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The CMR datasets were acquired retrospectively under the approval of the institutional review boards (IRBs) at each participating institution, including Beijing Fuwai Hospital, Beijing Anzhen Hospital, Guangdong Provincial Peoples Hospital, the 2nd Affiliated Hospital of Harbin Medical University, the First Hospital of Lanzhou University, Renji Hospital, Tongji Hospital and Peking Union Medical College Hospital. Informed consent was waived by the IRBs. Before model training, testing and reader studies, all data underwent deidentification processes.

The CMR database search was performed for all eight centers to identify CVDs and normal controls. All data were anonymized and deidentified, as per the Health Insurance Portability and Accountability Act Safe Harbor provision56. Inclusion criteria were (1) patients with a definitive diagnosis of CVD and (2) patients with CMR scans at baseline before surgical treatment, if any. Exclusion criteria were (1) incomplete cine or LGE modalities, (2) SAX cine with fewer than five views, (3) CMR images with insufficient scan quality, (4) CVD patients missing clinical data and (5) CMR examinations that could not be interpreted and agreed upon by the committee cardiologists according to the diagnostic criteria (Methods). The detailed diagnostic criteria of the 11 types of CVDs and normal controls included in this study was described in Methods. Table 1 and Extended Data Table 1 present the detailed demographics and distribution of the primary dataset and the external validation sets collected from the other seven medical centers across China. To offer a comprehensive perspective on our primary development dataset, we went the extra mile by collecting the LV ejection fraction (LVEF) metric for all 7,900 subjects (including 1,250 normal controls and 6,650 patients with CVD) within the primary dataset. We meticulously summarized the distribution of demographics and LVEF across the 11 specified CVD classes and the normal control class in Supplementary Table 5. Additionally, we generated density plots to illustrate the distribution of LVEF for each class in the primary dataset, offering a more comprehensive representation (Supplementary Fig. 1).

The fresh consecutive testing set is designed to capture the genuine spectrum of disease phenotypes in the real-world clinical prevalence. To offer a thorough understanding of the severity of cases in alignment with real-world clinical prevalence, we have presented five key cardiac function metrics. These metrics include LVEF, LV mass, LVMi (LV mass index), LV end-diastolic volume and LV end-diastolic volume index. Supplementary Table 6 presents the distribution of demographics and the cardiac functions across 11 CVD classes and the normal control class in the fresh consecutive testing set. For improved visualization and clarity, we have depicted the prevalence of the 11 CVD classes in both the fresh consecutive testing set (n=532 patients with CVD) and the primary discovery dataset (n=6,650 patients with CVD) using pie charts in Supplementary Fig. 2. The fresh consecutive testing set offers a representation of the genuine clinical prevalence. Through direct comparison, it is evident that the primary dataset and the consecutive testing set exhibit very similar CVD prevalence and distribution. The top three most prevalent CVDs referred to the CMR examination remain HCM, DCM and CAD.

All images were acquired by breath-holding and electrocardiographic gating. A balanced steady-state free precession sequence was used for cine images with a continuous sampling from the basal to the apical levels on SAX views and two-chamber, three-chamber and 4CH long-axis views. We included cine MRI from two views in this study: the standard SAX cine and the long-axis 4CH cine. The SAX cine clearly depicts the RV and the LV. The 4CH cine shows the four chambers of heart: right atrium, left atrium, RV and LV.

LGE MRI has been established as the gold standard reference for myocardial viability and replacement fibrosis in the myocardium57,58. In our CMR cohorts, the LGE images were obtained using phase-sensitive inversion recovery sequence with a segmented FLASH readout scheme performed 1015min after injection of gadolinium-based contrast with 0.15mmolkg1 per bolus. Gadolinium contrast agents can be used to detect areas of fibrosis, as the prolonged washout of the contrast correlates with a reduction in functional capillary density in the irreversibly injured myocardium59. The SAX LGE used in the study was acquired from the SAX view with the same section thickness, covering the entire left ventricle from the base to the apex (nine parallel views for most cases). Note that LGE is an invasive examination that requires contrast injection and was therefore not performed for normal controls.

