To train deep learning models to detect hypoxic-ischemic brain injury (HIBI) on early CT scans after pediatric out-of-hospital cardiac arrest (OHCA) and determine if models could identify HIBI that was not visually appreciable to a radiologist. Retrospective study of children who had a CT scan within 24 hours of OHCA compared to age-matched controls. We designed models to detect HIBI by discriminating CT images from OHCA cases and controls, and predict death and unfavorable outcome (PCPC 4-6 at hospital discharge) among cases. Model performance was measured by AUC. We trained a second model to distinguish OHCA cases with radiologist-identified HIBI from controls without OHCA and tested the model on OHCA cases without radiologist-identified HIBI. We compared outcomes between OHCA cases with and without model-categorized HIBI. We analyzed 117 OHCA cases (age 3.1 [0.7-12.2] years); 43% died and 58% had unfavorable outcome. Median time from arrest to CT was 2.1 [1.0,7.2] hours. Deep learning models discriminated OHCA cases from controls with a mean AUC of 0.87±0.05. Among OHCA cases, mean AUCs for predicting death and unfavorable outcome were 0.79±0.06 and 0.69±0.06, respectively. Mean AUC was 0.98±0.01for discriminating between 44 OHCA cases with radiologist-identified HIBI and controls. Among 73 OHCA cases without radiologist-identified HIBI, the model identified 36% as having presumed HIBI; 31% of whom died compared to 17% of cases without HIBI identified radiologically and via the model (p=0.174). Deep learning models can identify HIBI on early CT images after pediatric OHCA and detect some presumed HIBI visually not identified by a radiologist.

Resistance exercise, Taichi exercise, and the hybrid exercise program consisting of the two aforementioned methods have been demonstrated to increase the skeletal muscle mass of older individuals with sarcopenia. However, the exercise sequence has not been comprehensively investigated. Therefore, we designed a self-determined sequence exercise program, incorporating resistance exercises, Taichi, and the hybrid exercise program to overcome the decline of skeletal muscle area and reverse sarcopenia in older individuals. Ninety-one older patients with sarcopenia between the ages of 60 and 75 completed this three-stage randomized controlled trial for 24 weeks, including the self-determined sequence exercise program group (n = 31), the resistance training group (n = 30), and the control group (n = 30). We used quantitative computed tomography to measure the effects of different intervention protocols on skeletal muscle mass in participants. Participants' demographic variables were analyzed using one-way analysis of variance and chi-square tests, and experimental data were examined using repeated-measures analysis of variance. Furthermore, we utilized the Markov model to explain the effectiveness of the exercise programs among the three-stage intervention and explainable artificial intelligence to predict whether intervention programs can reverse sarcopenia. Repeated-measures analysis of variance results indicated that there were statistically significant Group × Time interactions detected in the L3 skeletal muscle density, L3 skeletal muscle area, muscle fat infiltration, handgrip strength, and relative skeletal muscle mass index. The stacking model exhibited the best accuracy (84.5%) and the best F1-score (68.8%) compared to other algorithms. In the self-determined sequence exercise program group, strength training contributed most to the reversal of sarcopenia. One self-determined sequence exercise program can improve skeletal muscle area among sarcopenic older people. Based on our stacking model, we can predict whether sarcopenia in older people can be reversed accurately. The trial was registered in ClinicalTrials.gov. TRN:NCT05694117. Our findings indicate that such tailored exercise interventions can substantially benefit sarcopenic patients, and our stacking model provides an accurate predictive tool for assessing the reversibility of sarcopenia in older adults. This approach not only enhances individual health outcomes but also informs future development of targeted exercise programs to mitigate age-related muscle decline.

Accurate segmentation of hepatic and portal veins is critical for preoperative planning in liver surgery, especially for resection and transplantation procedures. Extensive anatomical variability, pathological alterations, and inherent class imbalance between background and vascular structures challenge this task. Current state-of-the-art deep learning approaches often fail to generalize across patient variability or maintain vascular topology, thus limiting their clinical applicability. To overcome these limitations, we propose the D²-RD-UNet, a dual-stage, dual-class segmentation framework for hepatic and portal vessels. The D²-RD-UNet architecture employs dense and residual connections to improve feature propagation and segmentation accuracy. Our D²-RD-UNet integrates advanced data-driven preprocessing, a dual-path architecture for 3D and 4D data, with the latter concatenating computed tomography (CT) scans with four relevant vesselness filters (Sato, Frangi, OOF, and RORPO). The pipeline is completed by the first developed postprocessing multi-class vessel connectivity correction algorithm based on centerlines. Additionally, we introduce the first radius-based branching algorithm to evaluate the model's predictions locally, providing detailed insights into the accuracy of vascular reconstructions at different scales. In order to make up for the scarcity of well-annotated open datasets for hepatic vessels segmentation, we curated AIMS-HPV-385, a large, pathological, multi-class, and validated dataset on 385 CT scans. We trained different configurations of D²-RD-UNet and state-of-the-art models on 327 CTs of AIMS-HPV-385. Experimental results on the remaining 58 CTs of AIMS-HPV-385 and on the 20 CTs of 3D-IRCADb-01 demonstrate superior performances of the D²-RD-UNet variants over state-of-the-art methods, achieving robust generalization, preserving vascular continuity, and offering a reliable approach for liver vascular reconstructions.

