001). The test was understood as easy to perform, with an 87.5% recommendation rate among participants (n = 492 of the 560).
Conclusion: This simple, near-patient test afforded high-risk hard-to-reach individuals with access to an acceptable test that can facilitate appropriate referral. The automated test does not rely on interpretation of electrocardiogram (ECG) readouts and so is more effective at identifying underlying CVD than a traditional 12-lead ECG.
Keywords: cardiovascular diseases; clinical diagnosis; health inequities; inequalities; primary health care.
Overview
German Society for Cardiology - Cardiovascular Research e.V – 91st Annual German Cardiac Society Meeting 2025 – Evaluation of new vector electrocardiography algorithms for identifying patients with reduced left ventricular ejection fraction and left ventricular hypertrophy
Originally presented at the 91st annual German Cardiac Society meeting 2025.
Background:
Reduced left ventricular ejection fraction (LV-EF) and left ventricular hypertrophy (LVH) are associated with high mortality and morbidity. This study evaluated the diagnostic performance of a modified vectorcardiography (VCG) algorithm, a technique which records cardiac electrical activity in three dimensions, for detecting reduced LV-EF and LVH.
Methods:
This study included patients with reduced LV-EF (< 40%) and LVH (indexed LV mass > 55 g/m²) compared with controls without cardiac pathology. Cardiac magnetic resonance imaging (CMR) was performed and used as the reference standard for measuring LV-EF and LV mass. The modified VCG analyzed 533 VCG parameters per heartbeat to classify cardiac status. In total, 533 regular parameters and 50 Frenet-Serret parameters were analyzed, amounting to 583 parameters. Parameters were selected based on a low p-value from the Mann-Whitney-Wilcoxon test, indicating statistical significance. Bayes’ theorem was used to update prior probabilities and to derive posterior probabilities for reduced LV-EF and LVH when compared with controls.
Results:
A total of 280 patients were included. The group with reduced LV-EF (n=40) had a mean age of 56 ± 16 years and was predominantly male (78%), with a median LV-EF of 31.5% (23.4-36.3%). The LVH group (n=209) had a mean age of 60 ± 16 years (87% male) with an indexed LVM of 67g/m² (61-77 g/m²). Controls (n=31) had a mean age of 50 ± 16 years (61% male), normal LV-EF of 62 ± 5.6% and indexed LV mass of 40g/m² (35-47g/m²). In total 4 VCG parameters were used. A sensitivity of 80.0%, a specificity of 85.7% and an accuracy of 82.9% were achieved for the detection of reduced LV-EF. A Sensitivity of 74.5%, a specificity of 68.6% and an accuracy of 73.6% were reached for the detection of LVH. In with patients haing both reduced LV-EF and LVH (n=64), the VCG demonstrated a sensitivity of 79.3% and a specificity of 85.7% with an accuracy of 82.8%.
Conclusion:
The modified VCG algorithm showed notable diagnostic value for detecting reduced LV-EF and LVH. The VCG could serve as a fast, non-invasive, and cost-effective method to assist clinicians in identifying significant cardiac pathologies and guiding further diagnostic steps. Further optimization of the VCG algorithm is necessary to improve differentiation between healthy individuals and those with cardiac disease, enhancing its clinical use.
German Society for Cardiology - Cardiovascular Research e.V - DGK Herztage 2024 – AI-based 5-lead 3D-vectorcardiography (5L3DVCG-AI) detecting cardiac pathologies at rest may replace conventional 12-lead ECG with potential additional value
Originally presented at the DGK-Herztage 2024. https://doi.org/10.1007/s00392-024-02526-y
Introduction:
Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI) is easy to use, quantifies the individual risk for cardiac pathologies in need for further diagnostic procedures and may, with good results in women, facilitate and align the cardiological diagnostic pathway for coronary heart disease.
Methods:
In this multicentre retro- and prospective study, recordings from 5L3DVCG-AI were externally validated against automated 12-lead ECG in 287 patients with and without cardiac pathologies. 5L3DVCG-AI derived 12-lead ECG (VCG-ECG) was reconstructed from 5L3DVCG-AI with an algorithm (hgh-1.1.23) and time intervals were derived from 5L3DVCG-AI. Two independent specialist cardiologists masked for 12-lead ECG results interpreted VCG-ECG qualitatively and quantitatively. The following variables were compared between 5L3DVCG-AI and 12-lead ECG: electric heart axis and rhythm, HR, and time intervals for P, PQ, QT, QTcB. Presence of cardiac pathology (CP) was categorised as exclusion of any CP (control), mild CP or overt CP by 2 independent cardiologists from clinical practice with a follow-up period of 16.2 ± 7.5 months. Diagnostic accuracy was assessed for ECG findings and abnormalities. Correlation between VCG-ECG and 12-lead ECG was calculated for electric heart axis (Spearman’s rank correlation coefficient). Agreement was tested with Bland-Altman analyses. The modified PROCAM-score was used for cardiovascular risk factor (CVRF) assessment.