The typical CMR scan protocol and scanner parameters for the primary and external validation sets are presented in Supplementary Table 7. Extended Data Fig. 2 shows an illustration of cardiac MRIs (SAX cine, 4CH cine and SAX LGE) utilized in model development. Supplementary Videos 111 demonstrate example CMR of the 11 types of CVDs.

For each patient in the disease cohort, the textual description of the abnormalities in the CMR and the clinical report was extracted as the main reference. Besides that, all CMR records underwent additional annotation procedures. To annotate the disease cohort, a group of certified CMR experts reviewed all records and clinical reports. Every record was randomly assigned to be reviewed by a single physician specifically for this task, not for any other purpose. All annotators received specific instructions and training regarding how to annotate CMR data to improve labeling consistency. The diagnostic criteria we adopted in this study for each CVD class are described in Methods. CMR examinations that could not be interpreted by physicians received further annotation from a consensus committee of board-certified practicing cardiologists (with >15years of experience in CMR reading) working in Fuwai Hospital. The CMR examinations that could not be interpreted or agreed upon by the committee were removed from our dataset.

For the independent gold-standard test dataset with 500 patients (Extended Data Table 6) for humanmachine comparison, six physicians working in the MRI department at Fuwai Hospital contributed directly to its annotation (the six physicians were not involved in dataset annotation as described above). All participating physicians received specific instructions and training regarding how to annotate CMRs to ensure consistency. We divided the physicians into three groups according to their reading experience in CMR: 35years, 510years and more than 10years. CMR physicians in each group reviewed a randomly selected set of the 500 CMRs in a nonrepetitive manner.

The CMR preprocessing pipeline aimed to remove the additional burden of the deep neural network learning to find patterns between images for disease classification. All cardiac MRIs were preprocessed to (1) resample MRI images to the same spatial resolution and (2) localize the heart region of interest (ROI) to a crop image. We detailed the preprocessing step for cine and LGE MRI below and in Extended Data Fig. 4.

SAX cine comprises nine parallel views (for most cases) covering the apical to the basal levels of the LV. Each view contains 25 frames (cardiac phases), leading to 225 images in one single SAX cine record. We examined the representational power of different numbers of input views in developing the classification model. Balancing efficiency and effectiveness, the three-view input scheme achieved a greater representation of SAX cine and therefore is adopted throughout the rest of the study. The three-view input scheme includes the middle layer (the mid slice among the parallel layers spanning from the base to the apex), the second layer above the middle layer and the second layer below the middle layer (Extended Data Fig. 2). We extract the ImagePositionPatient tag and the ImageOrientationPatient tag from each Dicom header to locate the three layers. Then, three-spline interpolation provided by SimpleITK60 library (https://simpleitk.org/) is applied to resample the raw cine MRIs to the same spatial resolution of 0.994mm0.994mm, which is the most common spatial resolution across all subjects investigated in this study. We developed a heart ROI segmentation model (the following section) and used it to localize the region of heart for each cine MRI. The heart ROI segmentations predicted by the AI models were manually checked to ensure their accuracy. The extracted ROIs are padded to keep the aspect ratio the same without distortion, and then resized to 224224. The top and bottom 0.1% of the pixels in cine MRI images are clipped to avoid pixels that are outliners of the distribution. The cine images are scaled between 1 and 255, and then normalized by zero mean and unit variance before feeding them to the model. We sample a clip of 25 frames from each full-length cine sequence using a temporal stride of two, resulting in 13 frames as inputs to model development. The 4CH cine shares the same preprocessing pipeline as SAX cine, except that only one single layer (mid slice) is used to represent the 4CH view. For SAX LGE, all layers covering from the base to the apex of the heart are used for diagnostic model development. The preprocessing steps for SAX LGE are similar to that of cine MRI. We resampled SAX LGE along the z-axis to ensure that each LGE sequence contains nine slices because nine is the most common number of views for SAX LGE included in this study.

