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Applied Sciences
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Artificial Intelligence for Pediatric Monitoring, Diagnosis, and Treatment
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Artificial Intelligence for Pediatric Monitoring, Diagnosis, and Treatment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 3038

Special Issue Editors

Dr. Stefano Nobile

E-Mail Website
Guest Editor
Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
Interests: retinopathy of prematurity; nanotechnology; ultrasound and lung function tests; neonatology; neonatal resuscitation; mechanical ventilation; inflammatory bowel disease
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Perri

E-Mail Website
Guest Editor
Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
Interests: lung ultrasonography; echoencephalography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to this Special Issue on “Artificial Intelligence for Pediatric Monitoring, Diagnosis and Treatment”.

The term “artificial intelligence” (AI) refers to the general ability of computing algorithms to emulate human decision-making, while machine learning (ML) is a subdivision of AI that includes techniques that enable machines to learn from data without explicit programming. Over recent decades, both have deeply influenced personalized diagnostics and therapeutics, drug discovery, and medical imaging. These approaches have the potential to significantly improve our understanding of diseases and of therapeutic efficacy in both children and infants. Algorithms are employed in ML for the classification of data and to make predictions, while neonatal AI applications involve neuromonitoring, prediction of respiratory conditions such as respiratory distress syndrome, chronic lung disease, sepsis, retinopathy of prematurity, vital sign monitoring, jaundice, and more. Pediatric AI applications include analysis of medical imaging to improve the diagnosis of an array of conditions, and prediction of infections and chemotherapy-induced complications.

In the near future, AI may be able to improve clinical care and potentially transform healthcare resource organization, and there is a growing interest in the medical field regarding the modern applications of artificial intelligence in neonatal and pediatric care. In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of artificial Intelligence for pediatric monitoring, diagnosis and treatment, encompassing both theoretical and experimental studies, as well as comprehensive reviews and survey papers.

Dr. Stefano Nobile
Dr. Alessandro Perri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pediatric monitoring
  • pediatric diagnosis
  • pediatric treatment
  • machine learning
  • artificial intelligence
  • pediatric AI
  • artificial intelligence in neonatology
  • neonatal medicine

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Published Papers (3 papers)