Results:
Of 287 patients (m:w 62:38%, 55.9 ± 16.1 years) of mixed ethnicity and moderate CVRF (2.1 ± 1.2), 70% were controls, 21% had mild CP and 9% overt CP. Strong correlations were seen for HR and electric heart axis (r=0.97 and r=0.71, p<0.001 respectively) with 12-lead ECG which was visually confirmed. Quantification of ECG variables such as times for P, PQ, QT, QTcB showed strong correlations (all p<0.001), low systematic bias (SB; -0.9 to -3.9%) and narrow 95% upper and lower limits of agreement (uLoA; 5.6 to 17.3%, lLoA; -8.5 to 19.1%). In women, 5L3DVCG-AI at rest is strong in detecting CVR (r=0.71, p<0.001, mod. PROCAM-Score) and in differentiating cardiac pathologies (β=0.24, T=2.64, p<0.05, corrected for CVR). ECG abnormalities (AF, delayed R-progression, cardiac ischaemias, LVOT VES, sinus bradycardia, sinus tachycardia, AT, LAHB, LHH) were visually detected from 12-lead ECG and VCG-ECG with a sensitivity of 75% and specificity of 100% with low interrater variability. All clinically relevant ECG pathologies were displayed in both systems. The VCG-ECG had only small differences regarding the amplitude of the QRS-complex in three cases.
Conclusion:
In summary, 5L3DVCG-AI is an easy-to-use and feasible technology with good accuracy and reproducibility for electric heart axis, ECG-parameters and intervals and thus offers additional value in detecting individuals with cardiac pathologies or cardiac risks. 5L3DVCG-AI may replace conventional 12-lead ECG in the General Practice or cardiological outpatient departments. Especially for women this may offer additional value.
National Health Service (NHS), SBRI Healthcare: Assessing the impact of using community-based heart testing in primary care to detect early signs of cardiovascular disease through a novel, quick, low- cost test which uses sophisticated AI-based analysis.
The SBRI study (SBRIH21P3013) investigated how the Cardisiography could be incorporated into the NHS, focusing on its application within Primary Care to enhance early detection of cardiovascular issues and improve the efficiency of patient referrals to Secondary Care.
SBRI Healthcare is a UK government initiative aimed at promoting innovation in healthcare. It works closely with NHS partners to identify unmet clinical needs and provide support for developing solutions that enhance patient outcomes and healthcare efficiency.
Cardisiography (CSG), a non-invasive, AI-based diagnostic tool, is designed to be a faster and more cost- effective method for identifying heart conditions compared to traditional methods. Through the study, the goal was to assess its impact in routine NHS clinical settings and determine its effectiveness in improving patient referrals to more specialized care when necessary.
The study was published in the British Journal of General Practice in 2025.
Background:
Cardiovascular disease (CVD) is a leading cause of mortality worldwide, making early and accurate diagnosis essential for improving patient outcomes. Traditional diagnostic tools, like ECGs, are often inadequate in primary care settings, leading to inaccurate and unnecessary referrals to secondary care.
Objective:
This pilot-study, conducted by SBRI Healthcare, aimed to evaluate the feasibility, diagnostic accuracy, care pathway effects, and clinical utility of the Cardisio test—a vectorcardiography-based tool—for early detection of CVD in primary care settings.
Methods:
Participants: 628 asymptomatic, elevated-risk individuals were recruited from three primary care settings in the West Midlands, UK.
Settings: GP practice, in-pharmacy setting, and outreach pharmacy.
Procedure: The Cardisiography test was administered by trained non-clinical staff.
Analysis: Test outcomes were compared with standard care to assess diagnostic accuracy, and the impact on secondary care referrals was analyzed.
Results:
- High Diagnostic Accuracy: The Cardisio test demonstrated:
- Sensitivity of 73.8%
- Specificity of 94.4%
- Positive Predictive Value (PPV) of 80%
- Negative Predictive Value (NPV) of 90.4%
- Strong Correlation with Clinical Decisions: A strong correlation was found between Cardisio results and clinical decisions for secondary care referrals (p<0.001).