We developed heart detection DNN models to automatically extract the heart ROI regions (Extended Data Fig. 4). Three DNN models for SAX cine, 4CH cine and SAX LGE were trained and evaluated, respectively. We applied nnU-Net61 as our model backbone and generated the ground-truth segmentation masks for model supervision using a semi-automatic approach. (1) Automatic localization: for SAX cine and 4CH cine, we selected the pixel region with maximum standard deviation across all frames. These regions localize the heart ROI as heart is a beating organ with high standard deviation in its position. Specifically, for each cine movie sequence (s={{x}_{1},ldots ,{x}_{n}}), we computed a single pixel map of standard deviations across all frames ({x}_{mathrm{std}}=sigma ({{x}_{1},ldots ,{x}_{n}})). This map was used to compute an Otsu threshold to binarize and label regions with the greatest variation in cine modality21. For each cine sequence, a binary segmentation mask of the heart ROI is defined for the length of the cardiac cycle. All segmentation masks went through manual checking. The localization procedure captures the heart ROI in around 90% of cases. The rest of the cases are labeled manually. (2) Manual labeling: we manually drew the bounding box capturing the heart ROI, using 3D Slicer62 and ITK-SNAP63. We used the Scissors tool provided by the Segment Editor in 3D Slicer and the Polygon Inspector in ITK-SNAP to locate heart ROI. A binary segmentation mask was saved for each CMR sequence. For SAX LGE, we manually drew the annotations as model supervision.

In terms of model architecture, the detection model shares the classic U-net64 backbone with three small adjustments: (1) batch normalization is replaced with instance normalization65, (2) rectified linear unit (ReLU) is replaced with leaky ReLU66 as the activation function and (3) additional auxiliary losses are added in the decoder to all but the two lowest resolutions. The model outputs the binary bounding box that extracts the heart ROI. For model training, we adopted Adam optimizer and stochastic gradient descent (SGD) with Nesterov momentum (=0.99). The initial learning rate was set to be 0.01, and the decay of the learning rate followed the Poly learning rate policy67. Batch size was set to 36. Data augmentation included rotations, scaling, gamma correction and mirroring. The loss function is the sum of cross-entropy and Dice loss68.

For models based on cine sequence, we sampled a clip of 13 frames from each 25-frame cine video using a temporal stride of 2 and spatial size of 224224, resulting in 75656 input 3D tokens. The 3D patch partitioning layer obtains tokens, with each patch/token consisting of a 128-dimensional feature. In practice, 3D convolution without overlapping is applied for this tokenization, and the number of output channels is set to be 128 to project the features of each token to a 128-dimension.

The developed model consists of four stages, that is, four video swin transformer blocks. Each stage, besides the last stage, performs 2 spatial downsampling in the patch merging layer. It is worth noting that we do not downsample along the temporal dimension. The patch merging layer concatenates the features of each group of 22 spatially neighboring patches and applies a linear layer to project the concatenated features to half of their dimension. The video swin transformer block consists of a 3D window-based multihead self-attention module and a 3D-shifted window-based multihead self-attention module, followed by a feedforward network, that is, a two-layer multilayer perceptron, with Gaussian error linear unit nonlinearity in between. Layer normalization is applied before each multihead self-attention module and multilayer perceptron, and a residual connection is applied after each module. We used the base version of VST. The number of heads for each stage is 4, 8, 16 and 32. Extended Data Fig. 3a shows the schematic overview of the VST-based framework for modeling SAX cine.

Model performance improved with increasing training data sample size. For the screening model, we used random rotation, random color jitter and adding random number. During each step of SGD in the training process, we perturbed each training sample, cine video sequences, with a random rotation (between 45 and +45 degrees for SAX cine and between 20 to +20 degrees for 4CH cine), random color jitter and with adding a number sampled uniformly between 0.1 and 0.1 to image pixels (pixel values are normalized) to increase or decrease brightness of the images. For LGE, we used random rotation between 45 and +45 degrees, random color jitter and random flip along the z-axis. Data augmentation resulted in improvement for all models.

