Journal of imaging informatics in medicine最新文献

Carotid CT angiography (CTA) is valuable for diagnosing carotid artery disease but involves radiation and contrast agent risks. Deep Learning Image Reconstruction (DLIR-H) shows potential for maintaining image quality in low-dose protocols. In this prospective study, 180 patients undergoing dual-energy CTA were divided into three groups: a control group (ASIR-V 50%, NI = 4, contrast = 0.5 mL/kg), a low-dose group (DLIR-H, NI = 11, contrast = 0.5 mL/kg), and an ultra-low-dose group (DLIR-H, NI = 13, contrast = 0.4 mL/kg). Objective (CTV[CT values], noise, SNR, CNR) and subjective (5-point Likert scale) image quality were evaluated. The ultra-low-dose group achieved a 20.3% reduction in contrast volume and a 53.3% reduction in effective dose compared to the control group (P < 0.001). Both experimental groups showed lower noise and higher CNR/SNR (except at aortic arch) than controls. However, the ultra-low-dose group had significantly lower CNR/SNR than the low-dose group (P < 0.05). Subjective image quality was superior in both experimental groups (P < 0.001), with high inter-rater agreement. DLIR-H outperformed ASIR-V in low and ultra-low-dose protocols but could not fully compensate for image quality degradation when radiation and contrast were further reduced.

Pediatric swallowing dysfunction (SwD) poses serious health risks, including aspiration, malnutrition, and recurrent respiratory infections, making early and accurate diagnosis essential for preventing long-term sequelae such as chronic lung disease and growth failure. Fiberoptic endoscopic evaluation of swallowing (FEES) is widely used for direct visualization of the swallowing mechanism in children, offering advantages over fluoroscopy such as bedside accessibility and radiation-free imaging. During FEES, patients swallow green-dyed liquid with an endoscope positioned in the throat. Interpreting FEES recordings is a subjective, time-consuming process that requires specialized expertise. Automated, objective analysis tools would be useful to support clinical decision-making. In this study, we propose a hybrid framework for classifying pediatric FEES recordings as normal or abnormal. The approach combines a rule-based analysis which detects the green-tinted swallowed liquid, with a transformer-based deep learning model. Frames are first filtered using a Siamese network to exclude irrelevant or low-quality frames, followed by quantification of the green frame ratio based on frames containing green patches. A confidence-guided decision strategy classifies clear-cut cases via thresholding, while delegating uncertain cases to the deep learning model for further evaluation. Evaluation on 142 pediatric FEES videos (45 normal and 97 with abnormalities) showed that the hybrid approach outperformed both the deep learning and rule-based methods individually, achieving 89.4% accuracy, 96.6% precision, and 93.3% specificity for aspiration. Our results indicate that by combining rule-based and deep learning strategies, we could reliably detect swallowing abnormalities from pediatric FEES videos with accuracies comparable to experts.

This study evaluates a commercially available AI tool (Aidoc) for intracranial hemorrhage (ICH) detection-originally trained on adults-in pediatric patients, addressing the critical need for timely diagnosis and current research gaps in pediatric AI applications. This single-center, retrospective study included pediatric patients aged 6-17 who underwent head CT between January 2017 and November 2022. Radiological reports (unaided by AI) and CT images were analyzed by natural language processing (NLP) and image-based algorithms, respectively, to classify ICH presence or absence. Ground truth was assumed for concordant cases. Three radiologists independently reviewed discrepant cases using majority vote. Among 2502 pediatric patients undergoing head CT, the AI algorithm flagged 292 cases as suspected ICH-positive. A total of 174 discordant cases between NLP and AI were independently reviewed to create the reference standard. Results showed 144 true positives, 6 false negatives, 148 false positives, and 2204 true negatives, yielding sensitivity of 96.0% (91.5-98.5%) and specificity of 93.7% (92.6-94.7%). Overall algorithm accuracy was 93.8% (92.8-94.8%). The most frequent false positives were choroid plexus calcifications and hyperdense venous sinuses, while subdural hemorrhages accounted for most false negatives. This deep learning AI algorithm trained on adult data performs well in detecting pediatric ICH, with 96.0% sensitivity and 93.7% specificity. However, common false positives, choroid plexus calcifications and hyperdense venous sinuses, reflect pediatric-specific features, while missed subdural hemorrhages mirror known adult limitations. Results highlight the need for pediatric-focused AI training to improve diagnostic accuracy in this underserved population.

