Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, and echocardiography is critical for diagnosis of both common and congenital cardiac conditions. However, echocardiographic data for certain pathologies are scarce, hindering the development of robust automated diagnosis models. In this work, we propose Echo-Path, a novel generative framework to produce echocardiogram videos conditioned on specific cardiac pathologies. Echo-Path can synthesize realistic ultrasound video sequences that exhibit targeted abnormalities, focusing here on atrial septal defect (ASD) and pulmonary arterial hypertension (PAH). Our approach introduces a pathology-conditioning mechanism into a state-of-the-art echo video generator, allowing the model to learn and control disease-specific structural and motion patterns in the heart. Quantitative evaluation demonstrates that the synthetic videos achieve low distribution distances, indicating high visual fidelity. Clinically, the generated echoes exhibit plausible pathology markers. Furthermore, classifiers trained on our synthetic data generalize well to real data and, when used to augment real training sets, it improves downstream diagnosis of ASD and PAH by 7\% and 8\% respectively. Code, weights and dataset are available here https://github.com/Marshall-mk/EchoPathv1

Deep learning techniques have gained considerable attention for their ability to accelerate MRI data acquisition while maintaining scan quality. In this work, we present a convolutional neural network (CNN) based framework for learning undersampling patterns directly from multi-coil MRI data. Unlike prior approaches that rely on in-training mask optimization, our method is trained with precomputed scan-adaptive optimized masks as supervised labels, enabling efficient and robust scan-specific sampling. The training procedure alternates between optimizing a reconstructor and a data-driven sampling network, which generates scan-specific sampling patterns from observed low-frequency $k$-space data. Experiments on the fastMRI multi-coil knee dataset demonstrate significant improvements in sampling efficiency and image reconstruction quality, providing a robust framework for enhancing MRI acquisition through deep learning.

Lung cancer is a leading cause of cancer-related mortality worldwide, necessitating the development of accurate and efficient diagnostic methods. Early detection and accurate characterization of pulmonary nodules significantly influence patient prognosis and treatment planning and can improve the five-year survival rate. However, distinguishing benign from malignant nodules using conventional imaging techniques remain a clinical challenge due to subtle structural similarities. Therefore, to address this issue, this study proposes a novel two-pathway wavelet-based deep learning computer-aided diagnosis (CADx) framework forimproved lung nodule classification using high-resolution computed tomography (HRCT) images. The proposed Wavelet-based Lung Cancer Detection Network (WaveLCDNet) is capable of characterizing lung nodules images through a hierarchical feature extraction pipeline consisting of convolutional neural network (CNN) blocks and trainable wavelet blocks for multi-resolution analysis. The introduced wavelet block can capture both spatial and frequency-domain information, preserving fine-grained texture details essential for nodule characterization. Additionally, in this work, convolutional block attention module (CBAM) based attention mechanism has been introduced to enhance discriminative feature learning. The extracted features from both pathways are adaptively fused and processed using global average pooling (GAP) operation. The introduced WaveLCDNet is trained and evaluated on the publicly accessible LIDC-IDRI dataset and achieved sensitivity, specificity, accuracy of 96.89%, 95.52%, and 96.70% for nodule characterization. In addition, the developed framework was externally validated on the Kaggle DSB2017 test dataset, achieving 95.90% accuracy with a Brier Score of 0.0215 for lung nodule characterization, reinforcing its reliability across independent imaging sources and its practical value for integration into real-world diagnostic workflows. By effectively combining multi-scale convolutional filtering with wavelet-based multi-resolution analysisand attention mechanisms, the introduced framework outperforms different recent most state-of-the-art deep learning models and offers a promising CADx solution forenhancing lung cancer screening early diagnosis in clinical settings.

