Medical image segmentation suffers from data scarcity, particularly in polyp detection where annotation requires specialized expertise. We present SynDiff, a framework combining text-guided synthetic data generation with efficient diffusion-based segmentation. Our approach employs latent diffusion models to generate clinically realistic synthetic polyps through text-conditioned inpainting, augmenting limited training data with semantically diverse samples. Unlike traditional diffusion methods requiring iterative denoising, we introduce direct latent estimation enabling single-step inference with T x computational speedup. On CVC-ClinicDB, SynDiff achieves 96.0% Dice and 92.9% IoU while maintaining real-time capability suitable for clinical deployment. The framework demonstrates that controlled synthetic augmentation improves segmentation robustness without distribution shift. SynDiff bridges the gap between data-hungry deep learning models and clinical constraints, offering an efficient solution for deployment in resourcelimited medical settings.

Interpretability is critical in high-stakes domains such as medical imaging, where understanding model decisions is essential for clinical adoption. In this work, we introduce Sparse Autoencoder (SAE)-based interpretability to breast imaging by analyzing {Mammo-CLIP}, a vision--language foundation model pretrained on large-scale mammogram image--report pairs. We train a patch-level \texttt{Mammo-SAE} on Mammo-CLIP to identify and probe latent features associated with clinically relevant breast concepts such as \textit{mass} and \textit{suspicious calcification}. Our findings reveal that top activated class level latent neurons in the SAE latent space often tend to align with ground truth regions, and also uncover several confounding factors influencing the model's decision-making process. Additionally, we analyze which latent neurons the model relies on during downstream finetuning for improving the breast concept prediction. This study highlights the promise of interpretable SAE latent representations in providing deeper insight into the internal workings of foundation models at every layer for breast imaging.

To compare the trueness of artificial intelligence (AI)-based, manual, and global segmentation protocols by superimposing the resulting segmented 3D models onto reference gold standard surface scan models. Twelve dry human mandibles were used. A cone beam computed tomography (CBCT) scanner was used to scan the mandibles, and the acquired digital imaging and communications in medicine (DICOM) files were segmented using three protocols: global thresholding, manual, and AI-based segmentation (Diagnocat; Diagnocat, San Francisco, CA). The segmented files were exported as study 3D models. A structured light surface scanner (GoSCAN Spark; Creaform 3D, Levis, Canada) was used to scan all mandibles, and the resulting reference 3D models were exported. The study 3D models were compared with the respective reference 3D models by using a mesh comparison software (Geomagic Design X; 3D Systems Inc, Rock Hill, SC). Root mean square (RMS) error values were recorded to measure the magnitude of deviation (trueness), and color maps were obtained to visualize the differences. Comparisons of the trueness of three segmentation methods for differences in RMS were made using repeated measures analysis of variance (ANOVA). A two-sided 5% significance level was used for all tests in the software program. AI-based segmentations had significantly higher RMS values than manual segmentations for the entire mandible (p < 0.001), alveolar process (p < 0.001), and body of the mandible (p < 0.001). AI-based segmentations had significantly lower RMS values than manual segmentations for the condyles (p = 0.018) and ramus (p = 0.013). No significant differences were found between the AI-based and manual segmentations for the coronoid process (p = 0.275), symphysis (p = 0.346), and angle of the mandible (p = 0.344). Global thresholding had significantly higher RMS values than manual segmentations for the alveolus (p < 0.001), angle of the mandible (p < 0.001), body of the mandible (p < 0.001), condyles (p < 0.001), coronoid (p = 0.002), entire mandible (p < 0.001), ramus (p < 0.001), and symphysis (p < 0.001). Global thresholding had significantly higher RMS values than AI-based segmentation for the alveolar process (p = 0.002), angle of the mandible (p < 0.001), body of the mandible (p < 0.001), condyles (p < 0.001), coronoid (p = 0.017), mandible (p < 0.001), ramus (p < 0.001), and symphysis (p < 0.001). AI-based segmentations produced lower RMS values, indicating truer 3D models, compared to global thresholding, and showed no significant differences in some areas compared to manual segmentation. Thus, AI-based segmentation offers a level of segmentation trueness acceptable for use as an alternative to manual or global thresholding segmentation protocols.

