ArticleJIMD reports2022
Opportunities and challenges in machine learning-based newborn screening-A systematic literature review.
Article in JIMD reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Preferences, attitudes and views regarding genetic newborn screening (gNBS) for rare diseases: a systematic review of the literature and synthesis from 2009 to 2022.Orphanet journal of rare diseases · 2026Pooled it
- Machine Learning-Based Risk Stratification Tool for Hearing Loss in High-Risk Neonates.The Laryngoscope · 2026Article
- Enhancing TSH-based congenital hypothyroidism screening using machine learning and resampling algorithms.BMC medical informatics and decision making · 2025Article
- Newborn screening for central congenital hypothyroidism: past, present and future.European thyroid journal · 2025Review
- Newborn screening and the screening laboratory: past, present and future.European thyroid journal · 2025Review
- Digital-Tier Strategy Improves Newborn Screening for Glutaric Aciduria Type 1.International journal of neonatal screening · 2024Article
- Wilson and Jungner Revisited: Are Screening Criteria Fit for the 21st Century?International journal of neonatal screening · 2024Review
- Current Status of Newborn Bloodspot Screening Worldwide 2024: A Comprehensive Review of Recent Activities (2020-2023).International journal of neonatal screening · 2024Review
- Paving the way in implementation of SCID newborn screening in developing nations: feasibility study and strategies to move forward in Malaysia.Frontiers in immunology · 2024Article
- The significance of machine learning in neonatal screening for inherited metabolic diseases.Frontiers in pediatrics · 2024Article
- Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.Turkish journal of medical sciences · 2024Article
- Article
- Multivariate Independent Component Analysis Identifies Patients in Newborn Screening Equally to Adjusted Reference Ranges.International journal of neonatal screening · 2023Article
- Validation of a targeted metabolomics panel for improved second-tier newborn screening.Journal of inherited metabolic disease · 2023Article
- Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria.Metabolites · 2023Article
- dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening.International journal of neonatal screening · 2022Article
- Newborn screening for neurodevelopmental diseases: Are we there yet?American journal of medical genetics. Part C, Seminars in medical genetics · 2022Article
- Opportunities and challenges in machine learning-based newborn screening-A systematic literature review.JIMD reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development and continuous optimization of newborn screening (NBS) programs remains an important and challenging task due to the low prevalence of screened diseases and high sensitivity requirements for screening methods. Recently, different machine learning (ML) methods have been applied to support NBS. However, most studies only focus on single diseases or specific ML techniques making it difficult to draw conclusions on which methods are best to implement. Therefore, we performed a systematic literature review of peer-reviewed publications on ML-based NBS methods. Overall, 125 related papers, published in the past two decades, were collected for the study, and 17 met the inclusion criteria. We analyzed the opportunities and challenges of ML methods for NBS including data preprocessing, classification models and pattern recognition methods based on their underlying approaches, data requirements, interpretability on a modular level, and performance. In general, ML methods have the potential to reduce the false positive rate and identify so far unknown metabolic patterns within NBS data. Our analysis revealed, that, among the presented, logistic regression analysis and support vector machines seem to be valuable candidates for NBS. However, due to the variety of diseases and methods, a general recommendation for a single method in NBS is not possible. Instead, these methods should be further investigated and compared to other approaches in comprehensive studies as they show promising results in NBS applications.
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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.