First, we developed VST-based models for SAX cine, 4CH cine and SAX LGE, respectively. Then, to fuse information from different modalities, we added a global average pooling layer following the last self-attention module for each VST model. This resulted in a 1,024-dimension feature vector from each modality. We further concatenated the 1,024-dimension vectors and added a fully connected layer on top of that to aggregate the features. The final fully connected softmax layer produces a distribution over the output classes. In terms of training, we loaded and froze the pretrained weights of each VST branch from different modalities using transfer learning69 and only finetuned the last fully connected layers for feature aggregation.

Following the classic VST configuration27, we employed an AdamW optimizer using a cosine decay learning rate scheduler and 2.5 epochs of linear warmup. A batch size of 32 was used. The backbone VST is initialized from the ImageNet70 and Kinetics-600 (ref. 71) pretrained model; the head is randomly initialized. Model pretraining plays a strikingly important role in VST-based CMR interpretation. We also found that multiplying the learning rate of the backbone by 0.1 improves performance. Specifically, the initial learning rates for the pretrained backbone and randomly initialized head were set to be 1104 and 1103, respectively. The impact of learning rate modification on the VST backbone was systematically examined as below. We adopt 0.2 stochastic depth rate and 0.05 weight decay for the Swin base model used in this study. To prevent the models from becoming biased toward one class, we balanced the training datasets for both screening and diagnostics using the ClassBalancedDataset sampling strategy72. Each VST branch derived from the single modality was trained for 150 epochs and then fed into the fusion model, following with 20 epochs of finetuning particularly for the fusion layers. For inference, we set the batch size to be one and the number of workers to be four. The training time for model development using four NVIDIA GeForce RTX 3090 graphics processing units with 24GB VRAM was about 77h, and the inference time for each subject was only 0.233s.

The impact of learning rate modification on the VST backbone was systematically examined through a controlled experiment. The experiment encompassed a range of learning rates, from 1102 to 1106, with a focus on their effects on the AI diagnostic model based on SAX cine. The investigation was conducted on the primary cohort (6,650 CVD patients), utilizing a twofold configuration for training and the remaining fold for testing. The model was trained for 150 epochs with five different learning rate initializations for the model backbone: 1102, 1103, 1104 (as applied in this study), 1105 and 1106. Other configurations were kept consistent for a fair and direct comparison, and the training loss for each scheme was plotted for analysis (Supplementary Fig. 3). From the depicted figure, several key observations emerge. When the learning rate is set too high (1102, curve in blue color), the model struggles to converge and the training loss fails to descend, in stark contrast to the more optimal setting of 1104 (curve in green color). Notably, the model under the 1102 learning rate incorrectly classified all samples into the HCM class during testing. Conversely, when the learning rate is set too low (1106, curve in purple color), the loss descends very slowly over the training period. As depicted in the figure, the loss curves for 1105 and 1106 remain at a relatively high level compared with the more effective setting of 1104. Further evaluation included the calculation of F1 and area under the receiver operating characteristic curve scores for the testing fold under the aforementioned experimental settings (Supplementary Fig. 3). Notably, the model trained with a learning rate of 1102 failed to converge and was consequently excluded from the quantitative metrics. According to the evaluation results, the initialized learning rate of 1104 demonstrated superior performance compared with the other settings. Therefore, based on these comprehensive analyses, we selected 1104 as the initialized learning rate for our experiment.