- Positive Patient Feedback: Participants reported high satisfaction, with a Net Promoter Score of 88%.
- Ease of Use: Non-clinical staff were effectively trained to administer the test, confirming its scalability and feasibility for widespread use in non-clinical settings such as pharmacies.
- Remote and Community Testing: The Cardisio test enables community-based testing in pharmacies and outreach settings, improving access to heart disease screening and reducing health inequalities in underserved populations.
- ESG Results: The testing method through local GP surgeries and pharmacies showed a promising 60.7% CO2e reduction, a balanced gender ratio of 49:51, and included diverse ethnic minorities (67.8%). These results align with NHS sustainability goals.
Conclusion:
The Cardisio test is a highly accurate, cost-effective, and user-friendly tool for early detection of CVD in primary care. Its strong PPV and NPV values, ability to reduce unnecessary secondary care referrals, opportunities for remote and community testing, and positive environmental and social outcomes make it an ideal tool for early diagnosis and decision-making in healthcare settings.
About SBRI
The Small Business Research Initiative (SBRI) Healthcare is a national award-winning programme in the UK. It accelerates innovative technologies in the NHS and the wider health and social care system, addressing unmet health and care needs.
SBRI Healthcare provides funding and support to early-stage projects enabling testing for business feasibility and technology development, as well as for more mature products through support for real- world implementation studies
SBRI Healthcare, an Accelerated Access Collaborative initiative in partnership with the Academic Health Science Networks (AHSNs), has awarded £3.3 million to eight late-stage innovations that help detect, prevent, and manage cardiovascular disease (CVD).
You can read more about SBRI, and the healthcare grant awarded to Cardisio here:
German Society for Cardiology - Cardiovascular Research e.V - 90. DGK-Jahrestagung 2024 – 5L3DVCG-AI for identification of cardiac pathology in a mixed population
Originally presented at the 90. DGK-Jahrestagung 2024.
Purpose of the study
- Artificial Intelligence (AI) and access to a large global clinical data repository have the potential to boost the performance of Vectorcardiography (VCG) beyond conventional techniques.
- Quantifying cardiovascular risk (CVR) according to SCORE2, QRISK3 or ASCVD is not always feasible, especially in hard to reach populations.
- The modified PROCAM-Score (CVRF-Score) is a validated alternative.
Conclusion
- AI further improves the easy-to-use and inexpensive 5L3DVCG
- 5L3DVCG-AI identifies asymptomatic females at high risk for CVD
- CSG-Index differentiated between no signs and symptoms of CVD and patients with cardiac pathology or CVD
- 5L3DVCG-AI identifies patients at risk for CVD and cardiac pathology
- 5L3DVCG-AI opens up a diagnostic window for early detection of CVD
- CSG is superior to CVRF-Score in differentiating people at risk of CVD or cardiopathology, especially for women and hard-to-reach population
American Heart Association Scientific Sessions 2023 – Validation of the Artificial Intelligence Based 5 Lead 3D Vectorcardiography in Comparison to the 12 Lead ECG in a Mixed Population
Originally presented at the AHA 23 (American Heart Association Scientific Sessions 2023) and published in the American Heart Association Circulation. 2023;148:A16473.
Purpose of the study
- Validate Artificial Intelligence based 5 lead 3D vectorcardiography (5 L 3 DVCG AI)
- Use additional information of 5 L 3 DVCG AI over standard 12 lead electrocardiography (ECG) in the detection of cardiac pathology at rest.
- Basis for investigation of 5 L 3 DVCG AI as a new screening tool for cardiac pathology in ongoing prospective multinational trials
Conclusion
- 5 L 3 DVCG AI derived ECG showed high correlation and low bias compared to standard 12 lead ECG
- Easy to use 5 lead ECG may replace 12 lead ECG without major training or expertise
- Shorter intervals to be considered when interpreting 5 L 12 L ECG and “normal” values in the ongoing prospective large scale performance clinical trials
- 5 L 3 DVCG AI identifies persons at risk for CVD (s abstract 15181 PSu 3119)
German Society for Cardiology - Cardiovascular Research e.V. Heart Days 2023 – 5-lead 3D-vectorcardiography differentiates between high and low cardiovascular risk profiles in patients with suspected or known coronary heart disease
Purpose of the study
- Validate Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI)
- use additional information of 5L3DVCG-AI over standard 12-lead electrocardiography (ECG) in the detection of coronary vascular disease (CVD) at rest
- basis for investigation of 5L3DVCG-AI as a new screening tool for CVD in ongoing prospective multinational trials
Conclusion
- Data extend the previous findings of 5L3DVCG-AI identifying CVD patients with cardiac ischaemia
- Now differentiating healthy controls from CVD and those with higher risk for CVD
- Confirmation of results in female population
- Validation of ECG-reconstruction via heart axis
- CSG-Index is superior to CVRF-Score in identification of CVD
- The ongoing prospective large-scale performance clinical trials will have to confirm these preliminary data to verify the diagnostic accuracy.
