AIMC Topic: Child
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Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.
Scientific reports
Jul 2, 2025
Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of...
The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms.
Scientific reports
Jul 2, 2025
Hematoporphyrin monomethyl ether-photodynamic therapy (HMME-PDT) is a safe and effective treatment for port-wine stain (PWS). Comprehensive methods for predicting HMME-PDT efficacy based on clinical factors are lacking. This study aims to develop and...
Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia.
Scientific reports
Jul 1, 2025
Mitochondrial dysfunction is crucial in the pathogenesis and drug resistance of pediatric T-cell acute lymphoblastic leukemia (T-ALL), a malignant hematological disorder with unrestrained proliferation of immature T-cells. Therefore, the primary obje...
A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.
Scientific reports
Jul 1, 2025
Segmental/lobar pneumonia in children following Mycoplasma pneumoniae (MP) infection has a significant threat to the children's health, so early recognition of MP infection is critical to reduce the severity and improve the prognosis of segmental/lob...
Closed circuit artificial ıntelligence model named morgaf for childhood onset systemic lupus erythematosus diagnosis.
Scientific reports
Jul 1, 2025
Systemic Lupus Erythematosus (SLE) is a chronic, autoimmune disease characterized by multiple organ involvement and autoantibodies, and its diagnosis is not easy in clinical practice. Pediatric SLE (pSLE) is diagnosed using the SLICC 2012 criteria fo...
Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.
Scientific reports
Jul 1, 2025
Segmenting abnormalities is a leading problem in medical imaging. Using machine learning for segmentation generally requires manually annotated segmentations, demanding extensive time and resources from radiologists. We propose a weakly supervised ap...
Predicting autism from written narratives using deep neural networks.
Scientific reports
Jul 1, 2025
Despite the heterogeneity of language and communication abilities within the autistic population, challenges associated with the pragmatic (social) use of speech remain consistently observable across the entire spectrum of autism. Therefore, the stud...
GenAI exceeds clinical experts in predicting acute kidney injury following paediatric cardiopulmonary bypass.
Scientific reports
Jul 1, 2025
The emergence of large language models (LLMs) opens new horizons to leverage, often unused, information in clinical text. Our study aims to capitalise on this new potential. Specifically, we examine the utility of text embeddings generated by LLMs in...
Targeted metabolomics reveals bioactive inflammatory mediators from gut into blood circulation in children with NAFLD.
NPJ biofilms and microbiomes
Jul 1, 2025
Altered gut metabolites are important for the inflammatory progression in children with NAFLD. Fecal and plasma samples were collected from 145 subjects including 53 non-alcoholic fatty liver (NAFL), 39 nonalcoholic steatohepatitis (NASH) and 53 obes...
Personalized azithromycin treatment rules for children with watery diarrhea using machine learning.
Nature communications
Jul 1, 2025
We use machine learning to identify innovative strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit. Using data from a randomized trial of azithromycin for watery diarrhea (NCT03130114), we develop per...
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