Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot

Figures

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  Fig. 1. Overall construction process of DynaTOF. a. Data acquisition and processing. The study included participants with healthy controls with no cardiac abnormalities, those mimicking TOF, and those diagnosed with TOF. Following cohort filtering (see Fig. 2), multi-view echocardiographic videos were obtained for each participant. A View Classification and Selection module was developed to automatically identify and extract four standard echocardiographic views: Apical 4-Chamber (A4C), Apical 5-Chamber (A5C), Parasternal Short Axis (PSAX), and Parasternal Long Axis (PLAX). Subsequently, clinicians annotated the left and right ventricular inner diameters at end-systole and end-diastole on the A4C view, and the main pulmonary artery diameter on the PSAX view. b. Core modules of DynaTOF. All A4C and PSAX views were processed through the Key Inner Diameter Localisation and Calculation module (see Fig. 3a), resulting in the extraction of five key diameters: LVDs (left ventricular diameter at end-systole), LVDd (left ventricular diameter at end-diastole), RVDs (right ventricular diameter at end-systole), RVDd (right ventricular diameter at end-diastole), and MPAD (main pulmonary artery…

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  Fig. 2. Cohort filtering process. Participants aged between 1 and 12 months who underwent echocardiographic examinations and were either confirmed to have no cardiac abnormalities or diagnosed with TOF or TOF mimics were included in the study. A total of 3516 healthy controls, 497 TOF mimics and 537 TOF participants were recruited from Hospital 1, while 1263 healthy controls, 139 TOF mimics and 132 TOF participants were recruited across Hospitals 2, 3, and 4. Participants were excluded if their echocardiographic videos had a resolution lower than 256 × 256 pixels, contained less than one complete cardiac cycle, or lacked any of the four standard views: A4C, A5C, PSAX, or PLAX. After screening, 811 healthy controls, 368 TOF mimics and 395 TOF participants from Hospital 1, and 207 healthy controls, 112 TOF mimics and 93 TOF participants from Hospitals 2, 3, and 4 were retained, with all four qualified echocardiographic views available. Among the TOF participants, 175 cases from Hospital 1 and 49 individuals from Hospitals 2, 3, and 4 had undergone surgical repair for TOF and had received at least one postoperative echocardiographic follow-up that included all four qualified views.

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  Fig. 3. The architecture of DynaTOF's core modules. a. Key Inner Diameter Localisation and Calculation module. This module employs a multi-layer keypoint detection architecture based on HRNet22 to progressively localise the start and end points of LVDd through multi-scale feature representations. These keypoints are then converted into diameter values using a built-in inner diameter calculation block. The same process is applied to compute the values of LVDs, RVDs, RVDd, and MPAD. b. TOF Diagnosis module. This module consists of two branches: a video branch and a diameter branch. In the video branch, echocardiographic videos of the four standard views are first passed through the frame feature extraction block to obtain spatial features for each frame. These features are then processed by a Long Short-Term Memory (LSTM) network to capture and integrate temporal dependencies, resulting in a comprehensive representation for each view. These representations are subsequently used to predict whether a patient has TOF. In the diameter branch, the five inner diameter values are first transformed via a Multilayer Perceptron (MLP) to expand the feature representation. The expanded features are…

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  Fig. 4. DynaTOF's results for view classification and diameter localisation. a. ROC curves for classifying the A4C, A5C, PSAX, and PLAX echocardiographic views. b. Results of key inner diameter localisation and calculation, along with comparisons to ground truth. From left to right, examples of LVD, RVD, and MPAD are shown. Green indicates the ground truth, and orange indicates the predicted result. c. PCK (Percentage of Correct Keypoints) curves for the localisation of key inner diameter endpoints. Each point on the curve represents the proportion (y-axis) of predicted keypoints that are considered correctly localised when their distance from the ground truth keypoints is less than a given threshold (x-axis). d. Scatter plots comparing predicted and actual inner diameter lengths for LVD, RVD, and MPAD.

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  Fig. 5. DynaTOF's results for TOF diagnosis and postoperative prediction. a. A comparison of the performance metrics for TOF diagnosis under different modalities. b. Scatter plot of predicted versus real risk values for patients with TOF across different postoperative time points. c. ROC curve for TOF diagnosis under different modalities; “Normal” indicates healthy controls. d. Temporal trend of TOF's postoperative abnormal scores. Patients are stratified into high-risk (red) and low-risk (blue) groups based on the defined endpoint events. e. ROC curve for classifying postoperative patients into high-risk and low-risk groups based on predicted postoperative abnormal score series.

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  Fig. 6. The application pipeline of DynaTOF. First, echocardiographic videos were collected from infants who underwent echocardiography examinations. These videos were processed by the View Classification and Selection module, which selects A4C and PSAX views for input into the Key Inner Diameter Localisation and Calculation module, and A4C, A5C, PSAX, and PLAX views for input into the Video Feature Extraction module. Features extracted by these two modules were then fed into the TOF Diagnosis module to determine whether the patient has TOF. For patients diagnosed with TOF mimics, the detailed diagnosis and treatment are needed. For patients diagnosed with TOF, the Time-aware Postoperative Prediction module was used to predict postoperative risk trajectories prior to surgery. These predicted risk dynamics were further analysed by the Risk Stratification module, which classifies patients into high-risk and low-risk groups. Infants identified as high-risk were then placed under intensive surveillance for closer postoperative monitoring.