ArticleWireless communications & mobile computing2021
SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot recognition.
Article in Wireless communications & mobile computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- Confounder-Matched Deep Learning on Cardiac CT for the Diagnosis of Tetralogy of Fallot: A Proof of Concept.Diagnostics (Basel, Switzerland) · 2026Article
- Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot.EBioMedicine · 2026Article
- Artificial Intelligence in Tetralogy of Fallot: From Prenatal Diagnosis to Lifelong Management: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Robust Automatic Grading of Blunt Liver Trauma in Contrast-Enhanced Ultrasound Using Label-Noise-Resistant Models.Journal of imaging informatics in medicine · 2025Article
- Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
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- Joint coordinate attention mechanism and instance normalization for COVID online comments text classification.PeerJ. Computer science · 2024Article
- SUScientific reports · 2023Article
- Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals.Sensors (Basel, Switzerland) · 2022Article
- Extraction of Music Main Melody and Multi-Pitch Estimation Method Based on Support Vector Machine in Big Data Environment.Journal of environmental and public health · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Aim: This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). Methods: Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. Results: The results on ten runs of 10-fold cross-validation show that our SOSPCNN model yields a sensitivity of 92.25±2.19, a specificity of 92.75±2.49, a precision of 92.79±2.29, an accuracy of 92.50±1.18, an F1 score of 92.48±1.17, an MCC of 85.06±2.38, an FMI of 92.50±1.17, and an AUC of 0.9587. Conclusion: The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.
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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.