ArticleSensors (Basel, Switzerland)2021
Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Pooled it
- [Expert consensus on artificial intelligence-assisted monitoring and management of neonatal jaundice in primary medical institutions (2026)].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Construction of a Portable Colorimetric Biosensing Platform Based on the Co-TCPP (Fe)-MOF Nanosheet and Its Application in Glucose Detection.ACS omega · 2026Article
- Neonatal jaundice detection using a vision transformer-based deep learning model.Scientific reports · 2026Article
- Multimodal feature analysis for automated neonatal jaundice assessment using machine learning.JAMIA open · 2025Article
- Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images.Scientific reports · 2025Article
- In situ growth of luminescent d-f MOF nanostructures on bacterial cellulose as an accessible kit for early jaundice diagnosis.Scientific reports · 2025Article
- Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.Scientific reports · 2025Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice.Scientific reports · 2025Article
- Automatic deep learning method for analysis and prediction of neonatal hyperbilirubinemia in magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Management challenges in the treatment of severe hyperbilirubinemia in low- and middle-income countries: Encouraging advancements, remaining gaps, and future opportunities.Frontiers in pediatrics · 2023Review
- Factors to consider before implementing telemedicine protocols to manage neonatal jaundice.Clinical and experimental pediatrics · 2022Article
- On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review.Frontiers in public health · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neonatal jaundice is a common condition worldwide. Failure of timely diagnosis and treatment can lead to death or brain injury. Current diagnostic approaches include a painful and time-consuming invasive blood test and non-invasive tests using costly transcutaneous bilirubinometers. Since periodic monitoring is crucial, multiple efforts have been made to develop non-invasive diagnostic tools using a smartphone camera. However, existing works rely either on skin or eye images using statistical or traditional machine learning methods. In this paper, we adopt a deep transfer learning approach based on eye, skin, and fused images. We also trained well-known traditional machine learning models, including multi-layer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF), and compared their performance with that of the transfer learning model. We collected our dataset using a smartphone camera. Moreover, unlike most of the existing contributions, we report accuracy, precision, recall, f-score, and area under the curve (AUC) for all the experiments and analyzed their significance statistically. Our results indicate that the transfer learning model performed the best with skin images, while traditional models achieved the best performance with eyes and fused features. Further, we found that the transfer learning model with skin features performed comparably to the MLP model with eye features.
Indexed as
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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.