We examined the conventional CNNLSTM architecture in CMR interpretation. The CNNLSTM consists of a DenseNet encoder with 40 layers and a growth rate of 12 for feature extraction and an LSTM for temporal feature aggregation. DenseNet encoder comprised a series of two-dimensional convolutions with kernel sizes 11 and 33 and global average pooling to extract the feature vector for each input frame. For LSTM, the feature vector for each input frame is fed into the LSTM module sequentially. LSTM fuses the feature vectors and produces the final classification score after one fully connected layer. For the training configuration of the CNNLSTM model, we adopt the SGD optimizer with a learning rate of 0.001, a momentum of 0.9 and a weight decay of 0.001. A batch size of four is used for training and one is used for testing. The DenseNet encoder of the CNNLSTM model is initialized from the pretrained model21 and the LSTM component is randomly initialized. We kept data augmentation, the input scheme and computational resources the same as VST models with the only difference: SAX cine inputs are resized to 6464 due to CNNLSTM memory constraints.

The performance of the AI models was evaluated by assessing their sensitivity, specificity, precision and F1 score (harmonic mean of the predictive positive value and sensitivity), with two-sided 95% CIs, as well as the AUC of the ROC with two-sided CIs. The F1 score is complementary to the AUC, which is particularly useful in the setting of multiclass prediction and less sensitive than the AUC in settings of class imbalance. For an aggregate measure of model performance, we computed the class frequency-weighted mean for the F1 score and the AUC73.

The cutoff value was set to 0.5 for screening; the CVD class with the highest probability was the diagnostic prediction. Precision, sensitivity (recall), specificity, PPV, NPV and F1 score of each class are related to true-positive (TP), true-negative (TN), false-positive (FP) and false-negative (FN) rates, with formulas as follows:

$$text{Sensitivity}=frac{mathrm{TP}}{mathrm{TP}+mathrm{FN}},$$

$$text{Specificity}=frac{mathrm{TN}}{mathrm{TN}+mathrm{FP}},$$

$$mathrm{Precision}=frac{mathrm{TP}}{mathrm{TP}+mathrm{FP}},$$

$$mathrm{PPV},=frac{mathrm{TP},}{mathrm{TP}+mathrm{FP}},$$

$$mathrm{NPV},=frac{mathrm{TN},}{mathrm{TN}+mathrm{FN}},$$

$${F}_{1}text{-score}=frac{2times mathrm{Precision}times mathrm{Sensitivity}}{mathrm{Precision}+mathrm{Sensitivity}}.$$

The ROC space is defined by 1specificity and sensitivity as the x axis and the y axis, respectively. It depicts relative trade-offs between true positive and false positive, as the classification threshold goes from zero to one. A random guess will give a point along the diagonal line from the bottom left to the top right. Points above the diagonal line represent good classification results and points below the line represent bad results. We applied the class frequency-weighted F1 score and class frequency-weighted AUC to evaluate the performance of our diagnostic model, with the following formulas:

$${rm{Weighted}},{F}_{1}text{-}{rm{score}}=mathop{sum }limits_{i}^{C}{mathrm{ratio}}_{i}{F}_{1}text{-}{mathrm{score}}_{i},$$

$${rm{Weighted}},mathrm{AUC}=mathop{sum }limits_{i}^{C}{mathrm{ratio}}_{i}{mathrm{AUC}}_{i},$$

where ({{F}_{1}text{-score}}_{i}) and AUCi denote the F1 score and AUC for class i, respectively, and ({mathrm{ratio}}_{i}) denotes a frequency ratio for each class i.

In addition, to improve the model interpretability and visualize the features used by the DNN model that determine the final prediction, we used Grad-CAM29 to localize important regionssaliency regionsby visualizing class-specific gradient information. In Grad-CAM, the neuron importance weight ({alpha }_{k}^{,c}) is estimated as

$${alpha }_{k}^{,c}=frac{1}{Z}sum _{i}sum _{j}frac{partial {y}^{,c}}{partial {A}_{{ij}}^{k}},$$

where yc denotes the gradient score for class (c) before the softmax and Ak denotes the feature map activation of the kth layer. After computing the neuron importance weights for each feature map, we can generate a heat map indicating the significant regions related to class (c) by performing a weighted linear combination of the feature maps, followed with a ReLU activation function as

$${L}_{mathrm{Grad}-mathrm{CAM}}^{c}=mathrm{ReLU}left(sum _{k}{alpha }_{k}^{,c}{A}^{k}right).$$

