Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that affects behavior, attention, and learning. Current diagnoses rely heavily on subjective assessments, underscoring the need for objective imaging-based methods. This study aims to explore whether structural connectivity networks derived from MRI can reveal alterations associated with ADHD and support data-driven understanding. We collected brain MRI data from 947 individuals (aged 7-26 years; 590 males, 356 females, 1 unspecified) across eight centers, sourced from the Neuro Bureau ADHD-200 preprocessed dataset. Transformer-based deep learning models were used to learn relationships between different brain regions and construct structural connectivity networks. To prepare input for the model, each region was transformed into a standardized data sequence using four different strategies. The strength of connectivity between brain regions was then measured to identify structural differences related to ADHD. Five-fold cross-validation and statistical analyses were used to evaluate model robustness and group differences, respectively. Here we show that the proposed method performs well in distinguishing ADHD individuals from healthy controls, with accuracy reaching 71.9 percent and an area under curve of 0.74. The structural networks also reveal significant differences in connectivity patterns (paired t-test: P = 0.81 × 10^-6), particularly involving regions responsible for motor and executive function. Notably, the importance rankings of several brain regions, including the thalamus and caudate, differ markedly between groups. This study shows that ADHD may be associated with connectivity alterations in multiple brain regions. Our findings suggest that brain structural connectivity networks built using Transformer-based methods offer a promising tool for both diagnosis and further research into brain structure.

Sparse-view computed tomography (CT) holds promise for reducing radiation exposure and enabling novel system designs. Traditional reconstruction algorithms, including Filtered Backprojection (FBP) and Model-Based Iterative Reconstruction (MBIR), often produce artifacts in sparse-view data. Deep Learning Reconstruction (DLR) offers potential improvements, but task-based evaluations of DLR in sparse-view CT remain limited. This study employs an Artificial Intelligence (AI) observer to evaluate the diagnostic accuracy of FBP, MBIR, and DLR for intracranial hemorrhage detection and classification, offering a cost-effective alternative to human radiologist studies. A public brain CT dataset with labeled intracranial hemorrhages was used to train an AI observer model. Sparse-view CT data were simulated, with reconstructions performed using FBP, MBIR, and DLR. Reconstruction quality was assessed using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). Diagnostic utility was evaluated using Receiver Operating Characteristic (ROC) analysis and Area Under the Curve (AUC) values for One-vs-Rest and One-vs-One classification tasks. DLR outperformed FBP and MBIR in all quality metrics, demonstrating reduced noise, improved structural similarity, and fewer artifacts. The AI observer achieved the highest classification accuracy with DLR, while FBP surpassed MBIR in task-based accuracy despite inferior image quality metrics, emphasizing the value of task-based evaluations. DLR provides an effective balance of artifact reduction and anatomical detail in sparse-view CT brain imaging. This proof-of-concept study highlights AI observer models as a viable, cost-effective alternative for evaluating CT reconstruction techniques.

Fat and muscle mass are potential biomarkers of wellbeing and disease in oncology, but clinical measurement methods vary considerably. Here we evaluate the accuracy, precision and ability to track change for multiple deep learning (DL) models that quantify fat and muscle mass from abdominal MRI. Specifically, subcutaneous fat (SF), intra-abdominal fat (VF), external muscle (EM) and psoas muscle (PM) were evaluated using 15 convolutional neural network (CNN)-based and 4 transformer-based deep learning model architectures. There was negligible difference in the accuracy of human observers and all deep learning models in delineating SF or EM. Both of these tissues had excellent repeatability of their delineation. VF was measured most accurately by the human observers, then by CNN-based models, which outperformed transformer-based models. In distinction, PM delineation accuracy and repeatability was poor for all assessments. Repeatability limits of agreement determined when changes measured in individual patients were due to real change rather than test-retest variation. In summary, DL model accuracy and precision of delineating fat and muscle volumes varies between CNN-based and transformer-based models, between different tissues and in some cases with gender. These factors should be considered when investigators deploy deep learning methods to estimate biomarkers of fat and muscle mass.

Accurate diagnosis of pancreatic cystic lesions (PCLs) is essential to guide appropriate management and reduce unnecessary surgeries. Despite multiple guidelines in PCL management, a substantial proportion of patients still undergo major resections for benign cysts, and a majority of resected intraductal papillary mucinous neoplasms (IPMNs) show only low-grade dysplasia, leading to significant clinical, financial, and psychological burdens. This review highlights emerging endoscopic approaches that enhance diagnostic accuracy and support organ-sparing, minimally invasive management of PCLs. Recent studies suggest that endoscopic ultrasound (EUS) and its accessory techniques, such as contrast-enhanced EUS and needle-based confocal laser endomicroscopy, as well as next-generation sequencing analysis of cyst fluid, not only accurately characterize PCLs but are also well tolerated and cost-effective. Additionally, emerging therapeutics such as EUS-guided radiofrequency ablation (RFA) and EUS-chemoablation are promising as minimally invasive treatments for high-risk mucinous PCLs in patients who are not candidates for surgery. Accurate diagnosis of PCLs remains challenging, leading to many patients undergoing unnecessary surgery. Emerging endoscopic imaging biomarkers, artificial intelligence analysis, and molecular biomarkers enhance diagnostic precision. Additionally, novel endoscopic ablative therapies offer safe, minimally invasive, organ-sparing treatment options, thereby reducing the healthcare resource burdens associated with overtreatment.

