Achondroplasia, the most common chondrodysplasia in humans, is caused by one of two gain of function mutations localized in the transmembrane domain of fibroblast growth factor receptor 3 (FGFR3) leading to constitutive activation of FGFR3 and subsequent growth plate cartilage and bone defects. Phenotypic features of achondroplasia include macrocephaly with frontal bossing, midface hypoplasia, disproportionate shortening of the extremities, brachydactyly with trident configuration of the hand, and bowed legs. The condition is defined primarily on postnatal effects on bone and cartilage, and embryonic development of tissues in affected individuals is not well studied. Using the Fgfr3^Y367C/+ mouse model of achondroplasia, we investigated the developing chondrocranium and Meckel's cartilage (MC) at embryonic days (E)14.5 and E16.5. Sparse hand annotations of chondrocranial and MC cartilages visualized in phosphotungstic acid enhanced three-dimensional (3D) micro-computed tomography (microCT) images were used to train our automatic deep learning-based 3D segmentation model and produce 3D isosurfaces of the chondrocranium and MC. Using 3D coordinates of landmarks measured on the 3D isosurfaces, we quantified differences in the chondrocranium and MC of Fgfr3^Y367C/+ mice relative to those of their unaffected littermates. Statistically significant differences in morphology and growth of the chondrocranium and MC were found, indicating direct effects of this Fgfr3 mutation on embryonic cranial and pharyngeal cartilages, which in turn can secondarily affect cranial dermal bone development. Our results support the suggestion that early therapeutic intervention during cartilage formation may lessen the effects of this condition.

Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intensive task. Extensive expert annotations are needed to train effective machine learning models. This study presents an innovative approach that leverages federated learning in combination with a teacher‒student framework, an enhanced slice-aware network (SANet), and semisupervised learning (SSL) techniques to improve the CT-based liver tumor detection process while significantly reducing its labor and time costs. Federated learning enables collaborative model training to be performed across multiple institutions without sharing sensitive patient data, thus ensuring privacy and security. The teacher-student SANet framework takes advantage of both teacher and student models, with the teacher model providing reliable pseudolabels that guide the student model in a semisupervised manner. This method not only improves the accuracy of liver tumor detection but also reduces the dependence on extensively annotated datasets. The proposed method was validated through simulation experiments conducted in four scenarios, and it demonstrated a model accuracy of 83%, which represents an improvement over the original locally trained models. This study presents a promising method for enhancing the CT-based liver tumor detection while reducing the incurred labor and time costs by utilizing federated learning, the teacher-student SANet framework, and SSL techniques. Compared with previous approaches, the proposed method achieved a model accuracy of 83%, representing a significant improvement. Not applicable.

To develop and evaluate orbital CT deep learning (DL) models in optic neuropathy (ON) prediction in patients diagnosed with thyroid eye disease (TED), using partial versus entire 2D versus 3D images for input. Patients with TED ±ON diagnosed at a quaternary-level practice and who underwent orbital CT between 2002 and 2017 were included. DL models were developed using annotated CT data. The DL models were used to evaluate the hold-out test set. ON classification performances were compared between models and medical specialists, and saliency maps applied to randomized cases. 36/252 orbits in 126 TED patients (mean age, 51 years; 81 women) had clinically confirmed ON. With 2D image input for ON prediction, our models achieved (a) sensitivity 89%, AUC 0.86 on entire coronal orbital apex including bony walls, and (b) specificity 92%, AUC 0.79 on partial axial lateral orbital wall only annotations. ON classification performance was similar (<i>p</i> = 0.58) between DL model and medical specialists. DL models trained on 2D CT annotations rival medical specialists in ON classification, with potential to objectively enhance clinical triage for sight-saving intervention and incorporate model variants in the workflow to harness differential performance metrics.

