{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T13:15:19Z","timestamp":1788527719582,"version":"build-2803163510"},"reference-count":66,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart defect, and cardiac computed tomography (CT) is increasingly central to its anatomical and pre-procedural assessment. Artificial-intelligence research in TOF is dominated by MRI; deep learning on cardiac CT in congenital heart disease exists but addresses multi-class diagnosis and segmentation, and the one binary TOF-versus-control CT study used slice-level validation without confounder control, and, to our knowledge, no CT study reports controlling the confounding intrinsic to a TOF-versus-control comparison. This confounding is structural: TOF is imaged predominantly in infancy, so a naive classifier can learn age, body size, and acquisition protocol rather than pathology. We develop and internally evaluate a confounder-matched, anatomy-guided deep-learning pipeline for TOF on cardiac CT. Methods: Contrast-enhanced cardiac CT from a single scanner was de-identified and restricted to one reconstruction (FC15 kernel, 0.5 mm), then matched 1:1 on age and sex, yielding 42 TOF and 42 controls (n = 84); controls were children imaged for suspected but excluded cardiovascular disease, so scan indication, unlike age and sex, was not matched. Standardized volumes were decomposed into four fixed sub-volumes positioned to approximate the components of the diagnostic tetrad: malalignment ventricular septal defect (VSD), overriding aorta, right-ventricular outflow tract (RVOT), and right-ventricular hypertrophy (RVH). Whether each sub-volume contains its named target was audited against independent physician region-of-interest annotations. Per region, a 2.5D transfer-learning classifier (ImageNet ResNet18) and a 3D CNN (DenseNet121) were trained with leak-free patient-level five-fold cross-validation and the branches fused. Optimism was assessed by repeated cross-validation and, for model selection, by nested cross-validation with the component subset and operating point chosen inside an inner loop. Discrimination was reported with bootstrap 95% confidence intervals (CIs); AUROCs were compared by DeLong test, with Benjamini\u2013Hochberg correction applied to a seven-member family (the four within-component comparisons, two hybrid-versus-VSD contrasts, and hybrid versus whole-heart) and other comparisons reported uncorrected. Results: Matching removed the age difference (median 0.33 years, IQR 0.17\u20130.92 vs. 0.33, IQR 0.27\u20130.73; p = 0.86) with balanced sex (p = 1.00). The pre-specified four-component hybrid reached AUROC 0.829 (95% CI 0.74\u20130.91); the VSD region alone reached 0.828 (0.74\u20130.91), so the tetrad decomposition did not improve accuracy, and the containment audit shows it does not deliver the intended anatomical interpretability either. The 2.5D model exceeded the 3D CNN for every component (0.769\u20130.828 vs. 0.573\u20130.656; raw DeLong p = 0.007\u20130.037, Benjamini\u2013Hochberg q up to 0.065 under a seven-member family, the weakest comparison (RVH) not surviving correction). Repeated cross-validation gave 0.811 \u00b1 0.026 and nested cross-validation 0.787 \u00b1 0.029; a stronger backbone with multi-phase data, handcrafted radiomics, and a large CT foundation model did not significantly improve on the matched pipeline. Grad-CAM maps were sensitive to both model weights and labels and superior to a centred-blob null in all eight comparisons and significantly so in seven, but not consistently superior to a resolution-matched random attribution, so no localization claim is made. Calibration was imperfect (slope 0.67) and recalibration gave no net gain; at an in-sample Youden threshold sensitivity was 0.93 and specificity 0.64. Occlusion sensitivity on the whole-heart baseline model showed it relies on the physician-marked septal, aortic and right-ventricular sites 1.8\u20134.1 times more than distance-matched surrounding tissue, while gross morphometry alone reached 0.651\u20130.663. Two of the four sub-volumes did not contain their target: the RVOT prior contained the physician annotation in 48.1% of cases and, because the model samples only the central band, excluded it in 99.4%; the RVH box was offset toward the midline, containing the marked target in 43.4% of annotations. Repositioning the priors, leak-free and derived from controls only, did not change discrimination (all p \u2265 0.10), and boxes placed at random positions inside the standardized heart reached 0.765 on average against 0.796 for the published priors, a difference this cohort cannot resolve. Conclusions: As a proof of concept, confounder-matched deep learning can recognize TOF on cardiac CT. Increasing model capacity did not significantly improve on the matched pipeline; the separate contribution of matching itself was not isolated against an unmatched comparator. Given the small, single-centre sample and the absence of external validation, these findings are hypothesis-generating and require external, multi-centre confirmation before any clinical use.