Spinal navigation systems require pre- and/or intra-operative 3-D imaging, which expose young patients to harmful radiation. We assessed a scoliosis-specific MRI-protocol that provides T2-weighted MRI and AI-generated synthetic-CT (sCT) scans, through deep learning algorithms. This study aims to compare MRI-based synthetic-CT spinal navigation to CT for safety and accuracy of pedicle screw planning and placement at thoracic and lumbar levels. Spines of 5 cadavers were scanned with thin-slice CT and the scoliosis-specific MRI-protocol (to create sCT). Preoperatively, on both CT and sCT screw trajectories were planned. Subsequently, four spine surgeons performed surface-matched, navigated placement of 2.5 mm k-wires in all pedicles from T3 to L5. Randomization for CT/sCT, surgeon and side was performed (1:1 ratio). On postoperative CT-scans, virtual screws were simulated over k-wires. Maximum angulation, distance between planned and postoperative screw positions and medial breach rate (Gertzbein-Robbins classification) were assessed. 140 k-wires were inserted, 3 were excluded. There were no pedicle breaches > 2 mm. Of sCT-guided screws, 59 were grade A and 10 grade B. For the CT-guided screws, 47 were grade A and 21 grade B (p = 0.022). Average distance (± SD) between intraoperative and postoperative screw positions was 2.3 ± 1.5 mm in sCT-guided screws, and 2.4 ± 1.8 mm for CT (p = 0.78), average maximum angulation (± SD) was 3.8 ± 2.5° for sCT and 3.9 ± 2.9° for CT (p = 0.75). MRI-based, AI-generated synthetic-CT spinal navigation allows for safe and accurate planning and placement of thoracic and lumbar pedicle screws in a cadaveric model, without significant differences in distance and angulation between planned and postoperative screw positions compared to CT.

PXseg, a novel approach for tooth segmentation, numbering and abnormal morphology detection in panoramic X-ray (PX), was designed and promoted through optimizing annotation and applying pre-training. Derived from multicenter, ctPXs generated from cone beam computed tomography (CBCT) with accurate 3D labels were utilized for pre-training, while conventional PXs (cPXs) with 2D labels were input for training. Visual and statistical analyses were conducted using the internal dataset to assess segmentation and numbering performances of PXseg and compared with the model without ctPX pre-training, while the accuracy of PXseg detecting abnormal teeth was evaluated using the external dataset consisting of cPXs with complex dental diseases. Besides, a diagnostic testing was performed to contrast diagnostic efficiency with and without PXseg's assistance. The DSC and F1-score of PXseg in tooth segmentation reached 0.882 and 0.902, which increased by 4.6% and 4.0% compared to the model without pre-training. For tooth numbering, the F1-score of PXseg reached 0.943 and increased by 2.2%. Based on the promotion in segmentation, the accuracy of abnormal tooth morphology detection exceeded 0.957 and was 4.3% higher. A website was constructed to assist in PX interpretation, and the diagnostic efficiency was greatly enhanced with the assistance of PXseg. The application of accurate labels in ctPX increased the pre-training weight of PXseg and improved the training effect, achieving promotions in tooth segmentation, numbering and abnormal morphology detection. Rapid and accurate results provided by PXseg streamlined the workflow of PX diagnosis, possessing significant clinical application prospect.

