ArticleHealthcare (Basel, Switzerland)2021
A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 34 citations in OpenAlex.
- Quantitative Ultrasound Imaging and Artificial Intelligence in Neonatal Echocardiography: Methodological Advances, Reproducibility Challenges, and Computational Perspectives.Journal of imaging · 2026Review
- Article
- Artificial intelligence for patent ductus arteriosus-a systematic review.Frontiers in pediatrics · 2026Review
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- Review of Precision Medicine and Diagnosis of Neonatal Illness.Diagnostics (Basel, Switzerland) · 2025Review
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence.Reviews in cardiovascular medicine · 2023Review
- Artificial Intelligence in Congenital Heart Disease: Current State and Prospects.JACC. Advances · 2022Review
- Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System.Healthcare (Basel, Switzerland) · 2021Article
- The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.Cardiology in the young · 2021Article
- The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine.European heart journal. Digital health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.
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.