Search Results (1,591)

Manufacturer datasheets state battery cycle life under conditions that rarely match how cells are used, while public cycling datasets measure degradation under conditions datasheets do not cover. We present a knowledge-graph (KG) system that represents claims, measurements, and independent tests of commercial lithium-ion cells with full provenance, detects claim-versus-measured and claim-versus-claim discrepancies conditioned on the comparability of test conditions, and supports cycle-life prediction with coverage-gated abstention. On the 124-cell Severson dataset under leave-one-policy-group-out cross-validation, graph-derived neighbor features do not significantly improve point prediction over a strong early-cycle baseline (RMSE 135 vs. 141 cycles), but graph coverage provides a statistically significant abstention signal (Spearman $\rho =-0.25$ with prediction error, $p=0.006$) that reduces retained RMSE by roughly 40% at 60% retention, where random abstention does not. Deployed zero-shot on a second cycling study of the same commercial cell, the gate abstained on all 77 cells; the counterfactual confirms every refusal (approximately 83% error had it answered), an error an ungated baseline commits silently. On a third study with commensurable features, the gate’s first partial acceptance (17 of 45 cells) is itself diagnostic: coverage acts partly as a lifetime proxy out of distribution, and five labeled cells halve retained error while leaving that proxy in place—adaptation repairs the predictor, not the selection criterion. A 70B open-weight LLM extracts datasheet claims at $F_1=0.70$ with non-deterministic output even at temperature 0; a deterministic validator with three-run consensus raises this to $F_1=0.78$ with zero unsourced values; on a held-out datasheet, precision and the zero-unsourced-value property transfer while recall falls to 0.34, localizing the extractor’s boundary at table-structured content; row-level table grounding, implemented in response, raises held-out recall to 0.63 with zero hallucinations at a measured precision cost. Reconciling claims across document variants shows that roughly one in three cross-document specification comparisons (14 of 43, three commercial cells) yields a conflict or condition mismatch, twelve involving third-party documents and two internal to a single manufacturer’s own documents. A hand-labeled, condition-annotated gold standard of 103 claims (62 development, 41 held-out; inter-annotator $\kappa =0.74$ on property naming) and a staged, human-gated literature-monitoring pipeline are released with the code. Full article

Background/Objectives: Achondroplasia is the most common skeletal dysplasia and the leading genetic cause of disproportionate short stature. Although its biological basis involves gain-of-function variants in the FGFR3 gene, achondroplasia is a lifelong multisystem disorder associated with neurological, respiratory, orthopedic, otolaryngological, cardiovascular, oral, functional, and psychosocial complications. This narrative review aims to synthesize the evidence on developmental and adaptive functioning, age-specific healthcare needs, multidisciplinary service delivery, transition to adult care, psychosocial well-being, caregiver burden, and patient- and family-centered outcomes in achondroplasia across the lifespan. Methods: A narrative literature review was conducted using PubMed/MEDLINE, Scopus, Web of Science Core Collection, and CINAHL, with Google Scholar used as a supplementary source. Studies published between January 2010 and July 2026 were considered, together with earlier clinically relevant reports. Evidence addressing prenatal and postnatal diagnosis, age-specific manifestations, neurological and respiratory complications, orthopedic and otolaryngological care, cardiometabolic risk, growth monitoring, multidisciplinary management, transition to adult services, disease-modifying therapy, quality of life, and caregiver burden was evaluated. Results: The clinical priorities of achondroplasia change substantially across the lifespan. Infancy is characterized by an increased risk of foramen magnum stenosis, cervicomedullary compression, hypotonia, and sleep-disordered breathing, whereas orthopedic deformities, chronic pain, reduced mobility, spinal stenosis, hearing impairment, obesity, and cardiovascular risk become increasingly relevant during later childhood, adolescence, and adulthood. Early diagnosis, condition-specific imaging, neurological and respiratory surveillance, growth monitoring, and coordinated specialist care are essential for preventing severe complications. Vosoritide has introduced a disease-modifying therapeutic option, but it does not replace comprehensive clinical surveillance, rehabilitation, orthopedic care, psychosocial support, or shared decision-making. Functional limitations, environmental barriers, treatment burden, and caregiver stress contribute substantially to reduced quality of life. Conclusions: Achondroplasia should be managed as a lifelong multisystem condition rather than solely as a disorder of short stature. Standardized surveillance, multidisciplinary coordination, planned transition to adult care, and patient- and family-centered management are essential for improving function, autonomy, long-term health outcomes, and quality of life. Full article

