<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To assess the effectiveness of an explainable deep learning (DL) model, developed using multiparametric MRI (mpMRI) features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs ≥ 1 cm in diameter at mpMRI were included in the study. nn-Unet and Liver Imaging Feature Transformer (LIFT) models were developed using retrospective data from one hospital (January 2018-August 2023). nnU-Net was used for lesion segmentation and LIFT for FLL classification. External testing was performed on data from three hospitals (January 2018-December 2023), with a prospective test set obtained from January 2024 to April 2024. Model performance was compared with radiologists and impact of model assistance on junior and senior radiologist performance was assessed. Evaluation metrics included the Dice similarity coefficient (DSC) and accuracy. Results A total of 2131 individuals with FLLs (mean age, 56 ± [SD] 12 years; 1476 female) were included in the training, internal test, external test, and prospective test sets. Average DSC values for liver and tumor segmentation across the three test sets were 0.98 and 0.96, respectively. Average accuracy for features and lesion classification across the three test sets were 93% and 97%, respectively. LIFT-assisted readings improved diagnostic accuracy (average 5.3% increase, <i>P</i> < .001), reduced reading time (average 34.5 seconds decrease, <i>P</i> < .001), and enhanced confidence (average 0.3-point increase, <i>P</i> < .001) of junior radiologists. Conclusion The proposed DL model accurately detected and classified FLLs, improving diagnostic accuracy and efficiency of junior radiologists. ©RSNA, 2025.

In lung CT imaging, motion artifacts caused by cardiac motion and respiration are common. Recently, CLEAR Motion, a deep learning-based reconstruction method that applies motion correction technology, has been developed. This study aims to quantitatively evaluate the clinical usefulness of CLEAR Motion. A total of 129 lung CT was analyzed, and heart rate, height, weight, and BMI of all patients were obtained from medical records. Images with and without CLEAR Motion were reconstructed, and quantitative evaluation was performed using variance of Laplacian (VL) and PSNR. The difference in VL (DVL) between the two reconstruction methods was used to evaluate which part of the lung field (upper, middle, or lower) CLEAR Motion is effective. To evaluate the effect of motion correction based on patient characteristics, the correlation between body mass index (BMI), heart rate and DVL was determined. Visual assessment of motion artifacts was performed using paired comparisons by 9 radiological technologists. With the exception of one case, VL was higher in CLEAR Motion. Almost all the cases (110 cases) showed large DVL in the lower part. BMI showed a positive correlation with DVL (r = 0.55, p < 0.05), while no differences in DVL were observed based on heart rate. The average PSNR was 35.8 ± 0.92 dB. Visual assessments indicated that CLEAR Motion was preferred in most cases, with an average preference score of 0.96 (p < 0.05). Using Clear Motion allows for obtaining images with fewer motion artifacts in lung CT.

Stroke, frequently associated with carotid artery disease, is evaluated using carotid computed tomography angiography (CTA). Dual-energy CTA (DE-CTA) enhances imaging quality but presents challenges in maintaining high image clarity with low-dose scans. To compare the image quality of 50 keV virtual monoenergetic images (VMI) generated using Deep Learning Image Reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) algorithms under a triple-low scanning protocol in carotid CTA. A prospective study was conducted with 120 patients undergoing DE-CTA. The control group (Group 1), with a noise index (NI) of 4.0 and a contrast agent dose of 0.5 mL/kg, used the ASIR-V algorithm. The experimental group was divided into four subgroups: Group 2 (ASIR-V 50%), Group 3 (DLIR-L), Group 4 (DLIR-M), and Group 5 (DLIR-H), with a higher NI of 13.0 and a reduced contrast agent dose of 0.4 mL/kg. Objective image quality was assessed through signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and standard deviation (SD), while subjective quality was evaluated using a 5-point Likert scale. Radiation dose and contrast agent volume were also measured. The triple-low scanning protocol reduced radiation exposure by 53.2%, contrast agent volume by 19.7%, and injection rate by 19.8%. The DLIR-H setting outperformed ASIR-V, demonstrating superior image quality, better noise suppression, and improved contrast in small vessels. VMI at 50 keV showed enhanced diagnostic clarity with minimal radiation and contrast agent usage. The DLIR algorithm, particularly at high settings, significantly enhances image quality in DE-CTA VMI under a triple-low scanning protocol, offering a better balance between radiation dose reduction and image clarity.