We then used the Shapley values74 to evaluate the influence of each input modality (SAX cine, 4CH cine and SAX LGE). The Shapley value is a principled attribution method used in AI to quantify the contribution of individual input features by assigning each input modality an importance value for a particular prediction. The definition of the Shapley value75 is given in equations below:

$${{{phi }}}_{i}left(vright)=sum _{Ssubset N{i}}{left(begin{array}{c}n\ 1,left|Sright|,n-left|Sright|-1end{array}right)}^{-1}left(vleft(Scup {i}right)-vleft(Sright)right),$$

where ({phi}_{i}left(vright)) denotes the contribution value of input component i, namely the Shapley value of each input modality (player), (N) is the number of layers and (v) is a function mapping subsets of layers to the real numbers: (v:{2}^{N}to {R}), with (vleft(varnothing right)=0), where (varnothing) denotes the empty set. A set of players is called a coalition. The function (v) is called a characteristic function: if (S) is a coalition of players, then (v(S)), called the worth of coalition (S), describes the total expected sum of payoffs the members of (S) can obtain by cooperation. The sum extends over all subsets (S) of (N) not containing input component i; also note that (left(begin{array}{c}n\ a,{b},{c}end{array}right)) is the multinomial coefficient. This formula can also be interpreted as

$$begin{array}{l}{{{phi }}}_{i}left(vright)=frac{1}{{mathrm{Number}};{rm{of}};{rm{layers}}}\sum _{{mathrm{coalitions}}; {mathrm{including}};i}frac{{mathrm{Marginal}};{mathrm{contribution}}; {mathrm{of}};i;{mathrm{to}};{mathrm{coalition}}}{{mathrm{Number}}; {mathrm{of}}; {mathrm{coalitions}}; {mathrm{excluding}};i; {mathrm{of}}; {mathrm{this}}; {mathrm{size}}}.end{array}$$

The diagnosis of myocardial infarction or ischemic cardiomyopathy is based on the European Society of Cardiology, American College of Cardiology and American Heart Association committee criteria76 with significant stenosis on invasive coronary angiography (CAG) or coronary computed tomography angiography, and CMR showed subendocardial or transmural LGE with matching coronary arteries. We excluded cases without available CAG present or inadequate image quality due to arrhythmia or respiratory motion artifact.

We followed the 2020 American Heart Association and American College of Cardiology guidelines for the diagnosis of patients with HCM77. The clinical diagnosis of HCM was made by CMR showing a maximal end-diastolic wall thickness of 15mm anywhere in the LV, in the absence of another cause of hypertrophy in adults. More limited hypertrophy (1314mm) can be diagnostic when present in family members of a patient with HCM or in conjunction with a positive genetic test.

We excluded cases with the following conditions:

Valvular heart disease (aortic valve stenosis, etc.)

Long-term uncontrolled hypertension

Inflammatory heart disease (sarcoidosis, etc.)

Infiltrative cardiomyopathy (amyloidosis, Fabry disease, etc.)

Septal myectomy or alcohol ablation before CMR

CMR images with poor quality

The diagnosis of DCM is based on the diagnostic criteria of the World Health Organization78. Inclusion criteria were based on enlarged LV end-diastolic dimension (>60mm) and reduced LVEF (<45%). The exclusion criteria were as follows:

Significant stenosis of coronary artery (>50% stenosis, assessed on CAG or coronary computed tomography angiography)

Severe valvular disease, hypertension or congenital heart disease

Evidence of acute or subacute myocarditis (T2 weighted image and laboratory tests)

Any other metabolic disease through medical documentation

Inadequate CMR quality

The diagnosis of LVNC is based on previous studies32,79, as follows:

