Diagnostics最新文献

Background and Objective: Adolescent bipolar disorder (BD) has substantial symptom overlaps with other psychiatric disorders. Identifying its distinctive candidate neuroimaging markers may be helpful for exploratory early differentiation and to inform future translational studies after independent validation. Methods: This cross-sectional study enrolled adolescents with BD and age- and sex-matched healthy controls. Assessments included clinical/behavioral scales and an emotional Go/NoGo task-based fMRI (Go trials require a response; NoGo trials require response inhibition) acquired across three mood states (depression, mania, and remission) and matched controls. We applied several conventional machine learning classifiers to task-fMRI data to classify BD versus healthy controls and to identify the most relevant neuroimaging predictors. Results: A total of 43 adolescents with BD (15 in remission, 11 with depression, and 17 with mania) and 43 matched healthy controls were included. Under the Go - NoGo condition, activation-derived features in the remission state showed the strongest discrimination, with RF achieving the best performance (accuracy = 94.29%, AUC = 98.57%). These findings suggest that task-evoked functional alterations may remain detectable during remission. In addition, activation patterns in regions within the limbic system, prefrontal cortex, and default mode network were significantly correlated with clinical scales and behavioral measures implicating these regions in emotion regulation and cognitive functioning in adolescents with BD. Conclusions: This study showed that adolescents with BD during remission without manic and depressive symptoms may still have aberrant neural activity in the limbic system, prefrontal cortex, and default mode network, which may serve as a potential candidate neuroimaging signature of adolescent BD.

Background/Objectives: End-stage renal disease (ESRD) is characterized by profound and progressive microvascular dysfunction that contributes significantly to systemic morbidity. Because the retinal and renal microcirculations share structural and physiological similarities, optical coherence tomography (OCT) and OCT angiography (OCTA) have emerged as promising tools for detecting ocular microvascular changes that may parallel systemic vascular injury. This systematic review aimed to consolidate evidence on chronic retinal and choroidal alterations in ESRD as assessed by OCT and OCTA. Methods: A systematic search of PubMed/MEDLINE (inception-June 2025) was performed using combinations of terms related to OCT, OCTA, ESRD, and hemodialysis. After removing duplicates and screening titles, abstracts, and full texts, we included clinical studies involving adults with ESRD or undergoing dialysis that reported chronic or baseline OCT/OCTA findings. Non-English publications, editorials, conference abstracts, case reports, and studies limited to acute pre-/post-dialysis changes were excluded. Seventeen studies met eligibility criteria. Acute findings were summarized narratively only when no chronic data existed for a specific parameter but were not incorporated into the primary synthesis. Results: Across eligible studies, chronic structural and perfusion abnormalities were consistently reported, including thinning of the retinal nerve fiber and ganglion cell layers, reduced macular and peripapillary vascular densities, enlarged foveal avascular zones, and decreased choroidal thickness. These alterations aligned with markers of disease severity and systemic microvascular burden. Conclusions: Retinal imaging reveals reproducible chronic microvascular changes in ESRD and may serve as an accessible adjunct for systemic vascular assessment. We highlight the potential significance of retinal vascular screening in this population and the need for more standardized imaging protocols to support the effective integration of retinal biomarkers into CKD diagnostic and monitoring strategies.

Osteoarthritis (OA) is a prevalent joint disorder characterized by symptoms such as pain and stiffness, often leading to loss of function and disability. Knee osteoarthritis (KOA) represents the most prevalent type of osteoarthritis. KOA is usually detected using X-ray radiographs of the knee; however, the classification of disease severity remains subjective and varies among clinicians, motivating the need for automated assessment methods. In recent years, deep learning-based approaches have shown promising performance for KOA classification tasks, particularly when applied to structured imaging datasets. This review analyzes convolution neural network (CNN)-based approaches reported in the literature and compares their performance across multiple criteria. Studies were identified through systematic searches of IEEE Xplore, SpringerLink, Elsevier (ScienceDirect), Wiley Online Library, ACM Digital Library, and other sources such as PubMed and arXiv, with the last search conducted in March 2025. The review examines datasets used (primarily X-ray and MRI), preprocessing strategies, segmentation techniques, and deep learning architectures. Reported classification accuracies range from 61% to 98%, depending on the dataset, imaging modality, and task formulation. Finally, this paper highlights key methodological limitations in existing studies and outlines future research directions to improve the robustness and clinical applicability of deep learning-based KOA classification systems.