<\/jats:p>","DOI":"10.3390\/diagnostics16172814","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T15:08:43Z","timestamp":1788275323000},"page":"2814","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Confounder-Matched Deep Learning on Cardiac CT for the Diagnosis of Tetralogy of Fallot: A Proof of Concept"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3711-2922","authenticated-orcid":false,"given":"Elnur","family":"Karimov","sequence":"first","affiliation":[{"name":"Department of Interdisciplinary Developmental Behavioral Disorders and Integrated Approach, Institute of Graduate Studies in Health Sciences, \u0130stanbul University, 34126 \u0130stanbul, T\u00fcrkiye"},{"name":"Clinic of Pediatric Cardiology, Ba\u015fak\u015fehir \u00c7am and Sakura City Hospital, University of Health Sciences Turkey, 34480 \u0130stanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4488-1642","authenticated-orcid":false,"given":"\u0130nci","family":"Zaim G\u00f6kbay","sequence":"additional","affiliation":[{"name":"Department of Interdisciplinary Developmental Behavioral Disorders and Integrated Approach, Institute of Graduate Studies in Health Sciences, \u0130stanbul University, 34126 \u0130stanbul, T\u00fcrkiye"},{"name":"Department of Artificial Intelligence and Data Engineering, Faculty of Computer and Information Technologies, \u0130stanbul University, 34134 \u0130stanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3607-0620","authenticated-orcid":false,"given":"Serap","family":"Ba\u015f","sequence":"additional","affiliation":[{"name":"Clinic of Radiology, Ba\u015fak\u015fehir \u00c7am and Sakura City Hospital, University of Health Sciences Turkey, 34480 \u0130stanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1750-1172-4-2","article-title":"Tetralogy of Fallot","volume":"4","author":"Bailliard","year":"2009","journal-title":"Orphanet J. Rare Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1007\/s00247-021-05179-5","article-title":"Computed tomography in tetralogy of Fallot: Pre- and postoperative imaging evaluation","volume":"52","author":"Zucker","year":"2022","journal-title":"Pediatr. Radiol."},{"key":"ref_3","first-page":"617","article-title":"Clinical presentation and therapy of tetralogy of Fallot and double-outlet right ventricle","volume":"Volume 1441","author":"Haas","year":"2024","journal-title":"Congenital Heart Diseases"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.diii.2016.01.009","article-title":"Tetralogy of Fallot: Preoperative assessment with MR and CT imaging","volume":"97","author":"Lapierre","year":"2016","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s43044-020-00047-3","article-title":"Multi-detector computed tomography in the assessment of tetralogy of Fallot patients: Is it a must?","volume":"72","author":"Shaaban","year":"2020","journal-title":"Egypt. Heart J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101092","DOI":"10.1016\/j.jocmr.2024.101092","article-title":"Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model","volume":"26","author":"Tilborghs","year":"2024","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102953","DOI":"10.1016\/j.media.2023.102953","article-title":"A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images","volume":"90","author":"Xu","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, T., Zhuang, J., Yuan, H., Huang, M., Cen, J., Jia, Q., Dong, Y., and Shi, Y. (2020). ImageCHD: A 3D computed tomography image dataset for classification of congenital heart disease. Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Springer. Lecture Notes in Computer Science 12264.","DOI":"10.1007\/978-3-030-59719-1_8"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.crad.2026.107404","article-title":"Deep learning-based automated assessment of pulmonary artery indices and surgical approach triage for tetralogy of Fallot from multicenter cardiac computed tomography (CT)","volume":"99","author":"Yao","year":"2026","journal-title":"Clin. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5792975","DOI":"10.1155\/2021\/5792975","article-title":"SOSPCNN: Structurally optimized stochastic pooling convolutional neural network for tetralogy of Fallot recognition","volume":"2021","author":"Wang","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Apostolopoulou, S.C., Manginas, A., Kelekis, N.L., and Noutsias, M. (2019). Cardiovascular imaging approach in pre and postoperative tetralogy of Fallot. BMC Cardiovasc. Disord., 19.","DOI":"10.1186\/s12872-018-0996-9"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e210089","DOI":"10.1148\/ryct.210089","article-title":"Role of CT in the pre- and postoperative assessment of conotruncal anomalies","volume":"4","author":"Kumar","year":"2022","journal-title":"Radiol. Cardiothorac. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1038\/s42256-021-00338-7","article-title":"AI for radiographic COVID-19 detection selects shortcuts over signal","volume":"3","author":"DeGrave","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1136\/heartjnl-2019-316496","article-title":"Deep learning in congenital heart disease imaging: Hope but not haste","volume":"106","author":"Davies","year":"2020","journal-title":"Heart"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1186\/s12938-024-01299-9","article-title":"Self-supervised learning framework application for medical image analysis: A review and summary","volume":"23","author":"Zeng","year":"2024","journal-title":"Biomed. Eng. Online"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e200267","DOI":"10.1148\/ryai.2021200267","article-title":"Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging","volume":"3","author":"Arun","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1038\/s42256-022-00536-x","article-title":"Benchmarking saliency methods for chest X-ray interpretation","volume":"4","author":"Saporta","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e240300","DOI":"10.1148\/ryai.240300","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","year":"2025","journal-title":"Nat. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The Hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_24","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101840","DOI":"10.1016\/j.media.2020.101840","article-title":"Models Genesis","volume":"67","author":"Zhou","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_26","first-page":"3342","article-title":"Transfusion: Understanding Transfer Learning for Medical Imaging","volume":"32","author":"Raghu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Matsoukas, C., Haslum, J.F., Sorkhei, M., S\u00f6derberg, M., and Smith, K. (2022). What Makes Transfer Learning Work for Medical Images: Feature Reuse and Other Factors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.00901"},{"key":"ref_28","unstructured":"Pai, S., Hadzic, I., Bontempi, D., Bressem, K., Kann, B.H., Fedorov, A., Mak, R.H., and Aerts, H.J.W.L. (2025). Vision foundation models for computed tomography. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","article-title":"Visualizing and understanding convolutional networks","volume":"Volume 8689","author":"Zeiler","year":"2014","journal-title":"Computer Vision\u2014ECCV 2014"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ejmp.2020.12.002","article-title":"Selection of optimal cardiac phases for ECG-triggered coronary CT angiography in pediatrics","volume":"81","author":"Azais","year":"2021","journal-title":"Phys. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2528","DOI":"10.1007\/s00247-023-05734-2","article-title":"Partial voxel interpolation to reduce partial volume error of cardiac computed tomography ventricular volumetry in patients with congenital heart disease","volume":"53","author":"Goo","year":"2023","journal-title":"Pediatr. Radiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e15734056292876","DOI":"10.2174\/0115734056292876240327044029","article-title":"Choosing the adaptive cardiac phase for assessing cardiac dimensions using cardiac computed tomography for heart disease","volume":"20","author":"Wang","year":"2024","journal-title":"Curr. Med. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Varma, S., and Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-91"},{"key":"ref_36","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., and Steyerberg, E.W. (2019). Calibration: The Achilles heel of predictive analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"i6","DOI":"10.1136\/bmj.i6","article-title":"Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests","volume":"352","author":"Vickers","year":"2016","journal-title":"BMJ"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","article-title":"A simple, step-by-step guide to interpreting decision curve analysis","volume":"3","author":"Vickers","year":"2019","journal-title":"Diagn. Progn. Res."},{"key":"ref_42","unstructured":"Petsiuk, V., Das, A., and Saenko, K. (2018). RISE: Randomized Input Sampling for Explanation of Black-box Models. arXiv."},{"key":"ref_43","unstructured":"Rong, Y., Leemann, T., Borisov, V., Kasneci, G., and Kasneci, E. (2022). A Consistent and Efficient Evaluation Strategy for Attribution Methods. arXiv."},{"key":"ref_44","first-page":"9505","article-title":"Sanity checks for saliency maps","volume":"31","author":"Adebayo","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"102372","DOI":"10.1016\/j.artmed.2022.102372","article-title":"Explainable multiple abnormality classification of chest CT volumes","volume":"132","author":"Draelos","year":"2022","journal-title":"Artif. Intell. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106292","DOI":"10.1016\/j.ebiom.2026.106292","article-title":"Echocardiography-based intelligent diagnosis and risk stratification management for tetralogy of Fallot","volume":"129","author":"Gao","year":"2026","journal-title":"eBioMedicine"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"457","DOI":"10.3233\/THC-248040","article-title":"Deep learning-based differentiation of ventricular septal defect from tetralogy of Fallot in fetal echocardiography images","volume":"32","author":"Yu","year":"2024","journal-title":"Technol. Health Care"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Nurmaini, S., Rachmatullah, M.N., Sapitri, A.I., Darmawahyuni, A., Tutuko, B., Firdaus, F., Partan, R.U., and Bernolian, N. (2021). Deep learning-based computer-aided fetal echocardiography: Application to heart standard view segmentation for congenital heart defects detection. Sensors, 21.","DOI":"10.3390\/s21238007"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1038\/s41591-021-01342-5","article-title":"An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease","volume":"27","author":"Arnaout","year":"2021","journal-title":"Nat. Med."