While total intracranial carotid artery calcification (ICAC) volume is an established stroke biomarker, growing evidence shows this aggregate metric ignores the critical influence of plaque location, since calcification in different segments carries distinct prognostic and procedural risks. However, a finer-grained, segment-specific quantification has remained technically infeasible. Conventional 3D models are forced to process downsampled volumes or isolated patches, sacrificing the global context required to resolve anatomical ambiguity and render reliable landmark localization. To overcome this, we reformulate the 3D challenge as a \textbf{Parallel Probabilistic Landmark Localization} task along the 1D axial dimension. We propose the \textbf{Depth-Sequence Transformer (DST)}, a framework that processes full-resolution CT volumes as sequences of 2D slices, learning to predict $N=6$ independent probability distributions that pinpoint key anatomical landmarks. Our DST framework demonstrates exceptional accuracy and robustness. Evaluated on a 100-patient clinical cohort with rigorous 5-fold cross-validation, it achieves a Mean Absolute Error (MAE) of \textbf{0.1 slices}, with \textbf{96\%} of predictions falling within a $\pm1$ slice tolerance. Furthermore, to validate its architectural power, the DST backbone establishes the best result on the public Clean-CC-CCII classification benchmark under an end-to-end evaluation protocol. Our work delivers the first practical tool for automated segment-specific ICAC analysis. The proposed framework provides a foundation for further studies on the role of location-specific biomarkers in diagnosis, prognosis, and procedural planning. Our code will be made publicly available.

Nasal bone fractures represent the most common facial skeletal injury, challenging both function and aesthetics. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based review analyzed 23 studies published within the past 5 years, selected from 998 records retrieved from PubMed, Embase, and Web of Science. Data from 1780 participants were extracted, focusing on diagnostic methods, surgical techniques, anesthesia protocols, and long-term outcomes. Ultrasound and artificial intelligence-based algorithms improved diagnostic accuracy, while telephone triage streamlined necessary encounters. Navigation-assisted reduction, ballooning, and septal reduction with polydioxanone plates improved outcomes. Anesthetic approaches ranged from local nerve blocks to general anesthesia with intraoperative administration of lidocaine, alongside techniques to manage pain from nasal pack removal postoperatively. Long-term follow-up demonstrated improved quality of life, breathing function, and aesthetic satisfaction with timely and individualized treatment. This review highlights the trend toward personalized, technology-assisted approaches in nasal fracture management, highlighting areas for future research.

Lacunes, which are small fluid-filled cavities in the brain, are signs of cerebral small vessel disease and have been clinically associated with various neurodegenerative and cerebrovascular diseases. Hence, accurate detection of lacunes is crucial and is one of the initial steps for the precise diagnosis of these diseases. However, developing a robust and consistently reliable method for detecting lacunes is challenging because of the heterogeneity in their appearance, contrast, shape, and size. To address the above challenges, in this study, we propose a lacune detection method using the Segment Anything Model (SAM), guided by point prompts from a candidate prompt generator. The prompt generator initially detects potential lacunes with a high sensitivity using a composite loss function. The SAM model selects true lacunes by delineating their characteristics from mimics such as the sulcus and enlarged perivascular spaces, imitating the clinicians strategy of examining the potential lacunes along all three axes. False positives were further reduced by adaptive thresholds based on the region-wise prevalence of lacunes. We evaluated our method on two diverse, multi-centric MRI datasets, VALDO and ISLES, comprising only FLAIR sequences. Despite diverse imaging conditions and significant variations in slice thickness (0.5-6 mm), our method achieved sensitivities of 84% and 92%, with average false positive rates of 0.05 and 0.06 per slice in ISLES and VALDO datasets respectively. The proposed method outperformed the state-of-the-art methods, demonstrating its effectiveness in lacune detection and quantification.

Super-resolution imaging has emerged as a rapidly advancing field in diagnostic ultrasound. Ultrasound Localization Microscopy (ULM) achieves sub-wavelength precision in microvasculature imaging by tracking gas microbubbles (MBs) flowing through blood vessels. However, MB localization faces challenges due to dynamic point spread functions (PSFs) caused by harmonic and sub-harmonic emissions, as well as depth-dependent PSF variations in ultrasound imaging. Additionally, deep learning models often struggle to generalize from simulated to in vivo data due to significant disparities between the two domains. To address these issues, we propose a novel approach using the DEformable DEtection TRansformer (DE-DETR). This object detection network tackles object deformations by utilizing multi-scale feature maps and incorporating a deformable attention module. We further refine the super-resolution map by employing a KDTree algorithm for efficient MB tracking across consecutive frames. We evaluated our method using both simulated and in vivo data, demonstrating improved precision and recall compared to current state-of-the-art methodologies. These results highlight the potential of our approach to enhance ULM performance in clinical applications.