Point-of-care ultrasonography has become a valuable tool for assessing diaphragmatic function in critically ill patients receiving invasive mechanical ventilation. However, conventional diaphragm ultrasound assessment remains highly operator-dependent and subjective. Previous research introduced automatic measurement of diaphragmatic excursion and velocity using 2D speckle-tracking technology. This study aimed to develop an artificial intelligence-multimodal learning framework to improve the prediction of weaning failure and guide individualized weaning strategies. This prospective study enrolled critically ill patients older than 18 years who received mechanical ventilation for more than 48 hours and were eligible for a spontaneous breathing trial in 2 intensive care units in Guangzhou, China. Before the spontaneous breathing trial, diaphragm ultrasound videos were collected using a standardized protocol, and automatic measurements of excursion and velocity were obtained. A total of 88 patients were included, with 50 successfully weaned and 38 experiencing weaning failure. Each patient record included 27 clinical and 6 diaphragmatic indicators, selected based on previous literature and phenotyping studies. Clinical variables were preprocessed using OneHotEncoder, normalization, and scaling. Ultrasound videos were interpolated to a uniform resolution of 224×224×96. Artificial intelligence-multimodal learning based on clinical characteristics, laboratory parameters, and diaphragm ultrasonic videos was established. Four experiments were conducted in an ablation setting to evaluate model performance using different combinations of input data: (1) diaphragmatic excursion only, (2) clinical and diaphragmatic indicators, (3) ultrasound videos only, and (4) all modalities combined (multimodal). Metrics for evaluation included classification accuracy, area under the receiver operating characteristic curve (AUC), average precision in the precision-recall curve, and calibration curve. Variable importance was assessed using SHAP (Shapley Additive Explanation) to interpret feature contributions and understand model predictions. The multimodal co-learning model outperformed all single-modal approaches. The accuracy improved when predicted through diaphragm ultrasound video data using Video Vision Transformer (accuracy=0.8095, AUC=0.852), clinical or ultrasound indicators (accuracy=0.7381, AUC=0.746), and the multimodal co-learning (accuracy=0.8331, AUC=0.894). The proposed co-learning model achieved the highest score (average precision=0.91) among the 4 experiments. Furthermore, calibration curve analysis demonstrated that the proposed colearning model was well calibrated, as the curve was closest to the perfectly calibrated line. Combining ultrasound and clinical data for colearning improved the accuracy of the weaning outcome prediction. Multimodal learning based on automatic measurement of point-of-care ultrasonography and automated collection of objective clinical indicators greatly enhanced the practical operability and user-friendliness of the system. The proposed model offered promising potential for widespread clinical application in intensive care settings.

This retrospective-prospective study evaluated whether a deep learning-based MRI reconstruction algorithm can preserve diagnostic quality in brain MRI scans accelerated up to fourfold, using both public and prospective clinical data. The study included 18 healthy volunteers (scans acquired at 3T, January 2024-March 2025), as well as selected fastMRI public datasets with diverse pathologies. Phase-encoding-undersampled 2D/3D T1, T2, and FLAIR sequences were reconstructed with DeepFoqus-Accelerate and compared with standard-of-care (SOC). Three board-certified neuroradiologists and two MRI technologists independently reviewed 36 paired SOC/AI reconstructions from both datasets using a 5-point Likert scale, while quantitative similarity was assessed for 408 scans and 1224 datasets using Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Haar wavelet-based Perceptual Similarity Index (HaarPSI). No AI-reconstructed scan scored below 3 (minimally acceptable), and 95% scored $\geq 4$. Mean SSIM was 0.95 $\pm$ 0.03 (90% cases >0.90), PSNR >41.0 dB, and HaarPSI >0.94. Inter-rater agreement was slight to moderate. Rare artifacts did not affect diagnostic interpretation. These findings demonstrate that DeepFoqus-Accelerate enables robust fourfold brain MRI acceleration with 75% reduced scan time, while preserving diagnostic image quality and supporting improved workflow efficiency.

Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unresectable or metastatic uveal melanoma (mUM). Notably, a survival benefit has been observed even in the absence of radiographic response. This study aims to investigate the feasibility and prognostic value of artificial intelligence (AI)-assisted quantification and metabolic response assessment of [¹⁸F]FDG long axial field-of-view (LAFOV) PET/CT in mUM patients undergoing tebentafusp therapy. Fifteen patients with mUM treated with tebentafusp underwent [¹⁸F]FDG LAFOV PET/CT at baseline and 3 months post-treatment. Total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) were quantified using a deep learning-based segmentation tool On the RECOMIA platform. Metabolic response was assessed according to AI-assisted PERCIST 1.0 criteria. Associations between PET-derived parameters and overall survival (OS) were evaluated using Kaplan-Meier survival analysis. The median follow up (95% CI) was 14.1 months (12.9 months - not available). Automated TMTV and TLG measurements were successfully obtained in all patients. Elevated baseline TMTV and TLG were significantly associated with shorter OS (TMTV: 16.9 vs. 27.2 months; TLG: 16.9 vs. 27.2 months; p < 0.05). Similarly, higher TMTV and TLG at 3 months post-treatment predicted poorer survival outcomes (TMTV: 14.3 vs. 24.5 months; TLG: 14.3 vs. 24.5 months; p < 0.05). AI-assisted PERCIST response evaluation identified six patients with disease control (complete metabolic response, partial metabolic response, stable metabolic disease) and nine with progressive metabolic disease. A trend toward improved OS was observed in patients with disease control (24.5 vs. 14.6 months, p = 0.08). Circulating tumor DNA (ctDNA) levels based on GNAQ and GNA11 mutations were available in 8 patients; after 3 months Of tebentafusp treatment, 5 showed reduced Or stable ctDNA levels, and 3 showed an increase (median OS: 24.5 vs. 3.3 months; p = 0.13). Patients with increasing ctDNA levels exhibited significantly higher TMTV and TLG on follow-up imaging. AI-assisted whole-body quantification of [1⁸F]FDG PET/CT and PERCIST-based response assessment are feasible and hold prognostic significance in tebentafusp-treated mUM. TMTV and TLG may serve as non-invasive imaging biomarkers for risk stratification and treatment monitoring in this malignancy.

Carotid artery disease is a major cause of stroke and is frequently evaluated using Carotid CT Angiography (CTA). However, the associated radiation exposure and contrast agent use raise concerns, particularly for high-risk patients. Recent advances in Deep Learning Image Reconstruction (DLIR) offer new potential to enhance image quality under low-dose conditions. This study aimed to evaluate the effectiveness of the DLIR-H algorithm in improving image quality of 40 keV Virtual Monoenergetic Images (VMI) in dual-energy CTA (DE-CTA) while minimizing radiation dose and contrast agent usage. A total of 120 patients undergoing DE-CTA were prospectively divided into four groups: one control group using ASIR-V and three experimental groups using DLIR-L, DLIR-M, and DLIR-H algorithms. All scans employed a "triple-low" protocol-low radiation, low contrast volume, and low injection rate. Objective image quality was assessed via CT values, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Subjective image quality was evaluated using a 5-point Likert scale. The DLIR-H group showed the greatest improvements in image quality, with significantly reduced noise and increased SNR and CNR, particularly at complex vascular sites such as the carotid bifurcation and internal carotid artery. Radiation dose and contrast volume were reduced by 15.6 % and 17.5 %, respectively. DLIR-H also received the highest subjective image quality scores. DLIR-H significantly enhances DE-CTA image quality under ultra-low-dose conditions, preserving diagnostic detail while reducing patient risk. DLIR-H supports safer and more effective carotid imaging, especially for high-risk groups like renal-impaired patients and those needing repeated scans, enabling wider clinical use of ultra-low-dose protocols.