A post-flight analysis of unmanned aerial vehicle (UAV) imagery has the potential to result in a delay in the inspection of source water. This delay can occur when visible debris or changes in the water surface necessitate a prompt response. The present study does not evaluate in-flight operation; rather, it presents a bench-level feasibility assessment of two deployment tasks—broad two-class screening and close-range debris classification—using lightweight YOLO detectors on an edge-AI camera in a host-fed configuration that approximates the timing constraints of a future UAV workflow. The YOLOv8, YOLO11, and YOLO26 models were lightweighted through structural pruning (YOLOv8) or architecture scaling (YOLO11 and YOLO26). These models were then refined through a process of fine-tuning, exported to the camera, and evaluated in terms of several metrics. The metrics encompassed training-environment accuracy, the accuracy of device-returned outputs, round-trip latency, and snapshot-based operating-load estimates. The dataset under consideration is extensive, comprising 4813 training images and 575 validation images, accompanied by 13,051 and 1615 annotations, respectively. The depth-pruned YOLOv8s variant demonstrated a significant reduction in mean round-trip latency, from 426.87 milliseconds to 231.58 milliseconds (45.75%), while the mAP@0.5 metric exhibited a decrease from 0.7018 to 0.6650, and the mAP@0.5:0.95 metric demonstrated a decline from 0.5433 to 0.5290. A class-level analysis reveals that aggregate accuracy is primarily influenced by the weaker floating-debris class, whose AP@0.5 ranges from 0.29 to 0.46, in contrast to the 0.82 to 0.94 range observed for pond/reservoir. In comparison to a matched baseline that was trained for an equivalent number of epochs with the sampler disabled, debris-biased sampling contributes 1.5 ± 0.6 mAP@0.5 points for YOLO11 and 3.6 ± 0.2 points for YOLO26 across three seed-matched pairs. The primary effect of this method is to increase floating-debris recall by 4.7–5.9 percentage points, with a concomitant small reduction in precision. The latency reduction increased the broad-inspection rate by 1.85×, provided approximately 195 milliseconds of idle margin within a 1-hertz cycle, and increased the paired far/near rate by 1.59× with two models resident on the camera. Three-seed repetitions of compact-model fine-tuning yielded 0.6717 ± 0.0033 and 0.6290 ± 0.0028 mAP@0.5. These results express detector compression in terms of operational monitoring capacity rather than model-size reduction alone, while also showing that compression by itself does not resolve the weak-class limitation that governs source-water inspection accuracy. Full article

Thalassemia represents the world’s most prevalent inherited hemoglobin disorder, affecting approximately 4.4 per 10,000 live births globally. Accurate genetic characterization is indispensable both for definitive diagnosis and for lifetime clinical monitoring. The past two decades have witnessed a paradigm shift from conventional protein-based assays toward comprehensive molecular techniques, including next-generation sequencing (NGS) and third-generation (long-read) sequencing, which in turn have enabled reproductive applications such as preimplantation genetic testing for monogenic disease (PGT-M) to identify unaffected embryos before implantation. (1) To systematically evaluate the molecular techniques available for confirming the diagnosis of alpha- and beta-thalassemia, including their diagnostic accuracy, indications, and limitations; (2) to examine how genotype–phenotype correlation and genetic modifier profiling inform clinical prognosis and therapeutic decision-making; and (3) to define evidence-based genetic monitoring parameters for longitudinal follow-up of patients receiving transfusions, iron chelation, and novel curative therapies including gene therapy. A comprehensive narrative review was conducted by systematically searching PubMed/MEDLINE for English-language peer-reviewed articles published between January 2000 and December 2024. Forty-three studies were ultimately included after applying predefined inclusion and exclusion criteria. Quality of included studies was assessed using SANRA (Scale for the Assessment of Narrative Review Articles). HPLC and capillary electrophoresis remain first-line phenotyping tools; DNA-based confirmation is mandatory for complete genotyping. Among known, previously characterized mutations, NGS-based targeted panels achieve > 95% detection sensitivity, but they require MLPA co-testing or long-read sequencing to detect structural variants such as large deletions. Genotype–phenotype prediction is substantially improved, though not rendered fully deterministic, by profiling three major modifier loci: XmnI (Gγ), BCL11A, and HBS1L-MYB. PGT-M using NGS achieves near-complete genotyping accuracy (>99%) with live birth rates of 40–60% per frozen embryo transfer cycle. For patients receiving curative gene therapy (exagamglogene autotemcel/Casgevy), molecular follow-up protocols spanning 15 years are now recommended. Cardiac T2* MRI remains the most reliable non-invasive tool for iron overload follow-up, superior to serum ferritin alone. A tiered, genotype-informed approach—combining HPLC/CE phenotyping, targeted molecular diagnostics, genetic modifier profiling, and periodic re-evaluation—optimizes diagnostic precision and guides individualized management across the thalassemia spectrum. Integration of PGT-M and long-read sequencing into standard care pathways, alongside robust gene therapy follow-up protocols, will define the next era of thalassemia genetics. Full article