A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistical noise has been developed for PET/CT imaging. It may reduce the administered dose of ¹⁸F-FDG and minimize radiation exposure while maintaining diagnostic quality. This retrospective study evaluated whether the injected ¹⁸F-FDG dose could be reduced by applying DLR to PET images. To this aim, we compared the quantitative image quality metrics and the false-positive rate between DLR with a reduced ¹⁸F-FDG dose and Ordered Subsets Expectation Maximization (OSEM) with a standard dose. This study included 90 oncology patients who underwent ¹⁸F-FDG PET/CT. They were divided into 3 groups (30 patients each): group A (¹⁸F-FDG dose per body weight [BW]: 2.00-2.99 MBq/kg; PET image reconstruction: DLR), group B (3.00-3.99 MBq/kg; DLR), and group C (standard dose group; 4.00-4.99 MBq/kg; OSEM). The evaluation was performed using the signal-to-noise ratio (SNR), target-to-background ratio (TBR), and false-positive rate. DLR yielded significantly higher SNRs in groups A and B than group C (p < 0.001). There was no significant difference in the TBR between groups A and C, and between groups B and C (p = 0.983 and 0.605, respectively). In group B, more than 80% of patients weighing less than 75 kg had at most one false positive result. In contrast, in group B patients weighing 75 kg or more, as well as in group A, less than 80% of patients had at most one false-positives. Our findings suggest that the injected ¹⁸F-FDG dose can be reduced to 3.0 MBq/kg in patients weighing less than 75 kg by applying DLR. Compared to the recommended dose in the European Association of Nuclear Medicine (EANM) guidelines for 90 s per bed position (4.7 MBq/kg), this represents a dose reduction of 36%. Further optimization of DLR algorithms is required to maintain comparable diagnostic accuracy in patients weighing 75 kg or more.

Diagnostic delay remains a critical challenge in axial spondyloarthritis (axSpA). This review highlights key clinical and imaging research from 2024 that addresses this persistent issue, with a focus on the evolving roles of MRI, artificial intelligence (AI), and updated Canadian management recommendations. Multiple studies published in 2024 emphasized the continued problem of diagnostic delay in axSpA. Studies support the continued use of sacroiliac joint MRI as a central diagnostic tool for axSpA, particularly in patients with chronic back pain and associated conditions like uveitis, psoriasis (PsO), or inflammatory bowel disease. AI-based tools for interpreting sacroiliac joint MRIs demonstrated moderate agreement with expert assessments, offering a potential solution to variability and limited access to expert musculoskeletal radiology. These innovations may support earlier diagnosis and reduce misclassification. Innovative models of care, including patient-initiated telemedicine visits, reduced in-person visit frequency without compromising clinical outcomes in patients with stable axSpA. Updated Canadian treatment guidelines introduced more robust data on Janus kinase (JAK) inhibitors and offered stronger support for tapering biologics in patients with sustained low disease activity or remission, while advising against abrupt discontinuation. This clinical and imaging year in review covers challenges and innovations in axSpA, emphasizing the need for early access to care and the development of tools to support prompt diagnosis and sustained continuity of care.

AI-driven scan time reduction is rapidly transforming medical imaging with benefits such as improved patient comfort and enhanced efficiency. A Dual Contrastive Learning Generative Adversarial Network (DCLGAN) was developed to predict full-time PET scans from shorter, noisier scans, improving challenges in imaging patients with movement disorders. 18F FP-CIT PET/CT data from 391 patients with suspected Parkinsonism were used [250 training/validation, 141 testing (hospital A)]. Ground truth (GT) images were reconstructed from 15-minute scans, while denoised images (DIs) were generated from 1-, 3-, 5-, and 10-minute scans. Image quality was assessed using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), visual analysis, and clinical metrics like BPND and ISR for diagnosis of non-neurodegenerative Parkinson disease (NPD), idiopathic PD (IPD), and atypical PD (APD). External validation used data from 2 hospitals with different scanners (hospital B: 1-, 3-, 5-, and 10-min; hospital C: 1-, 3-, and 5-min). In addition, motion artifact reduction was evaluated using the Dice similarity coefficient (DSC). In hospital A, NRMSE, PSNR, and SSIM values improved with scan duration, with the 5-minute DIs achieving optimal quality (NRMSE 0.008, PSNR 42.13, SSIM 0.98). Visual analysis rated DIs from scans ≥3 minutes as adequate or higher. The mean BPND differences (95% CI) for each DIs were 0.19 (-0.01, 0.40), 0.11 (-0.02, 0.24), 0.08 (-0.03, 0.18), and 0.01 (-0.06, 0.07), with the CIs significantly decreasing. ISRs with the highest effect sizes for differentiating NPD, IPD, and APD (PP/AP, PP/VS, PC/VP) remained stable post-denoising. External validation showed 10-minute DIs (hospital B) and 1-minute DIs (hospital C) reached benchmarks of hospital A's image quality metrics, with similar trends in visual analysis and BPND CIs. Furthermore, motion artifact correction in 9 patients yielded DSC improvements from 0.89 to 0.95 in striatal regions. The DL-model is capable of generating high-quality 18F FP-CIT PET images from shorter scans to enhance patient comfort, minimize motion artifacts, and maintain diagnostic precision. Furthermore, our study plays an important role in providing insights into how imaging quality assessment metrics can be used to determine the appropriate scan duration for different scanners with varying sensitivities.