<b>BACKGROUND</b>. Artificial intelligence (AI) tools for evaluating low-dose CT (LDCT) lung cancer screening examinations are used predominantly for assisting radiologists' interpretations. Alternate utilization scenarios (e.g., use of AI as a prescreener or backup) warrant consideration. <b>OBJECTIVE</b>. The purpose of this study was to evaluate the impact of different AI utilization scenarios on diagnostic outcomes and interpretation times for LDCT lung cancer screening. <b>METHODS</b>. This retrospective study included 366 individuals (358 men, 8 women; mean age, 64 years) who underwent LDCT from May 2017 to December 2017 as part of an earlier prospective lung cancer screening trial. Examinations were interpreted by one of five readers, who reviewed their assigned cases in two sessions (with and without a commercial AI computer-aided detection tool). These interpretations were used to reconstruct simulated AI utilization scenarios: as an assistant (i.e., radiologists interpret all examinations with AI assistance), as a prescreener (i.e., radiologists only interpret examinations with a positive AI result), or as backup (i.e., radiologists reinterpret examinations when AI suggests a missed finding). A group of thoracic radiologists determined the reference standard. Diagnostic outcomes and mean interpretation times were assessed. Decision-curve analysis was performed. <b>RESULTS</b>. Compared with interpretation without AI (recall rate, 22.1%; per-nodule sensitivity, 64.2%; per-examination specificity, 88.8%; mean interpretation time, 164 seconds), AI as an assistant showed higher recall rate (30.3%; <i>p</i> < .001), lower per-examination specificity (81.1%), and no significant change in per-nodule sensitivity (64.8%; <i>p</i> = .86) or mean interpretation time (161 seconds; <i>p</i> = .48); AI as a prescreener showed lower recall rate (20.8%; <i>p</i> = .02) and mean interpretation time (143 seconds; <i>p</i> = .001), higher per-examination specificity (90.3%; <i>p</i> = .04), and no significant difference in per-nodule sensitivity (62.9%; <i>p</i> = .16); and AI as a backup showed increased recall rate (33.6%; <i>p</i> < .001), per-examination sensitivity (66.4%; <i>p</i> < .001), and mean interpretation time (225 seconds; <i>p</i> = .001), with lower per-examination specificity (79.9%; <i>p</i> < .001). Among scenarios, only AI as a prescreener demonstrated higher net benefit than interpretation without AI; AI as an assistant had the least net benefit. <b>CONCLUSION</b>. Different AI implementation approaches yield varying outcomes. The findings support use of AI as a prescreener as the preferred scenario. <b>CLINICAL IMPACT</b>. An approach whereby radiologists only interpret LDCT examinations with a positive AI result can reduce radiologists' workload while preserving sensitivity.

The rapid evolution of deepfake technology poses critical challenges to healthcare systems, particularly in safeguarding the integrity of medical imaging, electronic health records (EHR), and telemedicine platforms. As autonomous AI becomes increasingly integrated into smart healthcare, the potential misuse of deepfakes to manipulate sensitive healthcare data or impersonate medical professionals highlights the urgent need for robust and adaptive detection mechanisms. In this work, we propose DProm, a dynamic deepfake detection framework leveraging visual prompt tuning (VPT) with a pre-trained Swin Transformer. Unlike traditional static detection models, which struggle to adapt to rapidly evolving deepfake techniques, DProm fine-tunes a small set of visual prompts to efficiently adapt to new data distributions with minimal computational and storage requirements. Comprehensive experiments demonstrate that DProm achieves state-of-the-art performance in both static cross-dataset evaluations and dynamic scenarios, ensuring robust detection across diverse data distributions. By addressing the challenges of scalability, adaptability, and resource efficiency, DProm offers a transformative solution for enhancing the security and trustworthiness of autonomous AI systems in healthcare, paving the way for safer and more reliable smart healthcare applications.

