Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome prediction to clarify value drivers and execution risks. Convolutional, recurrent, and transformer architectures now trigger large‑vessel‑occlusion alerts, delineate ischemic core in seconds, and forecast 90‑day function. Commercial deployments-RapidAI, Viz.ai, Aidoc-report double‑digit reductions in door‑to‑needle metrics and expanded thrombectomy eligibility. However, dataset bias, opaque reasoning, and limited external validation constrain scalability. Hybrid image‑plus‑clinical models elevate predictive accuracy but intensify data‑governance demands. AI can operationalize precision stroke care, but enterprise‑grade adoption requires federated data pipelines, explainable‑AI dashboards, and fit‑for‑purpose regulation. Prospective multicenter trials and continuous lifecycle surveillance are mandatory to convert algorithmic promise into reproducible, equitable patient benefit.

This study evaluates adherence to the latest Society for Vascular Surgery (SVS) guidelines on imaging surveillance, physician evaluation, and surgical intervention for abdominal aortic aneurysm (AAA). AI-based natural language processing applied retrospectively identified AAA patients from imaging scans at a tertiary care center between January-March 2019 and 2021, excluding the pandemic period. Retrospective chart review assessed demographics, comorbidities, imaging, and follow-up adherence. Statistical significance was set at p<0.05. Among 479 identified patients, 279 remained in the final cohort following exclusion of deceased patients. Imaging surveillance adherence was 67.7% (189/279), with males comprising 72.5% (137/189) (Figure 1). The mean age for adherent patients was 73.9 (SD ±9.5) vs. 75.2 (SD ±10.8) for non-adherent patients (Table 1). Adherent females were significantly younger than non-adherent females (76.7 vs. 81.1 years; p=0.003) with no significant age difference in adherent males. Adherent patients were more likely to be evaluated by a vascular provider within six months (p<0.001), but aneurysm size did not affect imaging adherence: 3.0-4.0cm (p=0.24), 4.0-5.0cm (p=0.88), >5.0cm (p=0.29). Based on SVS surgical criteria, 18 males (AAA >5.5cm) and 17 females (AAA >5.0cm) qualified for intervention and repair rates increased in 2021. 34 males (20 in 2019 v. 14 in 2021) and 7 females (2021 only) received surgical intervention below the threshold for repair. Despite consistent SVS guidelines, adherence remains moderate. AI-based detection and follow-up algorithms may enhance adherence and long-term AAA patient outcomes, however further research is needed to assess the specific impacts of AI.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early lung cancer detection improves lung cancer-related mortality and survival. This report summarizes presentations and panel discussions from a webinar, "The Present and Future of Lung Cancer Screening: Latest Evidence and AI Perspectives." The webinar provided the perspectives of experts from the United States, United Kingdom, and China on evidence-based recommendations and management in lung cancer screening (LCS), barriers, and the role of artificial intelligence (AI). With several countries now incorporating the utilization of AI in their screening programs, AI offers potential solutions to some of the challenges associated with LCS.

Cephalometric landmark detection is essential for orthodontic diagnostics and treatment planning. Nevertheless, the scarcity of samples in data collection and the extensive effort required for manual annotation have significantly impeded the availability of diverse datasets. This limitation has restricted the effectiveness of deep learning-based detection methods, particularly those based on large-scale vision models. To address these challenges, we have developed an innovative data generation method capable of producing diverse cephalometric X-ray images along with corresponding annotations without human intervention. To achieve this, our approach initiates by constructing new cephalometric landmark annotations using anatomical priors. Then, we employ a diffusion-based generator to create realistic X-ray images that correspond closely with these annotations. To achieve precise control in producing samples with different attributes, we introduce a novel prompt cephalometric X-ray image dataset. This dataset includes real cephalometric X-ray images and detailed medical text prompts describing the images. By leveraging these detailed prompts, our method improves the generation process to control different styles and attributes. Facilitated by the large, diverse generated data, we introduce large-scale vision detection models into the cephalometric landmark detection task to improve accuracy. Experimental results demonstrate that training with the generated data substantially enhances the performance. Compared to methods without using the generated data, our approach improves the Success Detection Rate (SDR) by 6.5%, attaining a notable 82.2%. All code and data are available at: https://um-lab.github.io/cepha-generation