Micromachined ultrasonic transducers (MUTs) represent a notable advance in miniaturized sensing, enabling compact, low-power, and complementary metal-oxide-semiconductor (CMOS)-integrated platforms that extend ultrasonic capabilities into wearable, implantable, and edge-computing domains. The integration of artificial intelligence (AI) has introduced new approaches for signal interpretation, adaptive control, and data-driven optimization, enhancing performance in specific areas such as compressed sensing, neural beamforming, and learned image enhancement that complement conventional signal processing. Meanwhile, sensor fusion strategies that combine ultrasonic data with complementary modalities have improved robustness, contextual awareness, and diagnostic accuracy across applications ranging from industrial monitoring to clinical diagnostics. This review provides a comprehensive analysis of this active research area, systematically covering transducer hardware platforms, design methodologies, and intelligent signal processing frameworks. While traditional bulk piezoelectric transducers remain the benchmark for high-power applications, capacitive and piezoelectric micromachined variants offer superior acoustic impedance matching and monolithic CMOS compatibility essential for portable systems. We examine the evolution from deterministic analytical and numerical modeling toward AI-powered inverse design, which enables the discovery of non-intuitive, high-performance geometries beyond human intuition. Furthermore, the integration of machine learning (ML) for signal recovery, image enhancement, and multi-modal sensor fusion is discussed as a pathway to compensate for hardware constraints such as limited aperture, sparse sampling, and low signal-to-noise ratio (SNR), while pointing out that AI technology cannot overcome fundamental physical limits including acoustic attenuation, thermal noise floors, and transduction efficiency boundaries. By synthesizing recent advancements, this review demonstrates how the convergence of classical acoustic physics and data-driven intelligence is guiding the development of of intelligent ultrasonic systems. Full article

Background/Objectives: Antimicrobial resistance in microbial communities is a global health concern that leads to millions of deaths each year. Many bacterial pathogens have resistance to multiple antibiotics. Domestic wastewater treatment facilities are reservoirs for antibiotic-resistant bacteria and resistance genes. Wastewater-based epidemiology surveillance is crucial for monitoring antibiotic resistance genes (ARGs). Türkiye has one of the highest levels of antibiotic resistance with a lack of research on resistomes. This study is a focused reanalysis of publicly available wastewater metagenomes from Türkiye, comparing them to global and other country’s results. Methods: Ten metagenomic data of wastewater treatment from Türkiye were downloaded from NCBI-SRA database. Metagenome assemblies were performed and high-quality metagenome-assembled genomes (HQ-MAGs) were included in the study. Taxonomic annotations and antibiotic resistance profiles were identified in both the metagenome assemblies and HQ-MAGs. Results: A total of 401 different ARGs in 25 antibiotic classes have been identified, including Mcr (including mcr-1, mcr-2, mcr-3 and mcr-5 variants) and optrA. The vanR two-component regulatory system genes for controlling vancomycin antibiotic resistance were one of the most dominant along with other vancomycin resistance genes such as vanA and vanB. A total of 115 HQ-MAGs were obtained with at least eight ARGs. The HQ-MAG with the highest number of resistance genes (58) was found to belong to E. coli. The most frequently encountered resistance genes in HQ-MAGs were the multidrug ABC transporter, vanR, bacA and patA which confer resistance to multidrug, glycopeptide, bacitracin and fluoroquinolone antibiotic groups, respectively. Conclusions: To effectively address the problems of antibiotic resistance outbreaks, comparable AMR surveillance at national and global levels is required for the identification and prioritization of ARGs and resistance genes. This is the first report conducted in Türkiye. Full article