Background/Objectives: Hydroxychloroquine (HCQ) is widely used in the treatment of autoimmune rheumatologic diseases due to its immunomodulatory and anti-inflammatory properties. However, long-term HCQ therapy carries a risk of irreversible retinal toxicity caused by drug accumulation in the retinal pigment epithelium. The early identification of preclinical retinal changes is essential to prevent permanent visual impairment. Optical coherence tomography (OCT) and OCT-angiography (OCT-A) have emerged as key imaging modalities for the detection of structural and microvascular biomarkers of HCQ retinopathy. A narrative review of the literature was conducted using the PubMed database, focusing on studies published between January 2017 and February 2025. Search terms included "hydroxychloroquine" and "optical coherence tomography." Eligible studies evaluated HCQ-related retinal toxicity using OCT and/or OCT-A in human subjects. Data were extracted regarding study population characteristics, treatment duration, cumulative HCQ dose, daily dose normalized to real body weight, and reported imaging findings. Results: We identified 223 scientific papers of which 88 studies met the inclusion criteria. Structural OCT parameters-particularly alterations in the ellipsoid zone, outer nuclear layer, and retinal pigment epithelium-were consistently associated with early HCQ toxicity, often preceding functional impairment. OCT-A studies demonstrated microvascular alterations, including reduced vessel density and foveal avascular zone enlargement, though interpretation may be confounded by underlying autoimmune-disease-related vasculopathy. Conclusions: HCQ retinopathy is a potentially vision-threatening condition associated with the cumulative dose, treatment duration, and patient-specific risk factors. OCT and OCT-A provide complementary structural and vascular biomarkers that aid in the detection of subclinical retinal toxicity. The integration of quantitative and automated OCT-derived metrics may improve screening strategies, facilitate early diagnosis, and support personalized care in patients receiving long-term HCQ therapy.

Background/Objectives: Otitis media (OM), including acute otitis media (AOM) and chronic otitis media (COM), is a common middle ear disease that can lead to significant morbidity if not accurately diagnosed. Otoscopic interpretation remains subjective and operator-dependent, underscoring the need for objective and reproducible diagnostic support. Recent advances in artificial intelligence (AI) offer promising solutions for automated otoscopic image analysis. Methods: We developed an AI-based diagnostic framework consisting of three sequential steps: (1) semi-supervised learning for automatic recognition and semantic segmentation of tympanic membrane structures, (2) region-based feature extraction, and (3) disease classification. A total of 607 clinical otoscopic images were retrospectively collected, including normal ears (n = 220), AOM (n = 157), and COM with tympanic membrane perforation (n = 230). Among these, 485 images were used for training and 122 for independent testing. Semantic segmentation of five anatomically relevant regions was performed using multiple convolutional neural network architectures, including U-Net, PSPNet, HRNet, and DeepLabV3+. Following segmentation, color and texture features were extracted from each region and used to train a neural network-based classifier to differentiate disease states. Results: Among the evaluated segmentation models, U-Net demonstrated superior performance, achieving an overall pixel accuracy of 96.76% and a mean Dice similarity coefficient of 71.68%. The segmented regions enabled reliable extraction of discriminative chromatic and texture features. In the final classification stage, the proposed framework achieved diagnostic accuracies of 100% for normal ears, 100% for AOM, and 91.3% for COM on the independent test set, with an overall accuracy of 96.72%. Conclusions: This study demonstrates that a semi-supervised, segmentation-driven AI pipeline integrating feature extraction and classification can achieve high diagnostic accuracy for otitis media. The proposed framework offers a clinically interpretable and fully automated approach that may enhance diagnostic consistency, support clinical decision-making, and facilitate scalable otoscopic assessment in diverse healthcare screening settings for disease prevention and health education.

Background/Objectives: Cell-free DNA (cfDNA) is a valuable biomarker for cancer diagnosis and therapy monitoring; however, its low abundance and fragmented nature present major challenges for reliable isolation, particularly from limited plasma volumes. Here, we report the development and evaluation of a novel magnetically assisted microfluidic chip with a three-inlet design for efficient cfDNA extraction from small-volume plasma samples. Methods: The platform enables controlled infusion of plasma, lysis buffer, and magnetic nanoparticle suspensions at defined flow rates. An external magnetic field selectively captures cfDNA-bound nanoparticles while efficiently removing background impurities. Results: Direct comparison with two in vitro diagnostic (IVD)-certified commercial cfDNA extraction kits showed that the microfluidic system achieved comparable cfDNA yields at standard plasma volumes and superior performance at reduced input volumes. High DNA purity and integrity were confirmed by quantitative PCR amplification of a housekeeping gene and clinically relevant targets. The complete workflow required approximately 9 min, used minimal equipment, reduced contamination risk, and enabled rapid processing with future potential for parallel multi-chip configurations. Conclusions: These findings establish the proposed microfluidic platform as a rapid, reproducible, and scalable alternative to conventional cfDNA extraction methods. By significantly improving recovery efficiency from small plasma volumes, the system enhances the clinical feasibility of liquid biopsy applications in cancer diagnostics and precision medicine.

Bariatric surgery is an effective treatment for morbid obesity but is frequently complicated by anastomotic leaks, fistulas, and strictures, which can significantly impair patient outcomes. Optimal management of these complications relies on a timely and accurate diagnostic assessment; however, effective treatment strategies are central to improving clinical recovery. This review primarily focuses on the endoscopic management of post-bariatric surgery complications, while providing a concise overview of the diagnostic imaging modalities that guide therapeutic decision-making. Contrast-enhanced imaging techniques, including computed tomography (CT) and fluoroscopy, as well as endoscopic ultrasound (EUS), are briefly discussed in relation to their role in identifying complications, defining their extent, and selecting the most appropriate endoscopic intervention. The core of this review is dedicated to current endoscopic treatment approaches, including endoscopic internal drainage with double pigtail plastic stents, self-expanding metal stents (SEMSs), endoscopic vacuum therapy (EVT), and EUS-guided drainage of fluid collections. Particular emphasis is placed on indications, technical considerations, and outcomes of these therapies. Finally, this review highlights emerging endoscopic technologies that may further optimize the management of post-bariatric surgery complications and improve patient outcomes, underscoring the evolving role of minimally invasive endoscopic treatment within a multidisciplinary framework.

