Ultrasound Radiomics in Pediatric Imaging: Current Applications, Challenges, and Future Directions Toward Clinical Implementation

Ultrasound is widely used in pediatric imaging because it is safe, portable, real-time, and free of ionizing radiation, but interpretation remains qualitative and operator-dependent. Ultrasound radiomics can extract quantitative features from standard grayscale images, providing potential biomarkers of tissue patterns not readily apparent on visual assessment. This structured narrative review summarizes pediatric ultrasound radiomics applications across organ systems and key barriers to clinical translation. Current evidence suggests promise in neonatal brain injury, neurodevelopmental assessment, liver disease staging, renal characterization, oncologic risk stratification, and emerging lung and muscle applications. However, most studies remain limited by retrospective single-center designs, small cohorts, acquisition variability, segmentation inconsistency, and limited external validation. Future progress will require standardized workflows, prospective multicenter validation, and clinically interpretable integration into decision-support systems.

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Publication Details

Journal
Diagnostics
Published
2026-05-28
DOI
https://doi.org/10.3390/diagnostics16111669
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Ultrasound Radiomics in Pediatric Imaging: Current Applications, Challenges, and Future Directions Toward Clinical Implementation

Maria Mezher, Sean Schoeman, Maryam Al‐Hasani, L Sultan et al.
Diagnostics
Radiomics and Machine Learning in Medical Imaging
article

Ultrasound Radiomics in Pediatric Imaging: Current Applications, Challenges, and Future Directions Toward Clinical Implementation

Maria Mezher, Sean Schoeman, Maryam Al‐Hasani, L Sultan, Hansel J. Otero, Mohannad Elgamal
article en

Abstract

Ultrasound is widely used in pediatric imaging because it is safe, portable, real-time, and free of ionizing radiation, but interpretation remains qualitative and operator-dependent. Ultrasound radiomics can extract quantitative features from standard grayscale images, providing potential biomarkers of tissue patterns not readily apparent on visual assessment. This structured narrative review summarizes pediatric ultrasound radiomics applications across organ systems and key barriers to clinical translation. Current evidence suggests promise in neonatal brain injury, neurodevelopmental assessment, liver disease staging, renal characterization, oncologic risk stratification, and emerging lung and muscle applications. However, most studies remain limited by retrospective single-center designs, small cohorts, acquisition variability, segmentation inconsistency, and limited external validation. Future progress will require standardized workflows, prospective multicenter validation, and clinically interpretable integration into decision-support systems.

DiagnosticsVol. 16(11)
Children's Hospital of Philadelphia (US), University of Pennsylvania (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 5%
Radiomics and Machine Learning in Medical Imaging
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