Heart failure with preserved ejection fraction (HFpEF) poses significant diagnostic and prognostic challenges due to its clinical heterogeneity. This study proposes a multi-modal, explainable machine learning framework that integrates clinical variables and cardiac magnetic resonance (CMR)-derived features, particularly epicardial adipose tissue (EAT) volume, to improve risk stratification and outcome prediction in patients with HFpEF. A retrospective cohort of 301 participants (171 in the HFpEF group and 130 in the control group) was analyzed. Baseline characteristics, CMR-derived EAT volume, and laboratory biomarkers were integrated into machine learning models. Model performance was evaluated using accuracy, precision, recall, and F1-score. Additionally, receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC) were employed to assess discriminative power across varying decision thresholds. Hyperparameter optimization and ensemble techniques were applied to enhance predictive performance. HFpEF patients exhibited significantly higher EAT volume (70.9±27.3 vs. 41.9±18.3 mL, p<0.001) and NT-proBNP levels (1574 [963,2722] vs. 33 [10,100] pg/mL, p<0.001), along with a greater prevalence of comorbidities. The voting classifier demonstrated the highest accuracy for HFpEF diagnosis (0.94), with a precision of 0.96, recall of 0.94, and an F1-score of 0.95. For prognostic tasks, AdaBoost, XGBoost and Random Forest yielded superior performance in predicting adverse clinical outcomes, including rehospitalization and all-cause mortality (accuracy: 0.95). Key predictive features identified included EAT volume, right atrioventricular groove (Right AVG), tricuspid regurgitation velocity (TRV), and metabolic syndrome. Explainable models combining clinical and CMR-derived features, especially EAT volume, improve support for HFpEF diagnosis and outcome prediction. These findings highlight the value of a data-driven, interpretable approach to characterizing HFpEF phenotypes and may facilitate individualized risk assessment in selected populations.

This study aimed to evaluate the diagnostic performance of ChatGPT-4o, a large language model developed by OpenAI, in challenging cases of oral and maxillofacial diseases presented in the <i>Clinicopathologic Conference</i> section of the journal <i>Oral Surgery</i>, <i>Oral Medicine</i>, <i>Oral Pathology</i>, <i>Oral Radiology</i>. A total of 123 diagnostically challenging oral and maxillofacial cases published in the aforementioned journal were retrospectively collected. The case presentations, which included detailed clinical, radiographic, and sometimes histopathologic descriptions, were input into ChatGPT-4o. The model was prompted to provide a single most likely diagnosis for each case. These outputs were then compared to the final diagnoses established by expert consensus in each original case report. The accuracy of ChatGPT-4o was calculated based on exact diagnostic matches. ChatGPT-4o correctly diagnosed 96 out of 123 cases, achieving an overall diagnostic accuracy of 78%. Nevertheless, even in cases where the exact diagnosis was not provided, the model often suggested one of the clinically reasonable differential diagnoses. ChatGPT-4o demonstrates a promising ability to assist in the diagnostic process of complex maxillofacial conditions, with a relatively high accuracy rate in challenging cases. While it is not a replacement for expert clinical judgment, large language models may offer valuable decision support in oral and maxillofacial radiology, particularly in educational or consultative contexts. Not applicable.

Glioma, the most common primary intracranial tumor, poses significant challenges to precision diagnosis and treatment due to its heterogeneity and invasiveness. With the introduction of the 2021 WHO classification standard based on molecular biomarkers, the role of imaging in non-invasive subtyping and therapeutic monitoring of gliomas has become increasingly crucial. While conventional MRI shows limitations in assessing metabolic status and differentiating tumor recurrence, positron emission tomography (PET) combined with radiomics and artificial intelligence technologies offers a novel paradigm for precise diagnosis and treatment monitoring through quantitative extraction of multimodal imaging features (e.g., intensity, texture, dynamic parameters). This review systematically summarizes the technical workflow of PET radiomics (including tracer selection, image segmentation, feature extraction, and model construction) and its applications in predicting molecular subtypes (such as IDH mutation and MGMT methylation), distinguishing recurrence from treatment-related changes, and prognostic stratification. Studies demonstrate that amino acid tracers (e.g., ¹⁸F-FET, ¹¹C-MET) combined with multimodal radiomics models significantly outperform traditional parametric analysis in diagnostic efficacy. Nevertheless, current research still faces challenges including data heterogeneity, insufficient model interpretability, and lack of clinical validation. Future advancements require multicenter standardized protocols, open-source algorithm frameworks, and multi-omics integration to facilitate the transformative clinical translation of PET radiomics from research to practice.