Agentic artificial intelligence systems featuring iterative reasoning, autonomous tool use, or multi-agent collaboration have been proposed as solutions to the limitations of large language models (LLMs) in neuroradiology. However, the extent of their implementation and clinical validation remains unclear. We systematically searched PubMed, Web of Science, and Scopus (January 2022-August 2025) for studies implementing agentic AI in neuroradiology. Six independent reviewers (three medical doctors and three AI specialists) assessed full texts. Agentic AI was defined as requiring mandatory iterative reasoning plus either autonomous tool use or multi-agent collaboration. Study quality was evaluated using adapted QUADAS-AI criteria. From 230 records, 9 studies (3.90%) met inclusion criteria. Of these, five (55.60%) implemented true multi-agent architecture, two (22.20%) used hybrid or conceptual frameworks, and two (22.20%) relied on single-model LLMs without genuine agentic behavior. All nine studies were single center with no external validation. Sample sizes were small (median 142 cases; range 16-302). The only randomized controlled trial-INSPIRE (neurophysiology with imaging correlation)-demonstrated high technical performance (≈92% accuracy; AIGERS 0.94 for AI-assisted vs. 0.70 for AI-only, p < 0.001) but showed no measurable clinical benefit when physicians used AI assistance compared with independent reporting. Safety assessments were absent from all studies. Agentic AI in neuroradiology remains technically promising but clinically unproven. Severe evidence scarcity (3.90% inclusion rate), frequent overextension of the "agentic" label (30% of studies lacked genuine autonomy), and the persistent gap between technical performance and clinical utility indicate that the field remains in its early research phase. Current evidence is insufficient to support clinical deployment. Rigorous, multi-center prospective trials with patient-centered and safety outcomes are essential before clinical implementation can be responsibly considered.

Medical image multimodal registration is not only an indispensable processing step in medical image analysis but also plays a crucial role in disease diagnosis and treatment planning. However, the complex and unknown spatial deformation relationships between different organs and different modalities pose significant challenges to multimodal image registration. To address this problem, this study proposes an unsupervised and discriminator-free multimodal registration method based on a dual loss function-SA-HMT. Specifically, to address the challenge of cross-modal feature matching, the multi-scale skip Transformer module proposed in this study employs a hierarchical architecture to capture multi-scale deformation features. In the shallow network, the multi-scale skip pyramid module extracts modality-independent local structural features through parallel multi-branch convolution, effectively overcoming the differential expression of edges and textures across different modalities. In the deep network, the Transformer module establishes long-range dependencies via self-attention mechanism, enabling adaptive fusion of local deformation features with global semantics and effectively alleviating the matching difficulty of cross-modal structural features. In addition, this study further proposes a structure-aware deformable convolution module. The two-stage joint mechanism of "feature perception-offset generation" enhances the accuracy of feature matching through their progressive collaboration. The effectiveness of SA-HMT has been fully verified in five public data sets (covering chest and abdomen CT-MR, lung CT, brain CT-MR, cardiac MRI) and clinical abdominal data. Compared with the advanced method R2Net, our model achieves improvements in core indicators such as DSC, and the registration accuracy is generally comparable or better.

This study assessed the value of radiomics analysis in differentiating clear cell renal cell carcinoma (ccRCC) from renal oncocytoma (RO) using multi-phase contrast-enhanced CT. A retrospective analysis included 43 ccRCC and 43 RO cases (2013-2024). Preoperative three-phase CT scans (corticomedullary [CP], nephrographic [NP], excretory [EP]) were analyzed. Tumor regions of interest (ROIs) were semi-automatically segmented in 3D-Slicer, with texture features extracted via IBEX software. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were calculated for selected parameters in each phase. A support vector machine (SVM) classifier trained on texture parameters underwent diagnostic evaluation via ROC analysis. All phases showed high diagnostic accuracy (AUC > 0.9), with NP demonstrating the highest performance (AUC = 0.952; accuracy, 0.88; sensitivity, 0.91; specificity, 0.87). Intensity histogram IH_Skewness differed significantly between ccRCC and RO in CP and NP (P < 0.01 for both), with AUC values of 0.75 (CP) and 0.79 (NP). Combining LASSO dimension reduction with SVM using multi-phase CT radiomics features enabled the effective differentiation between ccRCC and RO, highlighting texture analysis as a promising clinical tool.