<b>Background/Objectives</b>: The limited availability of labeled data, particularly in the medical domain, poses a significant challenge for training accurate diagnostic models. While deep learning techniques have demonstrated notable efficacy in image-based tasks, they require large annotated datasets. In data-scarce scenarios-especially involving rare diseases-their performance deteriorates significantly. Meta-learning offers a promising alternative by enabling models to adapt quickly to new tasks using prior knowledge and only a few labeled examples. This study aims to evaluate the effectiveness of representative meta-learning models for thoracic disease classification in chest X-rays. <b>Methods</b>: We conduct a comparative evaluation of four meta-learning models: Prototypical Networks, Relation Networks, MAML, and FoMAML. First, we assess five backbone architectures (ConvNeXt, DenseNet-121, ResNet-50, MobileNetV2, and ViT) using a Prototypical Network. The best-performing backbone is then used across all meta-learning models for fair comparison. Experiments are performed on the ChestX-ray14 dataset under a 2-way setting with multiple <i>k</i>-shot configurations. <b>Results</b>: Prototypical Networks combined with DenseNet-121 achieved the best performance, with a recall of 68.1%, an F1-score of 67.4%, and a precision of 0.693 in the 2-way, 10-shot configuration. In a disease-specific analysis, Hernia obtains the best classification results. Furthermore, Prototypical and Relation Networks demonstrate significantly higher computational efficiency, requiring fewer FLOPs and shorter execution times than MAML and FoMAML. <b>Conclusions</b>: Prototype-based meta-learning, particularly with DenseNet-121, proves to be a robust and computationally efficient approach for few-shot chest X-ray disease classification. These findings highlight its potential for real-world clinical applications, especially in scenarios with limited annotated medical data.

Traumatic bone marrow lesions (BMLs) occur in ~80% of anterior cruciate ligament (ACL) injuries, typically in the lateral femoral condyle (LFC) and lateral tibial plateau (LTP). Associated with microfractures, vascular proliferation, inflammation, and bone density changes, BMLs may contribute to posttraumatic osteoarthritis. However, their relationship with knee pain is unclear. This study examined the prevalence, characteristics, and progression of BMLs after ACL injury, focusing on associations with pain, meniscal and ligament injuries, and fractures. Participants (N = 100, aged 14-55) with MRI-confirmed ACL tears were scanned within 6 weeks post-injury (mean = 30.0, SD = 9.6 days). BML volumes were quantified using a validated machine learning method, and pain assessed via the Knee Injury and Osteoarthritis Outcome Score (KOOS). Analyses included t-tests, Mann-Whitney U, chi-square, and Spearman correlations with false discovery rate correction. BMLs were present in 95% of participants, primarily in the LFC and LTP. Males had 33% greater volumes than females (p < 0.05), even after adjusting for BMI. Volumes were higher in cases with depression fractures (p = 0.022) and negatively associated with baseline KOOS Symptoms. At 1 year, 92.68% of lesions (based on lesion counts) resolved in Nonsurgical participants, with a 96.13% volume reduction (p < 0.001). KOOS outcomes were similar between groups, except for slightly better Pain scores in the Nonsurgical group. Baseline Pain and Sport scores predicted follow-up outcomes. BMLs are common post-ACL injury, vary by sex and fracture status, and modestly relate to early symptoms. Most resolve within a year, with limited long-term differences by surgical status.

There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.

Acquiring high-resolution Magnetic resonance (MR) images is challenging due to constraints such as hardware limitations and acquisition times. Super-resolution (SR) techniques offer a potential solution to enhance MR image quality without changing the magnetic resonance imaging (MRI) hardware. However, typical SR methods are designed for fixed upsampling scales and often produce over-smoothed images that lack fine textures and edge details. To address these issues, we propose a unified diffusion-based framework for arbitrary-scale in-plane MR image SR, dubbed Progressive Reconstruction and Denoising Diffusion Model (PRDDiff). Specifically, the forward diffusion process of PRDDiff gradually masks out high-frequency components and adds Gaussian noise to simulate the downsampling process in MRI. To reverse this process, we propose an Adaptive Resolution Restoration Network (ARRNet), which introduces a current step corresponding to the resolution of input MR image and an ending step corresponding to the target resolution. This design guide the ARRNet to recovering the clean MR image at the target resolution from input MR image. The SR process starts from an MR image at the initial resolution and gradually enhances them to higher resolution by progressively reconstructing high-frequency components and removing the noise based on the recovered MR image from ARRNet. Furthermore, we design a multi-stage SR strategy that incrementally enhances resolution through multiple sequential stages to further improve recovery accuracy. Each stage utilizes a set number of sampling steps from PRDDiff, guided by a specific ending step, to recover details pertinent to the predefined intermediate resolution. We conduct extensive experiments on fastMRI knee dataset, fastMRI brain dataset, our real-collected LR-HR brain dataset, and clinical pediatric cerebral palsy (CP) dataset, including T1-weighted and T2-weighted images for the brain and proton density-weighted images for the knee. The results demonstrate that PRDDiff outperforms previous MR image super-resolution methods in term of reconstruction accuracy, generalization, and downstream lesion segmentation accuracy and CP classification performance. The code is publicly available at https://github.com/Jiazhen-Wang/PRDDiff-main.