Rare-event detection is a critical task in intelligent risk monitoring systems, where missed minority events may lead to financial loss, security threats, or operational failures. Existing imbalance-handling methods frequently apply one class-level correction and therefore ignore the heterogeneous geometric roles of minority observations. This study presents density–boundary graph reweighting (DBGR), a unified sample-level weighting framework that combines continuous minority sparsity and majority-boundary exposure and regularizes the resulting scores through a minority-only K-nearest-neighbor graph. Its methodological contribution lies in this joint pre-training formulation and in producing classifier-compatible weights, rather than in claiming novelty for density estimation, graph propagation, or weighting individually. Two variants are considered: DBGR-Safe for sparse and relatively safe minority prototypes and DBGR-Danger for sparse boundary observations. Experiments on financial fraud detection, industrial fault diagnosis, and network intrusion detection show recall-oriented gains with XGBoost and reduced variability for the tested LightGBM setting, while the benefit is limited for Random Forest. On Creditcard, DBGR-Danger improves Recall from 0.8223 to 0.8949 and F2 from 0.8410 to 0.8931. On NSL-KDD U2R, focal loss remains better in Recall, F2, and PR-AUC, whereas DBGR-Safe achieves the best F1. DBGR is therefore positioned as a complementary, classifier-compatible strategy rather than a universally superior imbalance-handling method. Full article

Background/Objectives: Highly effective cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy has changed cystic fibrosis (CF) care, but Romanian patient-reported health-related quality of life (HRQoL) data remain limited. This national cross-sectional mixed-methods survey aimed to describe patient- and parent-reported HRQoL in Romanian people with CF (pwCF) aged ≥6 years using age-appropriate Cystic Fibrosis Questionnaire-Revised (CFQ-R) versions, with the respiratory domain as the primary outcome. Methods: The online survey was conducted between February and April 2026. Respondents provided demographic and clinical data, including genotype/F508del status, CFTR modulator status, percent predicted forced expiratory volume in 1 s (ppFEV₁) category, weight, height, and IV antibiotic-treated pulmonary exacerbations in the previous year. CFQ-R scores were summarized descriptively, and exploratory unadjusted associations were assessed using Mann–Whitney U tests and Spearman correlations. Open-ended responses were analyzed descriptively. Results: Of 67 responses, 61 were included: 43 participants aged 6–13 years and 18 aged ≥14 years. Most respondents were currently receiving CFTR modulators (52/61, 85.2%), predominantly elexacaftor/tezacaftor/ivacaftor. Mean CFQ-R Respiratory scores were 74.5 ± 23.1 in parent-proxy questionnaires for children aged 6–13 years, 83.3 ± 11.8 in self-reported questionnaires for children aged 12–13 years, and 69.8 ± 27.4 among respondents aged ≥14 years. Respiratory scores were higher among currently treated respondents and those with at least one F508del variant, and lower among those reporting IV antibiotic-treated exacerbations. Treatment burden remained among the lower-scoring domains. Qualitative responses described perceived respiratory, nutritional, and functional improvements, alongside residual treatment burden, psychosocial challenges, and access-related expectations. Conclusions: This national Romanian mixed-methods survey provides descriptive, exploratory CFQ-R-based HRQoL data in the CFTR modulator era. The findings suggest more favorable respiratory HRQoL among currently treated respondents, while supporting continued multidimensional patient-reported outcome monitoring in Romanian CF care. Full article

The reliable identification of microseismic events is an important prerequisite for microseismic monitoring. However, the complex underground mining environment and limited monitoring resources make it challenging for existing identification algorithms to balance accuracy and efficiency. Therefore, this paper proposes a lightweight microseismic signal classification model, MS-Mamba, based on time–frequency image analysis. This model efficiently integrates global time–frequency dependencies and local detail features through Lightweight Receptive Field Feature Interaction (LRFFI) and uses the embedded DB-Mamba to enhance the modeling of temporal and frequency-domain features. The inverted residual module is also introduced to complement local high-dimensional feature representations, thereby boosting classification performance without substantial computational overhead. To accommodate different computational budgets, three model variants of different scales are designed. Experimental results show that MS-Mamba achieves a favorable balance between accuracy and efficiency across different scales. Among them, MS-Mamba-B achieves the highest accuracy of 98.66%; MS-Mamba-T still reaches 97.94% with only 0.13 GFLOPs of computational cost. These results indicate that MS-Mamba achieves a favorable trade-off between classification accuracy and computational efficiency. Full article

