To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion. The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations. The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results. The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.

High intra-pancreatic fat deposition (IPFD) plays an important role in diseases of the pancreas. The intricate anatomy of the pancreas and the surrounding structures has historically made IPFD quantification a challenging measurement to make accurately on radiological images. To take on the challenge, automated IPFD quantification methods using artificial intelligence (AI) have recently been deployed. The aim was to benchmark the current knowledge on the use of AI-based models to measure IPFD automatedly. The search was conducted in the MEDLINE, Embase, Scopus, and IEEE Xplore databases. Studies were eligible if they used AI for both segmentation of the pancreas and quantification of IPFD. The ground truth was manual segmentation by radiologists. When possible, data were pooled statistically using a random-effects model. A total of 12 studies (10 cross-sectional and 2 longitudinal) encompassing more than 50 thousand people were included. Eight of the 12 studies used MRI, whereas four studies employed CT. U-Net model and nnU-Net model were the most frequently used AI-based models. The pooled Dice similarity coefficient of AI-based models in quantifying IPFD was 82.3% (95% confidence interval, 73.5 to 91.1%). The clinical application of AI-based models showed the relevance of high IPFD to acute pancreatitis, pancreatic cancer, and type 2 diabetes mellitus. Current AI-based models for IPFD quantification are suboptimal, as the dissimilarity between AI-based and manual quantification of IPFD is not negligible. Future advancements in fully automated measurements of IPFD will accelerate the accumulation of robust, large-scale evidence on the role of high IPFD in pancreatic diseases. KEY POINTS: Question What is the current evidence on the performance and clinical applicability of artificial intelligence-based models for automated quantification of intra-pancreatic fat deposition? Findings The nnU-Net model achieved the highest Dice similarity coefficient among MRI-based studies, whereas the nnTransfer model demonstrated the highest Dice similarity coefficient in CT-based studies. Clinical relevance Standardisation of reporting on artificial intelligence-based models for the quantification of intra-pancreatic fat deposition will be essential to enhancing the clinical applicability and reliability of artificial intelligence in imaging patients with diseases of the pancreas.

To develop and validate a deep learning model based on arterial phase-enhanced CT for predicting the pathological grading of clear cell renal cell carcinoma (ccRCC). Data from 564 patients diagnosed with ccRCC from five distinct hospitals were retrospectively analyzed. Patients from centers 1 and 2 were randomly divided into a training set (n=283) and an internal test set (n=122). Patients from centers 3, 4, and 5 served as external validation sets 1 (n=60), 2 (n=38), and 3 (n=61), respectively. A 2D model, a 2.5D model (three-slice input), and a radiomics-based multi-layer perceptron (MLP) model were developed. Model performance was evaluated using the area under the curve (AUC), accuracy, and sensitivity. The 2.5D model outperformed the 2D and MLP models. Its AUCs were 0.959 (95% CI: 0.9438-0.9738) for the training set, 0.879 (95% CI: 0.8401-0.9180) for the internal test set, and 0.870 (95% CI: 0.8076-0.9334), 0.862 (95% CI: 0.7581-0.9658), and 0.849 (95% CI: 0.7766-0.9216) for the three external validation sets, respectively. The corresponding accuracy values were 0.895, 0.836, 0.827, 0.825, and 0.839. Compared to the MLP model, the 2.5D model achieved significantly higher AUCs (increases of 0.150 [p<0.05], 0.112 [p<0.05], and 0.088 [p<0.05]) and accuracies (increases of 0.077 [p<0.05], 0.075 [p<0.05], and 0.101 [p<0.05]) in the external validation sets. The 2.5D model based on 2.5D CT image input demonstrated improved predictive performance for the WHO/ISUP grading of ccRCC.