This study aimed to analyze the correlation between the ultrasonographic radiomic features of multiple regions within and surrounding the primary tumor in breast cancer patients prior to receiving neoadjuvant chemotherapy (NAC) and the efficacy of NAC. By integrating clinical and pathological parameters, a predictive model was constructed to provide an accurate basis for personalized treatment and precise prognosis in breast cancer patients. This retrospective study included 321 breast cancer patients who underwent NAC treatment at the Second Hospital of Lanzhou University from January 2019 to December 2024. According to post-operative pathological results, the patients were divided into pathological complete response (PCR) and non-pathological complete response (non-PCR) groups. Regions of interest were outlined on 2-D ultrasound images using Itk-snap software. The intra-tumor (Intra) region and 5 mm (Peri-5 mm), 10 mm (Peri-10 mm) and 15 mm (Peri-15 mm) the peri-tumoralregions were demarcated, with radiomics features extracted from each region. Patients were randomly divided into a training set (n = 224) and a validation set (n = 97) in a 7:3 ratio. All features underwent Z-score normalization followed by dimensionality reduction using t-tests, Pearson correlation coefficients and least absolute shrinkage and selection operator. Radiomics models for Intra, Peri-5 mm, Peri-10 mm, Peri-15 mm and the combined intra-tumoral and peri-tumoral regions (Intra-tumoral, Peri-tumoral, IntraPeri) were constructed using a random forest machine-learning classifier. The predictive performance of the models was assessed by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Additionally, calibration curves and decision curve analysis were plotted to evaluate the model's goodness of fit and clinical net benefit RESULTS: A total of 214 radiomics features were extracted from the intra-tumoral and multi-region peri-tumoral areas. Using the least absolute shrinkage and selection operator regression model, eight intra-tumoral radiomics features, eight peri-10 mm radiomics features and nine IntraPeri-10 mm radiomics features were selected as being closely associated with PCR. The AUC of the intra-tumoral model was 0.860 and 0.823 in the training and validation sets, respectively. The AUCs of the peri-5 mm, Peri-10 mm and Peri-15 mm models were 0.836, 0.854 and 0.822 in the training set, and 0.793, 0.799 and 0.792 in the validation set. Among them, the AUC of the IntraPeri-10 mm model in the validation set was 0.842 (95% confidence interval [CI]: 0.764-0.921), which was superior to the AUC of the IntraPeri-5 mm model (0.831; 95% CI: 0.758-0.914) and the IntraPeri-15 mm model (0.838; 95% CI: 0.761-0.917). The combined model based on IntraPeri-10 mm and clinical pathological parameters (HER-2, Ki-67) achieved an AUC of 0.869 (95% CI: 0.800-0.937). The Delong test showed that the AUC of the combined model was significantly superior to that of the other models. The calibration curve indicated that the combined model had a good fit, and decision curve analysis demonstrated that the combined model provided a better clinical net benefit. The peri-10 mm region is the optimal predictive area for the tumor's surrounding tissue after NAC in breast cancer. The IntraPeri-10 mm model, incorporating clinical pathological parameters, performs better at predicting the efficacy of NAC in breast cancer and can accurately assess treatment response, offering valuable guidance for subsequent treatment decisions.