Opportunistic computed tomography (CT) screening for the evaluation of sarcopenia and myosteatosis has been gaining emphasis. A fully automated artificial intelligence (AI)-integrated system for body composition assessment on CT scans is a prerequisite for effective opportunistic screening. However, no study has evaluated the implementation of fully automated AI systems for opportunistic screening in real-world clinical practice for routine health check-ups. The aim of this study is to evaluate the performance and clinical utility of a fully automated AI-integrated system for body composition assessment on opportunistic CT during routine health check-ups. This prospective multicenter study included 537 patients who underwent routine health check-ups across 3 institutions. Our AI algorithm models are composed of selecting L3 slice and segmenting muscle and fat area in an end-to-end manner. The AI models were integrated into the Picture Archiving and Communication System (PACS) at each institution. Technical success rate, processing time, and segmentation accuracy in Dice similarity coefficient were assessed. Body composition metrics were analyzed across age and sex groups. The fully automated AI-integrated system successfully retrieved anonymized CT images from the PACS, performed L3 selection and segmentation, and provided body composition metrics, including muscle quality maps and muscle age. The technical success rate was 100% without any failed cases requiring manual adjustment. The mean processing time from CT acquisition to report generation was 4.12 seconds. Segmentation accuracy comparing AI results and human expert results was 97.4%. Significant age-related declines in skeletal muscle area and normal-attenuation muscle area were observed, alongside increases in low-attenuation muscle area and intramuscular adipose tissue. Implementation of the fully automated AI-integrated system significantly enhanced opportunistic sarcopenia screening, achieving excellent technical success and high segmentation accuracy without manual intervention. This system has the potential to transform routine health check-ups by providing rapid and accurate assessments of body composition.

<b><i>Background:</i></b> Oncologic emergencies in critically ill cancer patients frequently require rapid, real-time assessment of tumor responses to therapeutic interventions. However, conventional imaging modalities such as computed tomography and magnetic resonance imaging are often impractical in intensive care units (ICUs) due to logistical constraints and patient instability. Super-resolution ultrasound (SR-US) imaging has emerged as a promising noninvasive alternative, facilitating bedside evaluation of tumor microvascular dynamics with exceptional spatial resolution. This study assessed the clinical utility of real-time SR-US imaging in monitoring tumor perfusion changes during emergency management in oncological ICU settings. <b><i>Methods:</i></b> In this prospective observational study, critically ill patients with oncologic emergencies underwent bedside SR-US imaging before and after the initiation of emergency therapy (e.g., corticosteroids, decompression, or chemotherapy). SR-US was employed to quantify microvascular parameters, including perfusion density and flow heterogeneity. Data processing incorporated artificial intelligence for real-time vessel segmentation and quantitative analysis. <b><i>Results:</i></b> SR-US imaging successfully detected perfusion changes within hours of therapy initiation. A significant correlation was observed between reduced tumor perfusion and clinical improvement, including symptom relief and shorter ICU stay. This technology enables visualization of microvessels as small as 30 µm, surpassing conventional ultrasound limits. No adverse events were reported with the use of contrast microbubbles. In addition, SR-US imaging reduces the need for transportation to radiology departments, thereby optimizing ICU workflow. <b><i>Conclusions:</i></b> Real-time SR-US imaging offers a novel, bedside-compatible method for evaluating tumor vascular response during the acute phase of oncological emergencies. Its integration into ICU care pathways could enhance timely decision-making, reduce reliance on static imaging, and support personalized cancer management. Further multicenter validation is required.

Threatened miscarriage (TM), defined as first-trimester vaginal bleeding with a closed cervix and detectable fetal cardiac activity, affects up to 30 % of clinically recognised pregnancies and is linked to increased risk of adverse outcomes. This study evaluates the predictive value of first-trimester ultrasound (US) and biochemical (BC) markers in determining outcomes among women with TM symptoms. This prospective cohort study recruited 118 women with viable singleton pregnancies (5⁺⁰ to 12⁺⁶ weeks' gestation) from Malta's national public hospital between January 2023 and June 2024. Participants underwent US and BC assessment, along with collection of clinical and sociodemographic data. Pregnancy outcomes were followed to term and classified as live birth or loss. Univariate logistic regression identified individual predictors. Multivariate logistic regression (MLR) and random forest (RF) modelling assessed combined predictive performance. Among 118 TM cases, 77 % resulted in live birth, 23 % in loss. MLR identified progesterone, cervical length, mean gestational sac diameter (MGSD), trophoblast thickness, sFlt-1:PlGF ratio, and maternal age as significant predictors. Higher progesterone, cervical length, MGSD, and sFlt-1:PlGF ratio reduced risk, while maternal age over 35 increased it. MLR achieved 82.7 % accuracy (AUC = 0.89). RF improved accuracy to 93.1 % (AUC = 0.97), confirming the combined predictive value of US and BC markers. US and BC markers hold predictive value in TM. Machine learning, particularly RF, may improve early clinical risk stratification. This tool may support timely decision-making and personalised monitoring, intervention, and counselling for women with TM.