Supracondylar humerus fractures in children are among the most common elbow fractures in pediatrics. However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. Unfortunately, labeled samples for pediatric supracondylar fractures are scarce and difficult to obtain. To address this issue, this paper introduces a deep learning-based multi-scale patch residual network (MPR) for the automatic detection and localization of subtle pediatric supracondylar fractures. The MPR framework combines a CNN for automatic feature extraction with a multi-scale generative adversarial network to model skeletal integrity using healthy samples. By leveraging healthy images to learn the normal skeletal distribution, the approach reduces the dependency on labeled fracture data and effectively addresses the challenges posed by limited pediatric datasets. Datasets from two different hospitals were used, with data augmentation techniques applied during both training and validation. On an independent test set, the proposed model achieves an accuracy of 90.5%, with 89% sensitivity, 92% specificity, and an F1 score of 0.906-outperforming the diagnostic accuracy of emergency medicine physicians and approaching that of pediatric radiologists. Furthermore, the model demonstrates a fast inference speed of 1.1 s per sheet, underscoring its substantial potential for clinical application.

Background and ObjectivesBottom-of-sulcus dysplasia (BOSD) is a diagnostically challenging subtype of focal cortical dysplasia, 60% being missed on patients first MRI. Automated MRI-based detection methods have been developed for focal cortical dysplasia, but not BOSD specifically. Use of FDG-PET alongside MRI is not established in automated methods. We report the development and performance of an automated BOSD detector using combined MRI+PET data. MethodsThe training set comprised 54 mostly operated patients with BOSD. The test sets comprised 17 subsequently diagnosed patients with BOSD from the same center, and 12 published patients from a different center. 81% patients across training and test sets had reportedly normal first MRIs and most BOSDs were <1.5cm3. In the training set, 12 features from T1-MRI, FLAIR-MRI and FDG-PET were evaluated using a novel "pseudo-control" normalization approach to determine which features best distinguished dysplastic from normal-appearing cortex. Using the Multi-centre Epilepsy Lesion Detection groups machine-learning detection method with the addition of FDG-PET, neural network classifiers were then trained and tested on MRI+PET features, MRI-only and PET-only. The proportion of patients whose BOSD was overlapped by the top output cluster, and the top five output clusters, were assessed. ResultsCortical and subcortical hypometabolism on FDG-PET were superior in discriminating dysplastic from normal-appearing cortex compared to MRI features. When the BOSD detector was trained on MRI+PET features, 87% BOSDs were overlapped by one of the top five clusters (69% top cluster) in the training set, 76% in the prospective test set (71% top cluster) and 75% in the published test set (42% top cluster). Cluster overlap was similar when the detector was trained and tested on PET-only features but lower when trained and tested on MRI-only features. ConclusionDetection of BOSD is possible using established MRI-based automated detection methods, supplemented with FDG-PET features and trained on a BOSD-specific cohort. In clinical practice, an MRI+PET BOSD detector could improve assessment and outcomes in seemingly MRI-negative patients being considered for epilepsy surgery.

The implementation of artificial intelligence (AI), particularly Viz.ai software in stroke care, has emerged as a promising tool to enhance the detection of large vessel occlusion (LVO) and to improve stroke workflow metrics and patient outcomes. The aim of this systematic review and meta-analysis is to evaluate the impact of Viz.ai on stroke workflow efficiency in hospitals and on patients' outcomes. Following the PRISMA guidelines, we conducted a comprehensive search on electronic databases, including PubMed, Web of Science, and Scopus databases, to obtain relevant studies until 25 October 2024. Our primary outcomes were door-to-groin puncture (DTG) time, CT scan-to-start of endovascular treatment (EVT) time, CT scan-to-recanalization time, and door-in-door-out time. Secondary outcomes included symptomatic intracranial hemorrhage (ICH), any ICH, mortality, mRS score < 2 at 90 days, and length of hospital stay. A total of 12 studies involving 15,595 patients were included in our analysis. The pooled analysis demonstrated that the implementation of the Viz.ai algorithm was associated with lesser CT scan to EVT time (SMD -0.71, 95% CI [-0.98, -0.44], p < 0.001) and DTG time (SMD -0.50, 95% CI [-0.66, -0.35], p < 0.001) as well as CT to recanalization time (SMD -0.55, 95% CI [-0.76, -0.33], p < 0.001). Additionally, patients in the post-AI group had significantly lower door-in door-out time than the pre-AI group (SMD -0.49, 95% CI [-0.71, -0.28], p < 0.001). Despite the workflow metrics improvement, our analysis did not reveal statistically significant differences in patient clinical outcomes (p > 0.05). Our results suggest that the integration of the Viz.ai platform in stroke care holds significant potential for reducing EVT delays in patients with LVO and optimizing stroke flow metrics in comprehensive stroke centers. Further studies are required to validate its efficacy in improving clinical outcomes in patients with LVO.

