ArticleEBioMedicine2026
Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot.
Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Confounder-Matched Deep Learning on Cardiac CT for the Diagnosis of Tetralogy of Fallot: A Proof of Concept.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly and accurate detection of Tetralogy of Fallot (TOF), along with proper risk stratification management, is critical for improving patient survival and prognosis. We developed an end-to-end automated framework for TOF, aimed at supporting decisions from preoperative diagnosis through postoperative risk prediction.
methodsA total of 1986 filtered participants (1018 healthy controls, 480 TOF mimics, and 488 patients with TOF) from four centres were recruited for the development and validation of DynaTOF, an integrated diagnostic and predictive system. The DynaTOF system comprises: (1) an echocardiographic view classification module built on ResNet-18; (2) a key diameter localisation and calculation module constructed with HRNet and a custom composite loss combining heatmap loss with geometric constraint loss; (3) a multimodal TOF diagnostic module that integrates a ResNet-LSTM-based video feature extractor for echocardiographic videos and a Transformer-based feature extractor for key diameters; (4) a time-aware postoperative prediction module, implemented with a GNN (Graph Neural Network), which estimates postoperative abnormal score dynamics based on preoperative video data, key diameters, surgical type, and specific postoperative time; and (5) a risk-stratification module that employs a Random Forest classifier to differentiate between high- and low-risk patients using the predicted abnormal score series.
findingsThe view classification module achieved AUC values of 0.999, 0.999, 0.998, and 0.998 for classifying the Apical Four-Chamber (A4C), Apical Five-Chamber (A5C), Parasternal Short-Axis (PSAX), and Parasternal Long-Axis (PLAX) views, respectively. The key diameter localisation and calculation module demonstrated R
interpretationDynaTOF enables efficient diagnosis of TOF and provides personalised abnormal dynamics after operation, facilitating early screening and longitudinal monitoring. This system holds promise for improving comprehensive clinical care for infants with TOF.
fundingThis work was supported by Shanghai Municipal Education Commission (No. 2024AIYB010), Fundamental Research Funds for the Central Universities (YG2025LC03), Shanghai Special Fund for Promoting High-Quality Industrial Development - Pilot Industry Innovation Development (AI Special Topic) Project (No. 2025-GZL-RGZN-02078), National Key Research and Development Program of China (2025YFC2511603), Shenzhen Medical Research Special Project clinical multi-center study (No. C2405001), the Science and Technology Commission of Shanghai Municipality (STCSM) (Grant No. 23JS1400700; 24JS2840200; 25JS2850100), the Innovative Research Team of High-Level Local Universities in Shanghai, and the Sanya Science and Technology Special Fund (No. 2022KJCX41).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.