German Society for Cardiology - Cardiovascular Research e.V. Heart Days 2023 – Sensitivity and Specificity of the Artificial Intelligence-Based 5-Lead 3D Vectorcardiography in Patients With Suspected or Confirmed Coronary Heart Disease
Originally presented at the DGK Herztage 2023. Published in the American Heart Association Circulation. 2023;148:A15181
Purpose of the study
- Validate Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI)
- Use additional information of 5L3DVCG-AI over standard 12-lead electrocardiography (ECG) in the detection of coronary vascular disease (CVD) at rest
- Basis for investigation of 5L3DVCG-AI as a new screening tool for CVD in ongoing prospective multinational trials
Conclusion
These data extend the previous findings of 5L3DVCG-AI identifying CVD patients with cardiac ischaemia from those without to now differentiating healthy controls from CVD and those with higher risk for CVD. 5L3DVCG-AI may thus be a further scalable screening method to identify patients at risk for CVD in need for risk modification or further diagnostic procedures.
5L3DVCG-AI-derived ECG showed high correlation and low bias compared to standard 12-lead ECG. The ongoing prospective large-scale performance clinical trials will have to confirm these preliminary data to verify the diagnostic accuracy.
Congress of the European Society of Cardiology 2023 – HDZ-NRW / Mediacc: Comparison of cardisiography with CVRF score for non-invasive assessment of CHD
Summary and interpretation:
The CSG index (CSG parameter) was compared with the CVRF score in terms of predictive power for the presence of CHD
- Modified PROCAM score (CVRF score)
- Classic risk score for determining the pre-test probability of coronary heart disease
- Current analysis:
- 407 patients
- 225 patients HDZ, Bad Oeynhausen
- 182 patients from a GP practice, Berlin
- 407 patients
Results
“The CSG Index differentiated those with no signs and symptoms of CHD and patients with CHD and is a better predictor for cardiovascular risk than the classical risk factors”
- The CSG is superior to the CVRF score for the non-invasive assessment of CHD
- CSG index correlates significantly (p < 0.001) with clinically confirmed CHD status
- NPV (negative predictive value) of the CSG was 91%
Study Heart Center Bad Oeynhausen, Germany: Comparison of Cardisiography (CSG) with myocardial SPECT in suspected CHD and known CHD
Study at the Heart and Diabetes Center North Rhine-Westphalia in Bad Oeynhausen confirms the diagnostic relevance of Cardisiography.
Comparison of Cardisiography (CSG) with myocardial SPECT in suspected and known CHD:
- Cardisiography (CSG) shows a significant correlation with MPS in pre-diagnostic testing for CHD
- A normal CSG correlates with a normal to low pathologic MPS, corresponding to a high negative predictive value of 98%
- CSG is suitable as a pre-selection tool for GP or cardiology practices to decide on non-invasive imaging in patients with suspected CHD
Cardisio Validation Study Sana Heart Center Cottbus: Cardisiography as a novel non-invasive diagnostic tool for the detection of coronary artery disease at rest
The team led by Dr. Temirlan Erkenov from the Department of Cardiac Surgery at SANA Heart Center in Cottbus, Germany, concluded that: “… Cardisiography is a simple, precise and highly valid method that is suitable as a non-invasive diagnostic modality for the initial assessment of stable CHD in a clinical setting…” (Cardisiography as a novel non-invasive diagnostic tool for the detection of coronary artery disease at rest – a first prospective study of diagnostic accuracy; Temirlan Erkenov, Tomasz Stankowski, Oliver Grimmig, Sören Just, Prof. Oleg Remizov, Prof. Dirk Fritsche)
The study included data from 106 patients in whom coronary angiography was indicated and performed. Subsequently, Cardisiography was performed, the result of which was blindly correlated with that of coronary angiography. The result: In a total of 86 of the 106 patients, vascular disease was confirmed by coronary angiography. Cardisiography identified 82 of the 86 cases (95.4 percent), while conventional echocardiography detected only 12 cases. This results in a sensitivity of 95.4 percent for Cardisiography, a specificity of 90 percent and a positive predictive value of 97.6 percent for CHD.
