Computerized Diagnosis Methods for Pediatric Cardiology: A Review
Bibliographic record
Abstract
Pediatric cardiac disorders include an extensive range of heart conditions in infants, children and carried over to adolescents in certain cases. These disorders may be congenital or acquired and can vary in impacting the life of the pediatric subject which requires complex surgical procedures and certain cases that do not require medical intervention. Congenital heart disease (CHD) is present during birth and acquired cardiac diseases are developed after birth predominantly due to autoimmune responses or infections. Various acquisition techniques help in visualizing the heart, identify disorders and help the physicians to plan for operative procedures. Pediatric Cardiac Screening is one of the crucial techniques to record cardiac activity which has difficulties in acquisition as children tend to move during the procedure. The data obtained from these modalities may suffer from various artifacts which makes the diagnosis difficult for the clinicians. To make the diagnosis easier and artifact free, artificial intelligence plays a vital role. Artificial Intelligence (AI)-based techniques from traditional machine learning (ML) to deep learning (DL) techniques for classification and segmentation of pediatric cardiac signals and images are systematically reviewed. DL based studies have become a choice of research in pediatric healthcare. Support Vector Machines with linear kernels are the most commonly used ML based classifiers in the reviewed papers. DL methods use Convolutional Neural Networks (CNN) as the primary classifier and U-Net architectures are preferred for segmentation studies in the reviewed papers. There are a very few surveys available to present the diagnostics related to pediatric cardiac disorders and this paper tries to bring out the challenges along with the traditional machine learning and emerging deep learning techniques implemented in classification and segmentation of the pediatric cardiac disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".