Image-to-image (I2I) translation networks have emerged as promising tools for generating synthetic medical images; however, their clinical reliability and ability to preserve diagnostically relevant features remain underexplored. This study evaluates the performance of state-of-the-art 2D/3D I2I networks for converting ultrasound (US) images to synthetic MRI in prostate cancer (PCa) imaging. The novelty lies in combining radiomics, expert clinical evaluation, and classification performance to comprehensively benchmark these models for potential integration into real-world diagnostic workflows. A dataset of 794 PCa patients was analyzed using ten leading I2I networks to synthesize MRI from US input. Radiomics feature (RF) analysis was performed using Spearman correlation to assess whether high-performing networks (SSIM > 0.85) preserved quantitative imaging biomarkers. A qualitative evaluation by seven experienced physicians assessed the anatomical realism, presence of artifacts, and diagnostic interpretability of synthetic images. Additionally, classification tasks using synthetic images were conducted using two machine learning and one deep learning model to assess the practical diagnostic benefit. Among all networks, 2D-Pix2Pix achieved the highest SSIM (0.855 ± 0.032). RF analysis showed that 76 out of 186 features were preserved post-translation, while the remainder were degraded or lost. Qualitative feedback revealed consistent issues with low-level feature preservation and artifact generation, particularly in lesion-rich regions. These evaluations were conducted to assess whether synthetic MRI retained clinically relevant patterns, supported expert interpretation, and improved diagnostic accuracy. Importantly, classification performance using synthetic MRI significantly exceeded that of US-based input, achieving average accuracy and AUC of ~ 0.93 ± 0.05. Although 2D-Pix2Pix showed the best overall performance in similarity and partial RF preservation, improvements are still required in lesion-level fidelity and artifact suppression. The combination of radiomics, qualitative, and classification analyses offered a holistic view of the current strengths and limitations of I2I models, supporting their potential in clinical applications pending further refinement and validation.

Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, with significant variability in patient outcomes even within the same stage according to the Barcelona Clinic Liver Cancer staging system. Accurately predicting patient prognosis and potential treatment response prior to therapy initiation is crucial for personalized clinical decision-making. This review focuses on the application of artificial intelligence (AI) in magnetic resonance imaging for guiding individualized treatment strategies in HCC management. Specifically, we emphasize AI-based tools for pre-treatment prediction of therapeutic response and prognosis. AI techniques such as radiomics and deep learning have shown strong potential in extracting high-dimensional imaging features to characterize tumors and liver parenchyma, predict treatment outcomes, and support prognostic stratification. These advances contribute to more individualized and precise treatment planning. However, challenges remain in model generalizability, interpretability, and clinical integration, highlighting the need for standardized imaging datasets and multi-omics fusion to fully realize the potential of AI in personalized HCC care. Evidence level: 5. Technical efficacy: 4.

Artificial intelligence (AI) is already having a significant impact on healthcare. For example, AI-guided imaging can improve the diagnosis/treatment of vascular diseases, which affect over 200 million people globally. Recently, Chiu and colleagues (2024) developed an AI algorithm that supports nurses with no ultrasound training in diagnosing abdominal aortic aneurysms (AAA) with similar accuracy as ultrasound-trained physicians. This technology can therefore improve AAA screening; however, achieving clinical impact with new AI technologies requires careful consideration of commercialization strategies, including funding, compliance with safety and regulatory frameworks, health technology assessment, regulatory approval, reimbursement, and clinical guideline integration.

To evaluate image quality and diagnostic performance of prostate biparametric MRI (bi-MRI) with deep learning reconstruction (DLR). This prospective study included 61 adult male urological patients undergoing prostate MRI with standard-of-care (SOC) and fast protocols. Sequences included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. DLR images were generated from FAST datasets. Three groups (SOC, FAST, DLR) were compared using: (1) five-point Likert scale, (2) signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), (3) lesion slope profiles, (4) dorsal capsule edge rise distance (ERD). PI-RADS scores were assigned to dominant lesions. ADC values were measured in histopathologically confirmed cases. Diagnostic performance was analyzed via receiver operating characteristic (ROC) curves (accuracy/sensitivity/specificity). Statistical tests included Friedman test, one-way ANOVA with post hoc analyses, and DeLong test for ROC comparisons (P<0.05). FAST scanning protocols reduced acquisition time by nearly half compared to the SOC scanning protocol. When compared to T2WI_FAST, DLR significantly improved SNR, CNR, slope profile, and ERD (P < 0.05). Similarly, DLR significantly enhanced SNR, CNR, and image sharpness when compared to DWI_FAST (P < 0.05). No significant differences were observed in PI-RADS scores and ADC values between groups (P > 0.05). The areas under the ROC curves, sensitivity, and specificity of ADC values for distinguishing benign and malignant lesions remained consistent (P > 0.05). DLR enhances image quality in fast prostate bi-MRI while preserving PI-RADS classification accuracy and ADC diagnostic performance.