The prevalence of asymptomatic pancreatic cysts is increasing due to advances in imaging techniques. Among these, intraductal papillary mucinous neoplasms (IPMNs) are most common, with potential for malignant transformation, often necessitating close follow-up. This study evaluates novel MRI techniques for the assessment of IPMN. From May to December 2023, 59 patients undergoing abdominal MRI were retrospectively enrolled. Examinations were conducted on 3-Tesla scanners using a Deep-Learning Accelerated Half-Fourier Single-Shot Turbo Spin-Echo (HASTE_DL) and standard HASTE (HASTE_S) sequence. Two readers assessed minimum detectable lesion size and lesion-to-parenchyma contrast quantitatively, and qualitative assessments focused on image quality. Statistical analyses included the Wilcoxon signed-rank and chi-squared tests. HASTE_DL demonstrated superior overall image quality (p < 0.001), with higher sharpness and contrast ratings (p < 0.001, p = 0.112). HASTE_DL showed enhanced conspicuity of IPMN (p < 0.001) and lymph nodes (p < 0.001), with more frequent visualization of IPMN communication with the pancreatic duct (p < 0.001). Visualization of complex features (dilated pancreatic duct, septa, and mural nodules) was superior in HASTE_DL (p < 0.001). The minimum detectable cyst size was significantly smaller for HASTE_DL (4.17 mm ± 3.00 vs. 5.51 mm ± 4.75; p < 0.001). Inter-reader agreement was for (к 0.936) for HASTE_DL, slightly lower (к 0.885) for HASTE_S. HASTE_DL in IPMN imaging provides superior image quality and significantly reduced scan times. Given the increasing prevalence of IPMN and the ensuing clinical need for fast and precise imaging, HASTE_DL improves the availability and quality of patient care. Question Are there advantages of deep-learning-accelerated MRI in imaging and assessing intraductal papillary mucinous neoplasms (IPMN)? Findings Deep-Learning Accelerated Half-Fourier Single-Shot Turbo Spin-Echo (HASTE_DL) demonstrated superior image quality, improved conspicuity of "worrisome features" and detection of smaller cysts, with significantly reduced scan times. Clinical relevance HASTEDL provides faster, high-quality MRI imaging, enabling improved diagnostic accuracy and timely risk stratification for IPMN, potentially enhancing patient care and addressing the growing clinical demand for efficient imaging of IPMN.

Detecting malignant transformation of sinonasal inverted papilloma (SIP) into squamous cell carcinoma (SIP-SCC) before surgery is a clinical need. We aimed to explore the value of deep learning (DL) that leverages nasal endoscopy and T2-weighted magnetic resonance imaging (T2W-MRI) for automated tumor segmentation and differentiation between SIP and SIP-SCC. We conducted a retrospective analysis of 174 patients diagnosed with SIPs, who were divided into a training cohort (n = 121) and a testing cohort (n = 53). Three DL architectures were utilized to train automated segmentation models for endoscopic and T2W-MRI images. DL scores predicting SIP-SCC were generated using DenseNet121 from both modalities and combined to create a dual-modality DL nomogram. The diagnostic performance of the DL models was assessed alongside two radiologists, evaluated through the area under the receiver operating characteristic curve (AUROC), with comparisons made using the Delong method. In the testing cohort, the FCN_ResNet101 and VNet exhibited superior performance in automated segmentation, achieving mean dice similarity coefficients of 0.95 ± 0.03 for endoscopy and 0.93 ± 0.02 for T2W-MRI, respectively. The dual-modality DL nomogram based on automated segmentation demonstrated the highest predictive performance for SIP-SCC (AUROC 0.865), outperforming the radiology resident (AUROC 0.672, p = 0.071) and the attending radiologist (AUROC 0.707, p = 0.066), with a trend toward significance. Notably, both radiologists improved their diagnostic performance with the assistance of the DL nomogram (AUROCs 0.734 and 0.834). The DL framework integrating endoscopy and T2W-MRI offers a fully automated predictive tool for SIP-SCC. The integration of endoscopy and T2W-MRI within a well-established DL framework enables fully automated prediction of SIP-SSC, potentially improving decision-making for patients with suspicious SIP. Detecting the transformation of SIP into SIP-SCC before surgery is both critical and challenging. Endoscopy and T2W-MRI were integrated using DL for predicting SIP-SCC. The dual-modality DL nomogram outperformed two radiologists. The nomogram may improve decision-making for patients with suspicious SIP.