Poor quality prostate MRI images compromise diagnostic accuracy, with diffusion-weighted imaging and the resulting apparent diffusion coefficient (ADC) maps being particularly vulnerable. These maps are critical for prostate cancer diagnosis, yet current methods relying on standardizing technical parameters fail to consistently ensure image quality. We propose a novel deep learning approach to predict low-quality ADC maps using T2-weighted (T2W) images, enabling real-time corrective interventions during imaging. A multi-site dataset of T2W images and ADC maps from 486 patients, spanning 62 external clinics and in-house imaging, was retrospectively analyzed. A neural network was trained to classify ADC map quality as "diagnostic" or "non-diagnostic" based solely on T2W images. Rectal cross-sectional area measurements were evaluated as an interpretable metric for susceptibility-induced distortions. Analysis revealed limited correlation between individual acquisition parameters and image quality, with horizontal phase encoding significant for T2 imaging (p < 0.001, AUC = 0.6735) and vertical resolution for ADC maps (p = 0.006, AUC = 0.6348). By contrast, the neural network achieved robust performance for ADC map quality prediction from T2 images, with 83 % sensitivity and 90 % negative predictive value in multicenter validation, comparable to single-site models using ADC maps directly. Remarkably, it generalized well to unseen in-house data (94 ± 2 % accuracy). Rectal cross-sectional area correlated with ADC quality (AUC = 0.65), offering a simple, interpretable metric. The probability of low quality, uninterpretable ADC maps can be inferred early in the imaging process by a neural network approach, allowing corrective action to be employed.

Intraoperative brain ultrasound (US) provides real-time information on lesions and tissues, making it crucial for brain tumor resection. However, due to limitations such as imaging angles and operator techniques, US data is limited in size and difficult to annotate, hindering advancements in intelligent image processing. In contrast, Magnetic Resonance Imaging (MRI) data is more abundant and easier to annotate. If MRI data and models can be effectively transferred to the US domain, generating high-quality US data would greatly enhance US image processing and improve intraoperative US readability.&#xD;Approach. We propose a Cross-Modal Conditional Latent Diffusion Model (CCLD) for brain MRI-to-US image translation. We employ a noise mask restoration strategy to pretrain an efficient encoder-decoder, enhancing feature extraction, compression, and reconstruction capabilities while reducing computational costs. Furthermore, CCLD integrates the Frequency-Decomposed Feature Optimization Module (FFOM) and the Adaptive Multi-Frequency Feature Fusion Module (AMFM) to effectively leverage MRI structural information and US texture characteristics, ensuring structural accuracy while enhancing texture details in the synthetic US images.&#xD;Main results. Compared with state-of-the-art methods, our approach achieves superior performance on the ReMIND dataset, obtaining the best Learned Perceptual Image Patch Similarity (LPIPS) score of 19.1%, Mean Absolute Error (MAE) of 4.21%, as well as the highest Peak Signal-to-Noise Ratio (PSNR) of 25.36 dB and Structural Similarity Index (SSIM) of 86.91%. &#xD;Significance. Experimental results demonstrate that CCLD effectively improves the quality and realism of synthetic ultrasound images, offering a new research direction for the generation of high-quality US datasets and the enhancement of ultrasound image readability.&#xD.

Degenerative cerebellar ataxia, a group of progressive neurodegenerative disorders, is characterised by cerebellar atrophy and impaired motor learning. Using CerebNet, a deep learning algorithm for cerebellar segmentation, this study investigated the relationship between cerebellar subregion volumes and motor learning ability. We analysed data from 37 patients with degenerative cerebellar ataxia and 18 healthy controls. Using CerebNet, we segmented four cerebellar subregions: the anterior lobe, superior posterior lobe, inferior posterior lobe, and vermis. Regression analyses examined the associations between cerebellar volumes and motor learning performance (adaptation index [AI]) and ataxia severity (Scale for Assessment and Rating of Ataxia [SARA]). The inferior posterior lobe volume showed a significant positive association with AI in both single (B = 0.09; 95% CI: [0.03, 0.16]) and multiple linear regression analyses (B = 0.11; 95% CI: [0.008, 0.20]), an association that was particularly evident in the pure cerebellar ataxia subgroup. SARA scores correlated with anterior lobe, superior posterior lobe, and vermis volumes in single linear regression analyses, but these associations were not maintained in multiple linear regression analyses. This selective association suggests a specialised role for the inferior posterior lobe in motor learning processes. This study reveals the inferior posterior lobe's distinct role in motor learning in patients with degenerative cerebellar ataxia, advancing our understanding of cerebellar function and potentially informing targeted rehabilitation approaches. Our findings highlight the value of advanced imaging technologies in understanding structure-function relationships in cerebellar disorders.