In most medical centers, particularly in primary hospitals, non-contrast computed tomography (NCCT) serves as the primary imaging modality for diagnosing acute ischemic stroke. However, due to the small density difference between the infarct and the surrounding normal brain tissue on NCCT images within the initial 6 h post-onset, it poses significant challenges in promptly and accurately positioning and quantifying the infarct at the early stage. To investigate whether a radiomics-based model using NCCT could effectively assess the risk of acute ischemic stroke (AIS). This study proposed a machine learning (ML) for infarct detection, enabling automated quantitative assessment of AIS lesions on NCCT images. In this retrospective study, NCCT images from 228 patients with AIS (< 6 h from onset) were included, and paired with MRI-diffusion-weighted imaging (DWI) images (attained within 1 to 7 days of onset). NCCT and DWI images were co-registered using the Elastix toolbox. The internal dataset (153 AIS patients) included 179 AIS VOIs and 153 non-AIS VOIs as the training and validation groups. Subsequent cases (75 patients) after 2021 served as the independent test set, comprising 94 AIS VOIs and 75 non-AIS VOIs. The random forest (RF) model demonstrated robust diagnostic performance across the training, validation, and independent test sets. The areas under the receiver operating characteristic (ROC) curves were 0.858 (95% CI: 0.808-0.908), 0.829 (95% CI: 0.748-0.910), and 0.789 (95% CI: 0.717-0.860), respectively. Accuracies were 79.399%, 77.778%, and 73.965%, while sensitivities were 81.679%, 77.083%, and 68.085%. Specificities were 76.471%, 78.431%, and 81.333%, respectively. NCCT-based radiomics combined with a machine learning model could discriminate between AIS and non-AIS patients within less than 6 h of onset. This approach holds promise for improving early stroke diagnosis and patient outcomes. Not applicable.

To develop a deep learning model to detect apparent and occult scaphoid fractures from plain wrist radiographs and to compare the model's diagnostic performance with that of a group of experts. A dataset comprising 408 patients, 410 wrists, and 1011 radiographs was collected. 718 of these radiographs contained a scaphoid fracture, verified by magnetic resonance imaging or computed tomography scans. 58 of these fractures were occult. The images were divided into training, test, and occult fracture test sets. The images were annotated by marking the scaphoid bone and the possible fracture area. The performance of the developed DL model was compared with the ground truth and the assessments of three clinical experts. The DL model achieved a sensitivity of 0.86 (95 % CI: 0.75-0.93) and a specificity of 0.83 (0.64-0.94). The model's accuracy was 0.85 (0.76-0.92), and the area under the receiver operating characteristics curve was 0.92 (0.86-0.97). The clinical experts' sensitivity ranged from 0.77 to 0.89, and specificity from 0.83 to 0.97. The DL model detected 24 of 58 (41 %) occult fractures, compared to 10.3 %, 13.7 %, and 6.8 % by the clinical experts. Detecting scaphoid fractures using a segmentation-based DL model is feasible and comparable to previously developed DL models. The model performed similarly to a group of experts in identifying apparent scaphoid fractures and demonstrated higher diagnostic accuracy in detecting occult fractures. The improvement in occult fracture detection could enhance patient care.

Magnetic resonance imaging of the lumbar spine is a key technique for clarifying the cause of disease. The greatest challenges today are the repetitive and time-consuming process of interpreting these complex MR images and the problem of unequal diagnostic results from physicians with different levels of experience. To address these issues, in this study, an improved YOLOv8 model (GE-YOLOv8) that combines a gradient search module and efficient channel attention was developed. To address the difficulty of intervertebral disc feature extraction, the GS module was introduced into the backbone network, which enhances the feature learning ability for the key structures through the gradient splitting strategy, and the number of parameters was reduced by 2.1%. The ECA module optimizes the weights of the feature channels and enhances the sensitivity of detection for small-target lesions, and the mAP50 was improved by 4.4% compared with that of YOLOv8. GE-YOLOv8 demonstrated the significance of this innovation on the basis of a P value <.001, with YOLOv8 as the baseline. The experimental results on a dataset from the Pingtan Branch of Union Hospital of Fujian Medical University and an external test dataset show that the model has excellent accuracy.

To train and test an AI-based algorithm for automated detection of focal bone marrow lesions (FL) from MRI. This retrospective feasibility study included 444 patients with monoclonal plasma cell disorders. For this feasibility study, only FLs in the left pelvis were included. Using the nnDetection framework, the algorithm was trained based on 334 patients with 494 FLs from center 1, and was tested on an internal test set (36 patients, 89 FLs, center 1) and a multicentric external test set (74 patients, 262 FLs, centers 2-11). Mean average precision (mAP), F1-score, sensitivity, positive predictive value (PPV), and Spearman correlation coefficient between automatically determined and actual number of FLs were calculated. On the internal/external test set, the algorithm achieved a mAP of 0.44/0.34, F1-Score of 0.54/0.44, sensitivity of 0.49/0.34, and a PPV of 0.61/0.61, respectively. In two subsets of the external multicentric test set with high imaging quality, the performance nearly matched that of the internal test set, with mAP of 0.45/0.41, F1-Score of 0.50/0.53, sensitivity of 0.44/0.43, and a PPV of 0.60/0.71, respectively. There was a significant correlation between the automatically determined and actual number of FLs on both the internal (r=0.51, p=0.001) and external multicentric test set (r=0.59, p<0.001). This study demonstrates that the automated detection of FLs from MRI, and thereby the automated assessment of the number of FLs, is feasible.