To construct an artificial intelligence (AI)-assisted model for identifying the infraorbital posterior ethmoid cells (IPECs) based on deep learning using sagittal CT images. Sagittal CT images of 277 samples with and 142 samples without IPECs were retrospectively collected. An experienced radiologist engaged in the relevant aspects picked a sagittal CT image that best showed IPECs. The images were randomly assigned to the training and test sets, with 541 sides in the training set and 97 sides in the test set. The training set was used to perform a five-fold cross-validation, and the results of each fold were used to predict the test set. The model was built using nnUNet, and its performance was evaluated using Dice and standard classification metrics. The model achieved a Dice coefficient of 0.900 in the training set and 0.891 in the additional set. Precision was 0.965 for the training set and 1.000 for the additional set, while sensitivity was 0.981 and 0.967, respectively. A comparison of the diagnostic efficacy between manual outlining by a less-experienced radiologist and AI-assisted outlining showed a significant improvement in detection efficiency (P < 0.05). The AI model aided correctly in identifying and outlining all IPECs, including 12 sides that the radiologist should improve portraying. AI models can help radiologists identify the IPECs, which can further prompt relevant clinical interventions.

To compare the quality of AI-ADC maps and standard ADC maps in a multi-reader study. Multi-reader study included 74 consecutive patients (median age = 66 years, [IQR = 57.25-71.75 years]; median PSA = 4.30 ng/mL [IQR = 1.33-7.75 ng/mL]) with suspected or confirmed PCa, who underwent mpMRI between October 2023 and January 2024. The study was conducted in two rounds, separated by a 4-week wash-out period. In each round, four readers evaluated T2W-MRI and standard or AI-generated ADC (AI-ADC) maps. Fleiss' kappa, quadratic-weighted Cohen's kappa statistics were used to assess inter-reader agreement. Linear mixed effect models were employed to compare the quality evaluation of standard versus AI-ADC maps. AI-ADC maps exhibited significantly enhanced imaging quality compared to standard ADC maps with higher ratings in windowing ease (β = 0.67 [95% CI 0.30-1.04], p < 0.05), prostate boundary delineation (β = 1.38 [95% CI 1.03-1.73], p < 0.001), reductions in distortion (β = 1.68 [95% CI 1.30-2.05], p < 0.001), noise (β = 0.56 [95% CI 0.24-0.88], p < 0.001). AI-ADC maps reduced reacquisition requirements for all readers (β = 2.23 [95% CI 1.69-2.76], p < 0.001), supporting potential workflow efficiency gains. No differences were observed between AI-ADC and standard ADC maps' inter-reader agreement. Our multi-reader study demonstrated that AI-ADC maps improved prostate boundary delineation, had lower image noise, fewer distortions, and higher overall image quality compared to ADC maps. Question Can we synthesize apparent diffusion coefficient (ADC) maps with AI to achieve higher quality maps? Findings On average, readers rated quality factors of AI-ADC maps higher than ADC maps in 34.80% of cases, compared to 5.07% for ADC (p < 0.01). Clinical relevance AI-ADC maps may serve as a reliable diagnostic support tool thanks to their high quality, particularly when the acquired ADC maps include artifacts.

The specific role of lysophospholipids (LysoPLs) in the pathogenesis of chronic obstructive pulmonary disease (COPD) is not yet fully understood. We determined serum LysoPLs in 20 patients with stable COPD and 20 healthy smokers using liquid chromatography-mass spectrometry (LC-MS) and matching with the lipidIMMS library, and integrated these data with spirometry, systemic inflammation markers, and quantitative chest CT generated by an automated 3D-U-Net artificial intelligence algorithm model. Our findings identified three differential LysoPLs, lysophosphatidylcholine (LPC) (18:0), LPC (18:1), and LPC (18:2), which were significantly lower in the COPD group than in healthy smokers. Significant negative correlations were observed between these LPCs and the inflammatory markers C-reactive protein and Interleukin-6. LPC (18:0) and (18:2) correlated with higher post-bronchodilator FEV1, and the latter also correlated with FEV1% predicted, forced vital capacity (FVC), and FEV1/FVC ratio. Additionally, these three LPCs were negatively correlated with the volume and percentage of low attenuation areas (LAA), high-attenuation areas (HAA), honeycombing, reticular patterns, ground-glass opacities (GGO), and consolidation on CT imaging. In the patients with COPD, the three LPCs were most significantly associated with HAA and GGO. In conclusion, patients with stable COPD exhibited a unique LysoPL metabolism profile, with LPC (18:0), LPC (18:1), and LPC (18:2) being the most significantly altered lipid molecules. The reduction in these three LPCs was associated with impaired pulmonary function and were also linked to a greater extent of emphysema and interstitial lung abnormalities.