Sofia Bourhim

Introduction: Deep learning has emerged as a promising approach for automated bone age assessment; however, the scientific development of this field has not been comprehensively characterized. Materials and Methods: A bibliometric analysis of studies published between 2015 and 2025 was conducted using data from Web of Science, Scopus, MEDLINE (PubMed), IEEE Xplore, and Google Scholar. A total of 67 studies were included. Bibliometric mapping, network visualization, and knowledge structure analyses were performed using VOSviewer^® (v. 1.6.20) and CiteSpace^® (v. 6.3.R1). Results: Scientific output increased markedly, from 1 publication in 2015–2016 to 25 in 2025. The period 2019–2020 achieved the highest citation impact (1191 citations across 18 studies). China was the leading contributor, followed by the United States, South Korea, and Turkey. Keyword analysis identified convolutional neural networks, the RSNA dataset, and the Greulich–Pyle method as central research themes. Recent studies have increasingly focused on external validation, multicenter datasets, and transformer-based architectures. Conclusions: Research on deep learning for bone age assessment has grown substantially over the past decade. This bibliometric analysis highlights the field’s major contributors, influential publications, and emerging trends, while emphasizing the need for greater validation, dataset diversity, and standardized reporting.

Auxology is the scientific discipline dedicated to the study of human growth and development, with particular emphasis on the patterns, timing, and biological regulation of physical growth from infancy through adolescence. It integrates medical, biological, anthropological, and clinical perspectives to examine both normal and pathological growth processes, including somatic development, skeletal maturation, and pubertal progression. Historically, Auxology evolved from early anthropometric observations and the emergence of statistical reasoning, which established growth as a measurable and variable biological phenomenon. The discovery of X-rays in the late nineteenth century represented a major methodological advance, enabling direct assessment of skeletal maturation and leading to the development of standardized bone age methods, such as the Greulich and Pyle atlas and the Tanner–Whitehouse system. In recent decades, digital imaging and computational approaches, including machine learning and artificial intelligence, have further enhanced the accuracy and reproducibility of growth assessment. Today, auxology constitutes a fundamental scientific framework in pediatric medicine, epidemiology, and public health for understanding human growth as a dynamic, multifactorial, and context-dependent process.

Introduction: Bone-age assessment using posteroanterior left hand–wrist radiographs is indispensable in pediatric endocrinology and forensic age determination. Traditional methods—Greulich–Pyle atlas and Tanner–Whitehouse scoring—are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated bone-age estimation from left PA hand–wrist radiographs. Materials and Methods: A systematic review was carried out and previously registered in PROSPERO (CRD42024619808) in MEDLINE (PubMed), Google Scholar, ELSEVIER (Scopus), EBSCOhost, Cochrane Library, Web of Science (WoS), IEEE Xplore, and ProQuest for original studies published between 2019 and 2024. Two independent reviewers extracted study characteristics and outcomes, assessed methodological quality via the Newcastle–Ottawa Scale, and evaluated bias using ROBINS-E. Results: Seventy-seven studies met inclusion criteria, encompassing convolutional neural networks, ensemble and hybrid models, and transfer-learning approaches. Commercial systems (e.g., BoneXpert^®, Physis^®, VUNO Med^®-BoneAge) achieved mean absolute errors of 2–31.8 months—significantly surpassing Greulich–Pyle and Tanner–Whitehouse benchmarks—and reduced reading times by up to 87%. Common limitations included demographic bias, heterogeneous imaging protocols, and scarce external validation. Conclusions: AI-based approaches have substantially advanced automated bone-age estimation, delivering clinical-grade speed and mean absolute errors below 6 months. To ensure equitable, generalizable performance, future work must prioritize demographically diverse training cohorts, implement bias-mitigation strategies, and perform local calibration against region-specific standards.

Recommender systems (RS) are increasingly leveraging the power of graphs to enhance accuracy. However, we stipulate that existing methods don’t take into consideration the inherent behavior of communities and the interaction between all the sub-groups of the network. In this work, we develop a Deep Graph-based Collaborative Filtering recommender system (DGCF), which incorporates the concept of community profiling and leverages the power of Graph Neural Networks. DGCF utilizes multiple graphs to exploit all types of information from the different user interactions. It extracts the overlapping communities from the homophily user-user graph and also integrates the high-order information from the user-item bipartite graph. We conduct experiments and evaluate the DGCF on the MovieLens datasets(ML-100K and ML-1M), and Douban dataset. Our experiments reveal significant improvements over a number of the latest deep learning models for recommender systems. Results also support that DGCF has the potential to render better recommendations as it extracts deep relationships using the community structure.