The accurate and early evaluation of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for optimizing treatment strategies and minimizing unnecessary interventions. While deep learning (DL)-based approaches have shown promise in medical imaging analysis, existing models often fail to comprehensively integrate spatial and temporal tumor dynamics. This study aims to develop and validate a spatiotemporal interaction (STI) model based on longitudinal MRI data to predict pathological complete response (pCR) to NAC in breast cancer patients. This study included retrospective and prospective datasets from five medical centers in China, collected from June 2018 to December 2024. These datasets were assigned to the primary cohort (including training and internal validation sets), external validation cohorts, and a prospective validation cohort. DCE-MRI scans from both pre-NAC (T0) and early-NAC (T1) stages were collected for each patient, along with surgical pathology results. A Siamese network-based STI model was developed, integrating spatial features from tumor segmentation with temporal dependencies using a transformer-based multi-head attention mechanism. This model was designed to simultaneously capture spatial heterogeneity and temporal dynamics, enabling accurate prediction of NAC response. The STI model's performance was evaluated using the area under the ROC curve (AUC) and Precision-Recall curve (AP), accuracy, sensitivity, and specificity. Additionally, the I-SPY1 and I-SPY2 datasets were used for Kaplan-Meier survival analysis and to explore the biological basis of the STI model, respectively. The prospective cohort was registered with Chinese Clinical Trial Registration Centre (ChiCTR2500102170). A total of 1044 patients were included in this study, with the pCR rate ranging from 23.8% to 35.9%. The STI model demonstrated good performance in early prediction of NAC response in breast cancer. In the external validation cohorts, the AUC values were 0.923 (95% CI: 0.859-0.987), 0.892 (95% CI: 0.821-0.963), and 0.913 (95% CI: 0.835-0.991), all outperforming the single-timepoint T0 or T1 models, as well as models with spatial information added (all p < 0.05, Delong test). Additionally, the STI model significantly outperformed the clinical model (p < 0.05, Delong test) and radiologists' predictions. In the prospective validation cohort, the STI model identified 90.2% (37/41) of non-pCR and 82.6% (19/23) of pCR patients, reducing misclassification rates by 58.7% and 63.3% compared to radiologists. This indicates that these patients might benefit from treatment adjustment or continued therapy in the early NAC stage. Survival analysis showed a significant correlation between the STI model and both recurrence-free survival (RFS) and overall survival (OS) in breast cancer patients. Further investigation revealed that favorable NAC responses predicted by the STI model were closely linked to upregulated immune-related genes and enhanced immune cell infiltration. Our study established a novel noninvasive STI model that integrates the spatiotemporal evolution of MRI before and during NAC to achieve early and accurate pCR prediction, offering potential guidance for personalized treatment. This study was supported by the National Natural Science Foundation of China (82302314, 62271448, 82171920, 81901711), Basic and Applied Basic Research Foundation of Guangdong Province (2022A1515110792, 2023A1515220097, 2024A1515010653), Medical Scientific Research Foundation of Guangdong Province (A2023073, A2024116), Science and Technology Projects in Guangzhou (2023A04J1275, 2024A03J1030, 2025A03J4163, 2025A03J4162); Guangzhou First People's Hospital Frontier Medical Technology Project (QY-C04).