Non-uniform illumination in tunnel environments severely degrades image quality, posing substantial challenges to visual monitoring and intelligent transportation systems. While histogram equalization (HE) remains prevalent due to its computational simplicity, its non-linear pixel transformations frequently induce over-enhancement, artifacts, and structural distortions. This paper proposes Prior-Guided Histogram Equalization (PGHE), a lightweight enhancement framework that integrates conventional HE with Retinex-based illumination priors. Within the Retinex decomposition paradigm, PGHE constructs a contrast illumination map from the ratio between the HE-enhanced image and the original input. A Prior Correction Module (PCM) subsequently refines this map via relative total variation regularization, thereby restoring spatial coherence and alleviating local discontinuities introduced by HE. The corrected map is then applied to the original image to obtain the final enhanced result. Extensive evaluation on the LOL low-light benchmarks and a proprietary tunnel dataset comprising 247 real-world frames shows that PGHE offers favorable trade-offs among contrast enhancement, structural fidelity, and brightness preservation: it is particularly strong in brightness preservation and Entropy, while its PSNR/SSIM on LOL and its NIQE on the tunnel dataset are comparable to, but not always the best among, the compared methods. Furthermore, the proposed PCM functions as a plug-in module that improves existing HE variants with measurable gains in Structural Similarity and perceived naturalness at a modest cost in Absolute Mean Brightness Error. Full article

European urban policy increasingly requires city benchmarks that are people-centred, multidimensional and methodologically defensible, yet perception-based environmental evidence is rarely aggregated without arbitrary weighting. Accordingly, this paper develops and justifies a synthetic index of perceived urban environmental performance (UEP) for European cities. Specifically, using the environmental module of the 2023 Eurostat Urban Audit Perception Survey (UAPS) for 83 Functional Urban Areas (FUAs), four satisfaction indicators (air quality, noise, cleanliness and green spaces) are aggregated with a benefit-of-the-doubt (BoD) composite indicator that assigns each city endogenous, self-favouring weights. Standard, weight-restricted and cross-efficiency variants are estimated, benchmarked against an equal-weight comparator, and embedded in an exploratory spatial data analysis. The results demonstrate that nine cities form the efficient frontier, led by Oulu, Luxembourg and Zurich, while Skopje, Naples and Athens anchor the lower tail. Additionally, a robust North–South gradient emerges, and green space satisfaction is the dominant structural driver of composite scores, partially compensating weak air quality in many cities. The study addresses perception-based BoD benchmarking—uncovering dimension-specific environmental governance deficits masked by national indicators—and complements objective environmental monitoring for European Union (EU) cohesion and climate-neutrality policy. Full article

Stock index forecasting is essential for financial market analysis and risk monitoring, yet it remains challenging because index price series are nonlinear, non-stationary, and driven by heterogeneous market factors. Existing methods remain limited in preserving local price patterns, capturing multi-scale local dependencies, and integrating attention-derived structures with long-range state-space representations. To address these limitations, we propose AG-SSM, an attention-guided state-space model for multi-step stock index forecasting. The model first uses variable-wise patch embedding to construct local semantic units, which are then processed by the AG-SSM architecture for temporal representation learning. Its core block integrates dual-path local attention (DPLA), S4D-based state-space feature generation, attention-guided aggregation (AGA), and gated update (GU). Specifically, DPLA combines sliding and dilated local attention to capture contiguous and sparsely distributed dependencies, while AGA reuses local attention maps to refine state-space features, thereby coupling local market structures with long-range sequential dynamics. Experiments on six stock index datasets (SSE, SZSE, SMESE, SP500, DJIA, and NIKKEI225) under one-, five-, ten-, and fifteen-step forecasting horizons show that AG-SSM achieves the lowest horizon-averaged MAPE on all six datasets while maintaining competitive performance across other metrics and individual horizons. Averaged over five independent runs, the horizon-averaged MAPE values are 1.5466%, 2.2173%, 2.2293%, 1.4373%, 1.2995%, and 1.8738% on the six datasets, respectively. Ablation studies, state-space variant comparisons, sensitivity analyses, and statistical tests further support the effectiveness and robustness of the proposed framework. Full article