Otosclerosis is a disease affecting the middle and inner ear, characterized by abnormal bone remodeling that leads to stapes fixation and progressive hearing loss. Although high-resolution computed tomography (HRCT) is the standard imaging modality for diagnosis, its sensitivity is limited, with a high false-negative rate (FNR). This study investigates the use of radiomics and machine learning (ML) to improve diagnostic accuracy. HRCT scans from 99 subjects (48 otosclerosis, 51 controls) were analyzed, focusing on the stapes, antefenestral region (AF), and oval window (OW). From each scan, 6048 radiomic features were extracted and reduced to 1317 through feature selection. Statistical analyses and ML modeling were performed using the selected features. Sixty-seven biomarkers showed significant differences between cases and controls, primarily in the AF (56) and stapes (11); none were found in the OW. Both the AF and stapes exhibited increased heterogeneity in otosclerosis, reflecting the bone remodeling process. A reduction in the stapes' major axis was also observed, possibly related to torsional deformation. Image transformation filters enhanced disease visibility. Among several ML classifiers tested, L2-regularized logistic regression performed best, achieving an AUC of 0.90 ± 0.06, thereby enhancing the diagnostic accuracy reported in some studies for radiologists. Hierarchical clustering of the most predictive features further confirmed their strong discriminative power. Our findings highlight the potential of radiomics and ML to standardize otosclerosis diagnosis, reduce FNR, and support surgical decision-making. Future studies should validate these results using larger cohorts and advanced imaging technologies such as Photon-Counting CT.

Machine learning (ML), particularly deep learning (DL) and radiomics-based approaches, has emerged as a powerful tool for cancer outcome prediction using PET and SPECT imaging. However, the comparative performance of different techniques-handcrafted radiomics features (HRF), deep radiomics features (DRF), DL models, and hybrid fusion models (combinations of DRF, HRF, and clinical features)-remains inconsistent across clinical applications. This systematic review analyzed 226 studies published between 2020 and 2025 that applied ML to PET or SPECT imaging for cancer outcome prediction tasks. Each study was evaluated using a 59-item framework addressing dataset construction, feature extraction methods, validation strategies, interpretability, and risk of bias. We extracted key data, including model type, cancer site, imaging modality, and performance metrics such as accuracy and area under the curve (AUC). PET-based models (95%) generally outperformed SPECT, likely due to superior spatial resolution and sensitivity. DRF models achieved the highest mean accuracy (0.862 ± 0.051), while fusion models attained the highest AUC (0.861 ± 0.088). ANOVA revealed significant differences in accuracy (p = 0.0006) and AUC (p = 0.0027). Despite these promising findings, key limitations remain, including poor management of class imbalance (59%), missing data (29%), and low population diversity (19%). Only 48% adhered to IBSI (Image Biomarker Standardization Initiative) standards. This systematic review shows that DL and DRF-based models, especially in fusion with HRFs, outperform HRF-only methods for cancer outcome prediction using PET/SPECT, particularly in data-limited settings. Despite strong performance, challenges remain in interpretability and standardization, highlighting the need for unified DRF extraction frameworks across modalities.

Existing automated methods for white matter hyperintensity (WMH) segmentation often generalize poorly to heterogeneous clinical MRI due to variability in scanner types, field strengths, and protocols. To address this challenge, we introduce a diverse clinical WMH dataset and evaluate two deep learning-based solutions: an nnU-Net model trained directly on the data and a foundation model adapted through fine-tuning. This retrospective study included 195 routine brain MRI scans acquired from 71 scanners between June 2006 and October 2022. Participants ranged in age from 46 to 87 years (median, 70 years; 94 females). WMHs were manually annotated by an experienced rater and reviewed under neuroradiologist supervision. Several benchmark segmentation methods were evaluated against these annotations. We then developed Robust-WMH-UNet by training nnU-Net on the dataset and Robust-WMH-SAM by fine-tuning MedSAM, a vision foundation model. Benchmark methods demonstrated poor generalization, frequently missing small lesions and producing false positives in anatomically complex regions such as the septum pellucidum. Robust-WMH-UNet achieved superior accuracy (median Dice similarity coefficient [DSC], 0.768) with improved specificity, while Robust-WMH-SAM attained competitive performance (median DSC up to 0.750) after only limited training, reaching acceptable accuracy within a single epoch. This new clinically representative dataset provides a strong foundation for developing robust WMH algorithms, enabling fair cross-method comparisons, and supporting the translation of segmentation models into routine clinical practice.

The rapid development of machine learning (ML) and deep learning (DL) has greatly advanced Alzheimer's disease (AD) diagnosis. However, existing models struggle to capture weak structural features in the marginal regions of brain MRI images, leading to limited diagnostic accuracy. To address this challenge, we introduce a Dual-Branch Convolutional Neural Network (DBCNN) equipped with a Learnable Edge Detection Module designed to jointly learn global semantic representations and fine-grained edge cues within a unified framework. Experimental results on two public datasets demonstrate that DBCNN significantly improves classification accuracy, surpassing 98%. Notably, on the OASIS dataset, it achieved an average accuracy of 99.71%, demonstrating strong generalization and robustness. This high diagnostic performance indicates that the model can assist clinicians in the early detection of Alzheimer's disease, reduce subjectivity in manual image interpretation, and enhance diagnostic consistency. Overall, the proposed approach provides a promising pathway toward intelligent, interpretable, and computationally efficient solutions for MRI-based diagnosis, offering strong potential to support early clinical decision-making.