The presence of noncompacted and compacted LV myocardium with a two-layered appearance, with at least involvement of the LV apex

End-diastolic noncompaction/compaction ratio >2.3 on long-axis views and 3 on SAX views

Noncompacted mass >20% of the global LV mass

No pathologic (pressure/volume load, for example, hypertension) or physiologic (for example, pregnancy and vigorous physical activity) remodeling factors leading to excessive trabeculation

The diagnostic standards for ARVC were based on the revised Task Force Criteria80 score with either two major criteria, one major and two minor criteria or four minor criteria. The major criteria include regional RV akinesia or dyskinesia or dyssynchronous RV contraction, ratio of RV end-diastolic volume to body surface area >110mlm2 (male) or >100mlm2 (female) or RV ejection fraction <40%; fibrous replacement of the RV free wall myocardium, with or without fatty replacement of tissue on endomyocardial biopsy; repolarization abnormalities and depolarization or conduction abnormalities on ECG test.

The diagnosis of CAM is based on endomyocardial biopsy or extracardiac biopsy specimens showing positive birefringence with Congo red staining under polarized light, and with native and enhanced CMR imaging in a pattern consistent with CAM: LV wall thickness of more than 12mm shown by CMR without other known cause, with and without diffuse LGE81.

RCM is characterized by ventricular filling difficulties with increased stiffness of the myocardium. The restrictive cardiomyopathies are defined as restrictive ventricular physiology in the presence of normal or reduced diastolic volumes52,82, as follows:

Nondilated LV or RV with diastolic dysfunction

Bi-atrial dilation

Preserved ejection fraction (LVEF 50%)

We excluded subjects that met the following criteria:

With a reduced LV systolic function

Severe atrial fibrillation

Severe valvular disease, hypertension or congenital heart disease

Significant stenosis of coronary artery.

The diagnosis of PAH is based on the results of right heart catheterization examination. Patients are included in this study if they were clinically diagnosed as PAH83:

Mean pulmonary artery pressure (mPAP) 25mmHg

Pulmonary capillary wedge pressure (PCWP) <15mmHg

Pulmonary vascular resistance (PVR) >3 Wood units at rest

We excluded subjects with the following criteria:

Any evidence of cardiomyopathy, myocarditis, CAD, myocardial infarction, valvular disease, or constrictive pericarditis.

Any evidence of respiratory diseases.

History of cardiac surgery

The diagnosis of Ebsteins anomaly is based on apical displacement of tricuspid valve leaflets (8mmm2) with fibrous and muscular attachments to the underlying myocardium31. Patients with other concomitant malformation (for example, congenitally corrected transposition with Ebsteins anomaly) and history of cardiac surgery were excluded.

The diagnosis of acute myocarditis is based on the diagnostic criteria for clinically suspected myocarditis, as recommended by the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases84, and is fulfilled by meeting the Lake Louise criteria85 or by confirmation through endomyocardial biopsy.

Patients with clinically acute myocarditis had the following: acute chest pain, signs of acute myocardial injury (electrocardiographic changes and/or elevated troponin level) and increased laboratory markers of inflammation (for example, C-reactive protein level). CAD was excluded before cardiac MRI. Patients with preexisting CVD were excluded.

The diagnostic criteria for HHD include (1) a history of prolonged, uncontrolled arterial hypertension and (2) concentric hypertrophy with left ventricular maximal wall thickness 12mm.

We excluded patients with the following conditions:

Any other causes of LV hypertrophy

Cardiomyopathy

Obstructive coronary heart disease

Severe valvular disease

Inflammatory heart disease

Severe ventricular arrhythmia such as ventricular tachycardia or left bundle branch block

Poor CMR imaging quality

Healthy controls were recruited as volunteers without CVDs (including cardiomyopathy, CAD, severe arrhythmia or conduction block, valvular disease, congenital heart disease and so on) and other organic or systemic diseases on the comprehensive evaluation by patient history, clinical assessment, ECG and echocardiography.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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