“In Western countries, coronary heart disease is one of the leading causes of death and a common cause of physical disability. The reason for the severe course is the fact that the initial manifestation of the disease can be a heart attack or sudden cardiac death. Cardisiography is a new, easy-to-use and examiner-independent technology that uses vector cardiography with the modern analysis possibilities of artificial intelligence,” the authors explain the reason for conducting the study – and for its convincing course.
Multicenter Study – Comparison of Cardisiography (CSG) with Coronary Angiography in Patients with Suspected Coronary Artery Disease
Frankfurt, January 16, 2020: In the current issue, the renowned journal “Journal of Electrocardiology” publishes a peer-reviewed study demonstrating the impressive precision of Cardisiography in screening coronary heart disease. The article provides results on the sensitivity and specificity of Cardisiography: The sensitivity is 97% for male subjects, 90% for females, i.e. 97% of sick men are recognized as diseased and 90% of women. In terms of specificity, the women who participated in the study are ahead of the men with 74% with 76%, i.e. 76% of healthy women are recognized as healthy and 74% of men. Cardisiography is the first procedure that can be used to determine the risk of coronary heart disease (CHD) in asymptomatic people non-invasively, quickly and cheaply.
Complementary Studies And Literature On Our Technology
Artificial intelligence-supervised vectorcardiography for the diagnosis of a young adult with abnormal origin of the right coronary artery from aorta
Özyüksel A, Salatzki J, and Steen H (2024). Artificial intelligence- supervised vectorcardiography for the diagnosis of a young adult with abnormal origin of the right coronary artery from aorta. Cardiology in the Young, page 1 of 3.
The Poynting Vector: Power and Energy in Electromagnetic Fields
Carpenter KH. The Poynting vector: power and energy in electromagnetic fields. Department of Electrical and Computer Engineering. Kansas State University (2004)
Spatial Vector Electrocardiography; The Clinical Characteristics of S-T and T Vectors
Grant RP, Estes EH Jr, Doyle JT. Spatial vector electrocardiography; the clinical characteristics of S-T and T vectors. Circulation. 1951 Feb;3(2):182-97.
Cardiogoniometry: A New Noninvasive Method for Detection of Ischemic Heart Disease
Saner H, Baur HR, Sanz E, Gurtner HP. Cardiogoniometry: a new noninvasive method for detection of ischemic heart disease. Clin Cardiol. 1983 May;6(5):207-10.
Cardiogoniometry: An Electrocardiographic, Non-Invasive and Stress-Free Method for Detecting Cardiac Ischemia
Sanz, Ee. Schüpbach, M. Cardiogoniometry: an electrocardiographic, non-invasive and stress-free method for the detection of cardiac ischemia. GMS Medical Informatics, Biometry and Epidemiology. 5 (2009).
Applicability of Cardiogoniometry as a Non-Invasive Screening Tool for the Detection of Graft Vasculopathy in Heart Transplant Recipients
Spiliopoulos S, Hergesell V, Fischer D, Dapunt O, Krueger U, Koerfer R, Tenderich G. Applicability of cardiogoniometry as a non-invasive screening tool for the detection of graft vasculopathy in heart transplant recipients. Interact Cardiovasc Thorac Surg. 2016 Dec;23(6):976-978.
Computer Analysis of the Corrected, Orthogonal Cardiogram
von Mengden, H.J., Brodda, K. Computer analysis of the corrected, orthogonal cardiogram. Archiv für Kreislaufforschung 67, 123–141 (1972).
Assessment of the Spatial QRS-T Angle by Vectorcardiography: Current Data and Perspectives
Voulgari C, Tentolouris N. Assessment of the Spatial QRS-T Angle by Vectorcardiography: Current Data and Perspectives. Curr Cardiol Rev. 2009 Nov;5(4):251-62. doi: 10.2174/157340309789317850. Erratum in: Curr Cardiol Rev. 2010 Nov;6(4):373.
Improved Evaluation of Left Ventricular Hypertrophy Using the Spatial QRS-T Angle by Electrocardiography
Maanja, M., Schlegel, T.T., Kozor, R. et al. Improved evaluation of left ventricular hypertrophy using the spatial QRS-T angle by electrocardiography. Sci Rep 12, 15106 (2022).