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neuroimage.2017.06.061","article-title":"Cross-validation failure: Small sample sizes lead to large error bars","volume":"180","author":"Varoquaux","year":"2018","journal-title":"NeuroImage"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1093\/ejcts\/ezy474","article-title":"Tetralogy of Fallot: Morphological variations and implications for surgical repair","volume":"56","author":"Khan","year":"2019","journal-title":"Eur. J. Cardio-Thorac. Surg."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1136\/hrt.2005.061143","article-title":"Tetralogy of Fallot: From fetus to adult","volume":"92","author":"Shinebourne","year":"2006","journal-title":"Heart"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Alipour Symakani, R.S., van Genuchten, W.J., Zandbergen, L.M., Henry, S., Taverne, Y.J.H.J., Merkus, D., Helbing, W.A., and Bartelds, B. (2023). The right ventricle in tetralogy of Fallot: Adaptation to sequential loading. Front. Pediatr., 11.","DOI":"10.3389\/fped.2023.1098248"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.amjcard.2003.11.063","article-title":"Normal right and left ventricular mass development during early infancy","volume":"93","author":"Joyce","year":"2004","journal-title":"Am. J. Cardiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jelectrocard.2023.08.012","article-title":"Right and left ventricular mass development in early infancy: Correlation of electrocardiographic changes with echocardiographic measurements","volume":"81","author":"Joyce","year":"2023","journal-title":"J. Electrocardiol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1007\/s00246-020-02419-7","article-title":"Inferior right ventricular wall thickness by echocardiogram: A novel method of assessing hypertrophy in neonates and infants","volume":"41","author":"Pravder","year":"2020","journal-title":"Pediatr. Cardiol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"16366","DOI":"10.1038\/s41598-026-45943-x","article-title":"Real-time deep learning interpretation of echocardiographic video for automated detection of anatomical features associated with tetralogy of Fallot in pediatric patients: A feasibility study","volume":"16","author":"Kim","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","article-title":"LayerCAM: Exploring Hierarchical Class Activation Maps for Localization","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Abaid, A., Guidone, G., Alsubai, S., Alquahtani, F., Iqbal, T., Sharif, R., Elzomor, H., Bianchini, E., Almagal, N., and Madden, M.G. (2025). 3D CT-Based Coronary Calcium Assessment: A Feature-Driven Machine Learning Framework. arXiv.","DOI":"10.1007\/978-3-032-09569-5_34"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"0319","DOI":"10.34133\/research.0319","article-title":"Development and Validation of a Deep-Learning Network for Detecting Congenital Heart Disease from Multi-View Multi-Modal Transthoracic Echocardiograms","volume":"7","author":"Cheng","year":"2024","journal-title":"Research"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e210315","DOI":"10.1148\/ryai.210315","article-title":"RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning","volume":"4","author":"Mei","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1038\/s41586-026-10181-8","article-title":"Merlin: A computed tomography vision-language foundation model and dataset","volume":"652","author":"Blankemeier","year":"2026","journal-title":"Nature"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ben Khalifa, A., Mili, M., Maatouk, M., Ben Abdallah, A., Abdellali, M., Gaied, S., Ben Ali, A., Lahouel, Y., Bedoui, M.H., and Zrig, A. (2025). Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective Visual Evaluation. Diagnostics, 15.","DOI":"10.3390\/diagnostics15020207"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1007\/s10278-024-01204-9","article-title":"RadImageNet and ImageNet as Datasets for Transfer Learning in the Assessment of Dental Radiographs: A Comparative Study","volume":"38","author":"Okazaki","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5276","DOI":"10.1007\/s00330-022-08664-z","article-title":"Automatic coronary plaque detection, classification, and stenosis grading using deep learning and radiomics on computed tomography angiography images: A multi-center multi-vendor study","volume":"32","author":"Jin","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1007\/s10278-024-01273-w","article-title":"Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population","volume":"38","author":"Chatterjee","year":"2025","journal-title":"J. Imaging Inform. Med."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2814\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T12:29:51Z","timestamp":1788524991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":66,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16172814"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16172814","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]}}}