Deep learning (DL) reconstructions have shown utility for improving image quality of abdominal MRI in adult patients, but a paucity of literature exists in children. To compare image quality between three-dimensional fast spoiled gradient echo (SPGR) abdominal MRI acquisitions reconstructed conventionally and using a prototype method based on a commercial DL algorithm in a pediatric cohort. Pediatric patients (age < 18 years) who underwent abdominal MRI from 10/2023-3/2024 including gadolinium-enhanced accelerated 3D SPGR 2-point Dixon acquisitions (LAVA-Flex, GE HealthCare) were identified. Images were retrospectively generated using a prototype reconstruction method leveraging a commercial deep learning algorithm (AIR™ Recon DL, GE HealthCare) with the 75% noise reduction setting. For each case/reconstruction, three radiologists independently scored DL and non-DL image quality (overall and of selected structures) on a 5-point Likert scale (1-nondiagnostic, 5-excellent) and indicated reconstruction preference. The signal-to-noise ratio (SNR) and mean number of edges (inverse correlate of image sharpness) were also quantified. Image quality metrics and preferences were compared using Wilcoxon signed-rank, Fisher exact, and paired t-tests. Interobserver agreement was evaluated with the Kendall rank correlation coefficient (W). The final cohort consisted of 38 patients with mean ± standard deviation age of 8.6 ± 5.7 years, 23 males. Mean image quality scores for evaluated structures ranged from 3.8 ± 1.1 to 4.6 ± 0.6 in the DL group, compared to 3.1 ± 1.1 to 3.9 ± 0.6 in the non-DL group (all P < 0.001). All radiologists preferred DL in most cases (32-37/38, P < 0.001). There were a 2.3-fold increase in SNR and a 3.9% reduction in the mean number of edges in DL compared to non-DL images (both P < 0.001). In all scored anatomic structures except the spine and non-DL adrenals, interobserver agreement was moderate to substantial (W = 0.41-0.74, all P < 0.01). In a broad spectrum of pediatric patients undergoing contrast-enhanced Dixon abdominal MRI acquisitions, the prototype deep learning reconstruction is generally preferred to conventional methods with improved image quality across a wide range of structures.

Detecting small pancreatic ductal adenocarcinomas (PDAC) is challenging owing to their difficulty in being identified as distinct tumor masses. This study assesses the diagnostic performance of a three-dimensional convolutional neural network for the automatic detection of small PDAC using both automatic tumor mass detection and indirect indicator evaluation. High-resolution contrast-enhanced computed tomography (CT) scans from 181 patients diagnosed with PDAC (diameter ≤ 2 cm) between January 2018 and December 2023 were analyzed. The D/P ratio, which is the cross-sectional area of the MPD to that of the pancreatic parenchyma, was identified as an indirect indicator. A total of 204 patient data sets including 104 normal controls were analyzed for automatic tumor mass detection and D/P ratio evaluation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated to detect tumor mass. The sensitivity of PDAC detection was compared with that of the software and radiologists, and tumor localization accuracy was validated against endoscopic ultrasonography (EUS) findings. The sensitivity, specificity, PPV, and NPV for tumor mass detection were 77.0%, 76.0%, 75.5%, and 77.5%, respectively; for D/P ratio detection, 87.0%, 94.2%, 93.5%, and 88.3%, respectively; and for combined tumor mass and D/P ratio detections, 96.0%, 70.2%, 75.6%, and 94.8%, respectively. No significant difference was observed between the software's sensitivity and that of the radiologist's report (software, 96.0%; radiologist, 96.0%; p = 1). The concordance rate between software findings and EUS was 96.0%. Combining indirect indicator evaluation with tumor mass detection may improve small PDAC detection accuracy.

We conducted a systematic review and meta-analysis in diagnostic performance of studies that tried to use artificial intelligence (AI) algorithms in detecting pancreatic ductal adenocarcinoma (PDAC) and distinguishing them from other types of pancreatic lesions. We systematically searched for studies on pancreatic lesions and AI from January 2014 to May 2024. Data were extracted and a meta-analysis was performed using contingency tables and a random-effects model to calculate pooled sensitivity and specificity. Quality assessment was done using modified TRIPOD and PROBAST tools. We included 26 studies in this systematic review, with 22 studies chosen for meta-analysis. The evaluation of AI algorithms' performance in internal validation exhibited a pooled sensitivity of 93% (95% confidence interval [CI], 90 to 95) and specificity of 95% (95% CI, 92 to 97). Additionally, externally validated AI algorithms demonstrated a combined sensitivity of 89% (95% CI, 85 to 92) and specificity of 91% (95% CI, 85 to 95). Subgroup analysis indicated that diagnostic performance differed by comparator group, image contrast, segmentation technique, and algorithm type, with contrast-enhanced imaging and specific AI models (e.g., random forest for sensitivity and CNN for specificity) demonstrating superior accuracy. Although the potential biases should be further addressed, results of this systematic review and meta-analysis showed that AI models have the potential to be incorporated in clinical settings for the detection of smaller tumors and underpinning early signs of PDAC.