Reliable digital twins of complex nonlinear systems require not only high predictive accuracy but also physical consistency, robustness under degraded operating conditions, and explicit uncertainty handling. Purely data-driven models often suffer from poor generalization, unphysical behaviors, and limited interpretability when facing noisy measurements and unseen operating conditions. This paper introduces a knowledge-guided physics-informed hybrid learning framework that integrates recurrent neural networks with Unscented Kalman Filter (UKF) state estimation and embedded thermodynamic constraints within a unified uncertainty-aware architecture. The proposed PI-LSTM-UKF framework achieves competitive predictive accuracy and improved physical consistency relative to the residual-learning hybrids by tightly integrating physics-informed recurrent learning, thermodynamic constraints, and sequential UKF state estimation. While the UKF provides robust recursive correction under noisy measurements during closed-loop operation, the physics-informed Long Short-Term Memory (PI-LSTM) learns nonlinear corrections and long-term dynamics that cannot be captured by the linear model alone. The proposed framework is systematically benchmarked against a hierarchy of seven modeling approaches, including Dynamic Mode Decomposition with control (DMDc), Sparse Identification of Nonlinear Dynamics (SINDy), and residual-learning variants based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). High-fidelity Simscape simulations of a Rankine-cycle steam turbine system are used as a challenging simulation-based case study. Results show that the knowledge-guided hybrid approach achieves competitive predictive accuracy, improved physical consistency, and robust performance under an unseen load profile, severe thermodynamic degradation, valve hysteresis, and substantially elevated sensor noise. The framework provides a promising simulation-based foundation for uncertainty-aware digital twins of nonlinear thermal power systems. Validation using operational plant data remains necessary before its application to real-time monitoring and predictive maintenance. Full article

Unmanned surface vehicle (USV) formations operating under GNSS denial require accurate relative localization using proprioceptive sensors and inter-vehicle ranging. This paper presents an anomaly-aware adaptive extended Kalman filter for four-USV formations using inertial measurements, compass, and ultra-wideband ranging, and systematically characterizes its operating envelope. Observability analysis establishes that S-curve maneuvering achieves structural rank 24, with only global translation unobservable, while straight-line motion leads to a rank deficiency of exactly seven dimensions All four gyroscope biases remain observable under both trajectories. The proposed filter integrates chi-square testing, cumulative sum (CUSUM) detection, and bias drift rate monitoring to trigger coordinated R adaptation and Q-boost mechanisms. Controlled experiments spanning outlier magnitudes and drift rates reveal three performance regimes, clean conditions with equivalent performance across all variants, moderate outliers $[3{\sigma }_d,10{\sigma }_d]$ where the proposed method achieves 4.8–13.4% improvement, and extreme outliers where all robust methods converge. Critically, pure bias drift experiments expose a structural limitation of single-hypothesis, residual domain robustification within the tested drift range—all variants exhibit equivalent performance across the tested drift rates, analytically attributable to Kalman gain partitioning that distributes innovations between position and bias subspaces. The characterized operating envelope establishes that robust mechanisms provide measurable benefits for transient anomalies but encounter hard boundaries under persistent drift conditions, with all variants converging to equivalent performance across the tested range, necessitating multi-hypothesis or constraint-based approaches. Full article

Fms-related receptor tyrosine kinase 3 internal tandem duplication (FLT3-ITD) mutations occur in approximately 20–25% of patients with acute myeloid leukemia (AML) and are associated with increased relapse risk and inferior survival outcomes. Although measurable residual disease (MRD) has become a key prognostic tool for guiding post-remission treatment decisions, FLT3-ITD was historically considered a suboptimal MRD marker because of its subclonal nature, structural heterogeneity and dynamic behavior during disease evolution. In addition, FLT3-ITD has not yet been fully integrated into routine MRD monitoring due to methodological limitations and a lack of standardized workflows. The latest European LeukemiaNet (ELN)-DAVID 2025 recommendations stressed the use of ultra-high sensitivity (UHS) next-generation sequencing (NGS) technologies to detect FLT3-ITD MRD with improved precision, enabling reliable longitudinal tracking of patient-specific clones at very low variant allele frequencies (VAF). Indeed, despite prospective evidence supporting this approach remaining limited, FLT3-ITD-based MRD monitoring is emerging as a clinically relevant prognostic indicator, contributing to the identification of patients at increased risk of relapse and refining risk stratification, while also informing therapeutic decision-making, particularly in the peri-transplant setting. The present review summarizes the biological underpinnings of FLT3-ITD mutated (FLT3-ITD^mut) AML, discusses the methodological challenges of MRD detection, and critically evaluates the evolving role of MRD in refining relapse prediction, supporting post-remission therapy tailoring, and contributing to a harmonized framework for FLT3-ITD^mut AML management. Full article