We conducted a systematic review and meta-analysis to assess and compare the diagnostic performance of prostate-specific membrane antigen positron-emission tomography (PSMA PET) with conventional imaging modalities in detecting biochemical recurrence of prostate cancer, and to assess the role of artificial intelligence in this context. A comprehensive search of PubMed, Embase, Web of Science, the Cochrane Library, and Scopus was conducted for studies, initially on May 7, 2025, and updated on July 28, 2025. Studies that compared PSMA PET with conventional imaging and assessed artificial intelligence for detecting biochemical recurrence of prostate cancer were considered. The QUADAS-2 technique was employed to evaluate study quality. Diagnosis accuracy and detection rates were aggregated utilizing a bivariate random-effects model. A total of 7637 patients from 67 studies were included. PSMA PET demonstrated significantly higher overall diagnostic accuracy for biochemical recurrence of prostate cancer compared to mpMRI, CT, and AI test sets, with accuracy values of (0.89 vs. 0.71, 0.45, and 0.76, P<0.01). For local recurrence, mpMRI outperformed PSMA PET and CT (0.93 vs. 0.84 and 0.77, P<0.01). PSMA PET was superior in detecting lymph node metastasis than mpMRI and CT (0.89 vs. 0.79 and 0.72, P<0.05). For bone metastasis, PSMA PET outperformed mpMRI, CT, and Bone scan (0.95 vs. 0.85, 0.81, and 0.80, P<0.05). For visceral metastasis, PSMA PET outperformed mpMRI (0.96 vs. 0.89, P=0.23), and CT (0.96 vs. 0.78, P<0.05). 21 studies involving 3113 samples were included to evaluate the performance of artificial intelligence in detecting biochemical recurrence of prostate cancer. The pooled sensitivity, specificity, DOR, and AUC of AI test sets in detecting biochemical recurrence of prostate cancer were 0.77, 0.76, 10.39, and 0.79. Heterogeneity limits the generalizability of our findings. PSMA PET outperformed mpMRI and CT in detecting overall, local recurrence, bone, and visceral metastasis, while mpMRI was more effective for local recurrence. While AI exhibits potential diagnostic efficacy. Despite promising results, heterogeneity and limited validation highlight the need for further research to support routine clinical use.

Fatty liver disease, encompassing non-alcoholic fatty liver disease (NAFLD) and alcohol-related liver disease (ALD), affects ∼25% of adults globally. Magnetic resonance imaging (MRI), particularly proton density fat fraction (PDFF), is the non-invasive gold standard for hepatic steatosis quantification, but its clinical use is limited by cost, protocol variability, a analysis time. Machine learning (ML) and deep learning (DL) techniques, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), show promise in enhancing MRI-based quantification and staging. To systematically review the diagnostic accuracy, reproducibility, and clinical utility of ML and DL techniques applied to MRI for quantifying and staging hepatic steatosis in fatty liver disease. This systematic review was registered in PROSPERO (CRD420251121056) and adhered to PRISMA guidelines, searching PubMed, Cochrane Library, Scopus, IEEE Xplore, Web of Science, Google Scholar, and grey literature for studies on ML/DL applications in MRI for fatty liver disease. Eligible studies involved human participants with suspected/confirmed NAFLD, NASH, or ALD, using ML/DL (e.g., CNNs, GANs) on MRI data (e.g., PDFF, Dixon MRI). Outcomes included diagnostic accuracy (sensitivity, specificity, area under the curve (AUC)), reproducibility (intraclass correlation coefficient (ICC), Dice), and clinical utility (e.g., treatment planning). Two reviewers screened studies, extracted data, and assessed risk of bias using QUADAS-2. Narrative synthesis and meta-analysis (where feasible) were conducted. From 2550 records, 15 studies (n = 25-1038) were included, using CNNs, GANs, radiomics, and dictionary learning on PDFF, chemical shift-encoded MRI, or Dixon MRI. Diagnostic accuracy was high (AUC 0.85-0.97, r = 0.94-0.99 vs. biopsy/MRS), with reproducibility metrics robust (ICC 0.94-0.99, Dice 0.87-0.94). Efficiency improved significantly (e.g., processing <0.16 s/slice, scan time <1 min). Clinical utility included virtual biopsies, surgical planning, and treatment monitoring. Limitations included small sample sizes, single-center designs, and vendor variability. ML and DL enhance MRI-based hepatic steatosis assessment, offering high accuracy, reproducibility, and efficiency. CNNs excel in segmentation/PDFF quantification, while GANs and radiomics aid free-breathing MRI and NASH staging. Multi-center studies and standardization are needed for clinical integration.