The differential diagnosis of invasive pulmonary aspergillosis (IPA), pulmonary mucormycosis (PM), bacterial pneumonia (BP) and pulmonary tuberculosis (PTB) are challenging due to overlapping clinical and imaging features. Manual CT lesion segmentation is time-consuming, deep-learning (DL)-based segmentation models offer a promising solution, yet disease-specific models for these infections remain underexplored. We aimed to develop and validate dedicated CT segmentation models for IPA, PM, BP and PTB to enhance diagnostic accuracy. Methods：Retrospective multi-centre data (115 IPA, 53 PM, 130 BP, 125 PTB) were used for training/internal validation, with 21 IPA, 8PM, 30 BP and 31 PTB cases for external validation. Expert-annotated lesions served as ground truth. An improved 3D U-Net architecture was employed for segmentation, with preprocessing steps including normalisations, cropping and data augmentation. Performance was evaluated using Dice coefficients. Results：Internal validation achieved Dice scores of 78.83% (IPA), 93.38% (PM), 80.12% (BP) and 90.47% (PTB). External validation showed slightly reduced but robust performance: 75.09% (IPA), 77.53% (PM), 67.40% (BP) and 80.07% (PTB). The PM model demonstrated exceptional generalisability, scoring 83.41% on IPA data. Cross-validation revealed mutual applicability, with IPA/PTB models achieving > 75% Dice for each other's lesions. BP segmentation showed lower but clinically acceptable performance ( ＞72%), likely due to complex radiological patterns. Disease-specific DL segmentation models exhibited high accuracy, particularly for PM and PTB. While IPA and BP models require refinement, all demonstrated cross-disease utility, suggesting immediate clinical value for preliminary lesion annotation. Future efforts should enhance datasets and optimise models for intricate cases.

We aimed to evaluate the diagnostic performance of artificial intelligence (AI) in detecting coronary artery stenosis and calcified plaque on CT angiography (CTA), comparing its diagnostic performance with that of radiologists. A thorough search of the literature was performed using PubMed, Web of Science, and Embase, focusing on studies published until October 2024. Studies were included if they evaluated AI models in detecting coronary artery stenosis and calcified plaque on CTA. A bivariate random-effects model was employed to determine combined sensitivity and specificity. Study heterogeneity was assessed using I² statistics. The risk of bias was assessed using the revised quality assessment of diagnostic accuracy studies-2 tool, and the evidence level was graded using the Grading of Recommendations Assessment, Development and Evalutiuon (GRADE) system. Out of 1071 initially identified studies, 17 studies with 5560 patients and images were ultimately included for the final analysis. For coronary artery stenosis ≥50%, AI showed a sensitivity of 0.92 (95% CI: 0.88-0.95), specificity of 0.87 (95% CI: 0.80-0.92), and AUC of 0.96 (95% CI: 0.94-0.97), outperforming radiologists with sensitivity of 0.85 (95% CI: 0.67-0.94), specificity of 0.84 (95% CI: 0.62-0.94), and AUC of 0.91 (95% CI: 0.89-0.93). For stenosis ≥70%, AI achieved a sensitivity of 0.88 (95% CI: 0.70-0.96), specificity of 0.96 (95% CI: 0.90-0.99), and AUC of 0.98 (95% CI: 0.96-0.99). In calcified plaque detection, AI demonstrated a sensitivity of 0.93 (95% CI: 0.84-0.97), specificity of 0.94 (95% CI: 0.88-0.96), and AUC of 0.98 (95% CI: 0.96-0.99)." AI-based CT demonstrated superior diagnostic performance compared to clinicians in identifying ≥50% stenosis in coronary arteries and showed excellent diagnostic performance in recognizing ≥70% coronary artery stenosis and calcified plaque. However, limitations include retrospective study designs and heterogeneity in CTA technologies. Further external validation through prospective, multicenter trials is required to confirm these findings. The original findings of this research are included in the article. For additional inquiries, please contact the corresponding authors.

Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transitioning to Mild Cognitive Impairment (MCI) and evaluates its potential for predicting MCI risk. We enrolled 90 ESRD patients with 2-year follow-up, categorized as MCI converters (MCI_C, n=48) and non-converters (MCI_NC, n=42). Brain networks were constructed using baseline rs-fMRI and high angular resolution diffusion imaging, focusing on regional structural-functional coupling (SFC). A Support Vector Machine (SVM) model was used to identify brain regions associated with cognitive decline. Mediation analysis was conducted to explore the relationship between kidney function, brain network reconfiguration, and cognition. MCI_C patients showed decreased network efficiency in the structural network and compensatory changes in the functional network. Machine learning models using multimodal network features predicted MCI with high accuracy (AUC=0.928 for training set, AUC=0.903 for test set). SHAP analysis indicated that reduced hippocampal SFC was the most significant predictor of MCI_C. Mediation analysis revealed that altered brain network topology, particularly hippocampal SFC, mediated the relationship between kidney dysfunction and cognitive decline. This study provides new insights into the link between kidney function and cognition, offering potential clinical applications for structural and functional MRI biomarkers.