Advances in artificial intelligence (AI), soft robotics, and miniaturized imaging technologies have accelerated the development of endorobotic platforms that aim to enhance detection accuracy and improve patient experience. In this narrative review, we synthesize evidence on AI-assisted detection and characterization systems (CADe/CADx), robotic locomotion mechanisms, adhesion strategies, imaging modalities, and material and power constraints relating to next-generation CRC screening technologies. Reported performance metrics are interpreted within their original methodological contexts, acknowledging the heterogeneity of datasets, limited representation of diverse populations, underreporting of negative findings, and scarcity of large, real-world comparative trials. We introduce a conceptual translational framework that links engineering design principles with validation needs across in silico, in vitro, preclinical, and clinical stages, and we outline safety considerations, workflow integration challenges, and sterility requirements that influence real-world deployability. Regulatory alignment is discussed using the U.S. FDA Total Product Life Cycle (TPLC) and Good Machine Learning Practice (GMLP) frameworks to highlight expectations for data quality, model robustness, device-software interoperability, and post-market monitoring. Collectively, the evidence demonstrates promising technological innovation but also highlights substantial gaps that must be addressed before AI-enabled endorobotic systems can be safely and effectively integrated into routine CRC screening. Continued interdisciplinary work, supported by rigorous validation and transparent reporting, will be essential to advance these technologies toward meaningful clinical impact.

Background: Age-related macular degeneration (AMD) is a major cause of vision loss, yet publicly available Optical Coherence Tomography (OCT) datasets lack demographic diversity, particularly from South Asian populations. Existing datasets largely represent Western cohorts, limiting AI generalizability. Moreover, raw OCT volumes contain redundant spatial information and speckle noise, hindering efficient analysis. Methods: We introduce BanglaOCT2025, a retrospective dataset collected from the National Institute of Ophthalmology and Hospital (NIOH), Bangladesh, using Nidek RS-330 Duo 2 and RS-3000 Advance systems. We propose a novel preprocessing pipeline comprising two stages: (1) A constraint-based centroid minimization algorithm automatically localizes the foveal center and extracts a fixed 33-slice macular sub-volume, robust to retinal tilt and acquisition variability; and (2) A self-supervised volumetric denoising module based on a Flip-Flop Swin Transformer (FFSwin) backbone suppresses speckle noise without requiring paired clean reference data. Results: The dataset comprises 1585 OCT volumes (202,880 B-scans), including 857 expert-annotated cases (54 DryAMD, 61 WetAMD, and 742 NonAMD). Denoising quality was evaluated using reference-free volumetric metrics, paired statistical analysis, and blinded clinical review by a retinal specialist, confirming preservation of pathological biomarkers and absence of hallucination. Under a controlled paired evaluation using the same classifier with frozen weights, downstream AMD classification accuracy improved from 69.08% to 99.88%, interpreted as an upper-bound estimate of diagnostic signal recoverability rather than independent generalization. Conclusions: BanglaOCT2025 is the first clinically validated OCT dataset representing the Bengali population and establishes a reproducible fovea-centric volumetric preprocessing and restoration framework for AMD analysis, with future validation across independent and multi-centre test cohorts.

Objectives: This study aimed to assess whether low-iodine diet (LID) adherence is associated with therapeutic response in papillary thyroid carcinoma (PTC), specifically in relation to post-therapeutic thyroglobulin (Tg) release as a surrogate marker for the acute radiation-induced response following radioactive iodine (RAI) therapy. Methods: This retrospective study included 895 patients with PTC treated with RAI. LID adherence was assessed using the urine iodine-to-creatinine (I/Cr) ratio, with <66.2 μg/g Cr defined as good adherence. The Tg ratio (ratioTg), calculated by dividing post-RAI Tg (measured 7 days after RAI) by pre-RAI Tg, was used to reflect the magnitude of the radiation-induced Tg release. Patients were stratified by ratioTg (≤1 vs. >1), and associations between LID adherence and therapeutic response were analyzed within each group. Results: Well-adherent patients exhibited significantly higher ratioTg compared to poorly adherent patients (15.7 ± 2.2 vs. 8.9 ± 1.3, p = 0.007). Among patients with ratioTg > 1 (n = 630), LID adherence was independently associated with improved therapeutic response (OR, 2.004; 95% CI, 1.270-3.162; p = 0.003). No such association was observed in patients with ratioTg ≤ 1 (n = 265; p = 0.546). Conclusions: The clinical benefit of LID appears to depend on the presence of a certain magnitude of radiation-induced Tg release. RatioTg may serve as a useful marker for identifying patients likely to benefit from LID.