Accurate diagnosis of orthopedic injuries, especially pelvic and hip fractures, is vital in trauma management. While pelvic radiographs (PXRs) are widely used, misdiagnosis is common. This study proposes an automated system that uses convolutional neural networks (CNNs) to detect potential fracture areas and predict fracture conditions, aiming to outperform traditional object detection-based systems. We developed two deep learning models for hip fracture detection and prediction, trained on PXRs from three hospitals. The first model utilized automated hip area detection, cropping, and classification of the resulting patches. The images were preprocessed using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The YOLOv5 architecture was employed for the object detection model, while three different pre-trained deep neural network (DNN) architectures were used for classification, applying transfer learning. Their performance was evaluated on a test dataset, and compared with 35 clinicians. YOLOv5 achieved a 92.66% accuracy on regular images and 88.89% on CLAHE-enhanced images. The classifier models, MobileNetV2, Xception, and InceptionResNetV2, achieved accuracies between 94.66% and 97.67%. In contrast, the clinicians demonstrated a mean accuracy of 84.53% and longer prediction durations. The DNN models showed significantly better accuracy and speed compared to human evaluators (p < 0.0005, p < 0.01). These DNN models highlight promising utility in trauma diagnosis due to their high accuracy and speed. Integrating such systems into clinical practices may enhance the diagnostic efficiency of PXRs.

To develop an early budget impact analysis of and inform future research on the national adoption of a commercially available AI application to support clinicians reviewing radiographs for suspected fractures across NHS emergency departments in England. A decision tree framework was coded to assess a change in outcomes for suspected fractures in adults when AI fracture detection was integrated into clinical workflow over a 1-year time horizon. Standard of care was the comparator scenario and the ground truth reference cases were characterised by radiology report findings. The effect of AI on assisting ED clinicians when detecting fractures was sourced from US literature. Data on resource use conditioned on the correct identification of a fracture in the ED was extracted from a London NHS trust. Sensitivity analysis was conducted to account for the influence of parameter uncertainty on results. In one year, an estimated 658,564 radiographs were performed in emergency departments across England for suspected wrist, ankle or hip fractures. The number of patients returning to the ED with a missed fracture was reduced by 21,674 cases and a reduction of 20, 916 unnecessary referrals to fracture clinics. The cost of current practice was estimated at £66,646,542 and £63,012,150 with the integration of AI. Overall, generating a return on investment of £3,634,392 to the NHS. The adoption of AI in EDs across England has the potential to generate cost savings. However, additional evidence on radiograph review accuracy and subsequent resource use is required to further demonstrate this.

Artificial intelligence (AI) has been proposed to assist radiologists in reporting multiparametric magnetic resonance imaging (mpMRI) of the prostate. We evaluate the diagnostic performance of radiologists with different levels of experience when reporting mpMRI with the support of available AI-based software (Quantib Prostate). This is a single-center study (NCT06298305) involving 110 patients. Those with a positive mpMRI (PI-RADS ≥ 3) underwent targeted plus systematic biopsy (TBx plus SBx), while those with a negative mpMRI but a high clinical suspicion of prostate cancer (PCa) underwent SBx. Three readers with different levels of experience, identified as R1, R2, and R3 reviewed all mpMRI. Inter-reader agreement among the three readers with or without the assistance of Quantib Prostate as well as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for the detection of clinically significant PCa (csPCa) were assessed. 102 patients underwent prostate biopsy and the csPCa detection rate was 47%. Using Quantib Prostate resulted in an increased number of lesions identified for R3 (101 vs. 127). Inter-reader agreement slightly increased when using Quantib Prostate from 0.37 to 0.41 without vs. with Quantib Prostate, respectively. PPV, NPV and diagnostic accuracy (measured by the area under the curve [AUC]) of R3 improved (0.51 vs. 0.55, 0.65 vs.0.82 and 0.56 vs. 0.62, respectively). Conversely, no changes were observed for R1 and R2. Using Quantib Prostate did not enhance the detection rate of csPCa for readers with some experience in prostate imaging. However, for an inexperienced reader, this AI-based software is demonstrated to improve the performance. Name of registry: clinicaltrials.gov. NCT06298305. Date of registration: 2022-09.