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Research

29 pages, 2312 KiB  
Article
A Study of Immunoenzymatic Parameters in Pediatric Ischemic Stroke as a Contribution to More Efficient Pediatric Monitoring and Diagnosis
by Mariana Sprincean, Ludmila Sidorenko, Serghei Sprincean, Svetlana Hadjiu and Niels Wessel
Appl. Sci. 2025, 15(6), 3152; https://doi.org/10.3390/app15063152 - 14 Mar 2025
Viewed by 423
Abstract
Introduction: Pediatric ischemic stroke (IS) is a rare but severe neurological emergency, with an incidence of 2–13 per 100,000. Most cases occur in the prenatal period or early infancy. Integrating artificial intelligence (AI) into clinical practice may enhance the early recognition of stroke. [...] Read more.
Introduction: Pediatric ischemic stroke (IS) is a rare but severe neurological emergency, with an incidence of 2–13 per 100,000. Most cases occur in the prenatal period or early infancy. Integrating artificial intelligence (AI) into clinical practice may enhance the early recognition of stroke. This pilot study aimed to identify immunoenzymatic markers as early predictors of pediatric IS, supporting machine learning applications. Materials and Methods: A prospective study (2017–2019) in Moldova included 53 children with IS and 53 healthy controls. The serum levels of vascular endothelial growth factor (VEGF), ciliary neurotrophic factor (CNTF), the S100B protein, CD105 (endoglin), antiphospholipid antibodies (APAs), and interleukin-6 (IL-6) were measured using ELISA during the acute phase. Results: Endoglin levels were significantly lower in IS patients (2.06 ± 0.012 ng/mL) vs. controls (2.51 ± 0.071 ng/mL) (p < 0.001). S100B levels were elevated (0.524 ± 0.0850 ng/mL vs. 0.120 ± 0.0038 ng/mL, p < 0.01). VEGF levels were significantly increased (613.41 ± 39.299 pg/mL vs. 185.50 ± 12.039 pg/mL, p < 0.001), correlating with the infarct size and disease severity. CNTF levels were also higher (7.84 ± 0.322 pg/mL vs. 5.29 ± 0.067 pg/mL, p < 0.001). APA levels were elevated (1.37 ± 0.046 U/mL vs. 0.92 ± 0.021 U/mL, p < 0.001). IL-6 levels were 10 times higher in IS patients (22.02 ± 2.143 pg/mL vs. 2.38 ± 0.302 pg/mL, p < 0.001), correlating with the infarct size (p < 0.004) and neurological prognosis at six months (p < 0.01). Conclusions: IL-6, VEGF, CNTF, S100B, CD105, and APAs are key markers in pediatric IS, reflecting neuroinflammation, vascular disruption, and the long-term prognosis. Their integration into AI-driven diagnostic models may improve early stroke detection and pediatric monitoring. Full article
(This article belongs to the Special Issue Artificial Intelligence for Pediatric Monitoring, Diagnosis, and Treatment)
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14 pages, 2766 KiB  
Article
Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
by Wajahat Nawaz, Kevin Albert, Philippe Jouvet and Rita Noumeir
Appl. Sci. 2025, 15(3), 1512; https://doi.org/10.3390/app15031512 - 2 Feb 2025
Cited by 1 | Viewed by 840
Abstract
Medical instruments are essential in pediatric intensive care units (PICUs) for measuring respiratory parameters to prevent health complications. However, the assessment of acute respiratory distress (ARD) is still conducted through intermittent visual examination. This process is subjective, labor-intensive, and prone to human error, [...] Read more.
Medical instruments are essential in pediatric intensive care units (PICUs) for measuring respiratory parameters to prevent health complications. However, the assessment of acute respiratory distress (ARD) is still conducted through intermittent visual examination. This process is subjective, labor-intensive, and prone to human error, making it unsuitable for continuous monitoring and early detection of deterioration. Previous studies have proposed solutions to address these challenges, but their techniques rely on color information, the performance of which can be influenced by variations in skin tone and lighting conditions. We propose leveraging multi-modality data to address these limitations. Our method integrates color and depth data using deep convolutional neural networks with a late feature fusion scheme. We train and evaluate our model on a dataset of 153 patients with respiratory illnesses, 86 of whom have ARD of varying severity levels. Experimental results demonstrate that multi-modality data combined with simple late fusion techniques are more effective with limited data, offering higher confidence scores compared to using color information alone. Our approach achieves an accuracy of 85.2%, a precision of 86.7%, a recall of 85.2%, and an F1 score of 85.8%. These findings suggest that multi-modality data provide a promising solution for improving ARD detection accuracy and confidence in clinical settings. Full article
(This article belongs to the Special Issue Artificial Intelligence for Pediatric Monitoring, Diagnosis, and Treatment)
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9 pages, 1556 KiB  
Article
A Comparison of Automatic Bone Age Assessments between the Left and Right Hands: A Tool for Filtering Measurement Errors
by Kyu-Chong Lee, Chang Ho Kang, Kyung-Sik Ahn, Kee-Hyoung Lee, Jae Joon Lee, Kyu Ran Cho and Saelin Oh
Appl. Sci. 2024, 14(18), 8135; https://doi.org/10.3390/app14188135 - 10 Sep 2024
Viewed by 1092
Abstract
This study aimed to investigate whether the left and right hands yield the same bone age using the automated bone age assessment (BAA) system and proposed the right-hand BAA as a tool for filtering out measurement errors. The Bland–Altman, Passing–Bablok, and Spearman correlation [...] Read more.
This study aimed to investigate whether the left and right hands yield the same bone age using the automated bone age assessment (BAA) system and proposed the right-hand BAA as a tool for filtering out measurement errors. The Bland–Altman, Passing–Bablok, and Spearman correlation coefficients were analyzed to compare the automated BAA results for each hand. The absolute difference between each hand obtained by the model (ADBH model) was calculated. The mean absolute difference (MAD) was estimated between the automatic BAA results for each hand and the reference standard. The mean of the ADBH model was 0.23 ± 0.19 years; 92.2% of the participants showed an ADBH model result of <0.5 years. The Passing–Bablok regression analysis revealed an excellent overall correlation between the BAAs of both hands. Of the total cases, 59 participants showed an ADBH model result >0.5 years, with a MAD between the model and the reference standard of 0.409 years for the left hand and 0.424 years for the right hand; both MADs were higher than those of previous studies using the same model. Given the excellent overall correlation of the BAA between both hands using the model, the high ADBH model value may indicate BAA measurement errors and serve as a cue for manual supervision. Full article
(This article belongs to the Special Issue Artificial Intelligence for Pediatric Monitoring, Diagnosis, and Treatment)
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