ArticleMedical & biological engineering & computing2022
Camera fusion for real-time temperature monitoring of neonates using deep learning.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Spatial temperature monitoring of preterm infants using a multi-modal camera setup.Medical & biological engineering & computing · 2026Article
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Neonatal pose estimation in the unaltered clinical environment with fusion of RGB, depth and IR images.NPJ digital medicine · 2025Article
- Revolutionizing Neonatal Care: A Comprehensive Review of Intact Cord Resuscitation in Newborns.Cureus · 2024Review
- A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning.BMC veterinary research · 2024Article
- Emerging innovations in neonatal monitoring: a comprehensive review of progress and potential for non-contact technologies.Frontiers in pediatrics · 2024Review
- Challenges and prospects of visual contactless physiological monitoring in clinical study.NPJ digital medicine · 2023Review
- Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study.BMC pediatrics · 2023Article
- Multi-modal body part segmentation of infants using deep learning.Biomedical engineering online · 2023Article
- Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning-based algorithms and camera modalities such as RGB and infrared thermography can improve the development of cable-free methods for the extraction of vital parameters. In this study, a real-time approach for local extraction of temperatures on the body surface of neonates using a multi-modal clinical dataset was implemented. Therefore, a trained deep learning-based keypoint detector was used for body landmark prediction in RGB. Image registration was conducted to transfer the RGB points to the corresponding thermographic recordings. These landmarks were used to extract the body surface temperature in various regions to determine the central-peripheral temperature difference. A validation of the keypoint detector showed a mean average precision of 0.82. The registration resulted in mean absolute errors of 16.4 px (8.2 mm) for x and 22.4 px (11.2 mm) for y. The evaluation of the temperature extraction revealed a mean absolute error of 0.55 [Formula: see text]C. A final performance of 31 fps was observed on the NVIDIA Jetson Xavier NX module, which proves real-time capability on an embedded GPU system. As a result, the approach can perform real-time temperature extraction on a low-cost GPU module.
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.