The clinical diagnosis of clear cell renal cell carcinoma (ccRCC) primarily depends on histopathological analysis and computed tomography (CT). Although pathological diagnosis is regarded as the gold standard, invasive procedures such as biopsy carry the risk of tumor dissemination. Conversely, CT scanning offers a non-invasive alternative, but its resolution may be inadequate for detecting microscopic tumor features, which limits the performance of prognostic assessments. To address this issue, we propose a high-order correlation-driven method for predicting the survival of ccRCC using only CT images, achieving performance comparable to that of the pathological gold standard. The proposed method utilizes a cross-modal hypergraph neural network based on hypergraph transfer learning to perform high-order correlation modeling and semantic feature extraction from whole-slide pathological images and CT images. By employing multi-kernel maximum mean discrepancy, we transfer the high-order semantic features learned from pathological images to the CT-based hypergraph neural network channel. During the testing phase, high-precision survival predictions were achieved using only CT images, eliminating the need for pathological images. This approach not only reduces the risks associated with invasive examinations for patients but also significantly enhances clinical diagnostic efficiency. The proposed method was validated using four datasets: three collected from different hospitals and one from the public TCGA dataset. Experimental results indicate that the proposed method achieves higher concordance indices across all datasets compared to other methods.

Deep learning-based medical image segmentation faces significant challenges arising from limited labeled data and domain shifts. While prior approaches have primarily addressed these issues independently, their simultaneous occurrence is common in medical imaging. A method that generalizes to unseen domains using only minimal annotations offers significant practical value due to reduced data annotation and development costs. In pursuit of this goal, we propose FSDA-DG, a novel solution to improve cross-domain generalizability of medical image segmentation with few single-source domain annotations. Specifically, our approach introduces semantics-guided semi-supervised data augmentation. This method divides images into global broad regions and semantics-guided local regions, and applies distinct augmentation strategies to enrich data distribution. Within this framework, both labeled and unlabeled data are transformed into extensive domain knowledge while preserving domain-invariant semantic information. Additionally, FSDA-DG employs a multi-decoder U-Net pipeline semi-supervised learning (SSL) network to improve domain-invariant representation learning through consistent prior assumption across multiple perturbations. By integrating data-level and model-level designs, FSDA-DG achieves superior performance compared to state-of-the-art methods in two challenging single domain generalization (SDG) tasks with limited annotations. The code is publicly available at https://github.com/yezanting/FSDA-DG.

Fetal MRI is increasingly being employed in the diagnosis of fetal lung anomalies and segmentation-derived total fetal lung volumes are used as one of the parameters for prediction of neonatal outcomes. However, in clinical practice, segmentation is performed manually in 2D motion-corrupted stacks with thick slices which is time consuming and can lead to variations in estimated volumes. Furthermore, there is a known lack of consensus regarding a universal lung parcellation protocol and expected normal total lung volume formulas. The lungs are also segmented as one label without parcellation into lobes. In terms of automation, to the best of our knowledge, there have been no reported works on multi-lobe segmentation for fetal lung MRI. This work introduces the first automated deep learning segmentation pipeline for multi-regional lung segmentation for 3D motion-corrected T2w fetal body images for normal anatomy and congenital diaphragmatic hernia cases. The protocol for parcellation into 5 standard lobes was defined in the population-averaged 3D atlas. It was then used to generate a multi-label training dataset including 104 normal anatomy controls and 45 congenital diaphragmatic hernia cases from 0.55T, 1.5T and 3T acquisition protocols. The performance of 3D Attention UNet network was evaluated on 18 cases and showed good results for normal lung anatomy with expectedly lower Dice values for the ipsilateral lung. In addition, we also produced normal lung volumetry growth charts from 290 0.55T and 3T controls. This is the first step towards automated multi-regional fetal lung analysis for 3D fetal MRI.

ImportanceDifferentiating pseudopapilloedema from papilloedema is challenging, but critical for prompt diagnosis and to avoid unnecessary invasive procedures. Following diagnosis of papilloedema, objectively grading severity is important for determining urgency of management and therapeutic response. Automated machine learning (AutoML) has emerged as a promising tool for diagnosis in medical imaging and may provide accessible opportunities for consistent and accurate diagnosis and severity grading of papilloedema. ObjectiveThis study evaluates the feasibility of AutoML models for distinguishing the presence and severity of papilloedema using near infrared reflectance images (NIR) obtained from standard optical coherence tomography (OCT), comparing the performance of different AutoML platforms. Design, setting and participantsA retrospective cohort study was conducted using data from University Hospitals of Leicester, NHS Trust. The study involved 289 adults and children patients (813 images) who underwent optic nerve head-centred OCT imaging between 2021 and 2024. The dataset included patients with normal optic discs (69 patients, 185 images), papilloedema (135 patients, 372 images), and optic disc drusen (ODD) (85 patients, 256 images). AutoML platforms - Amazon Rekognition, Medic Mind (MM) and Google Vertex were evaluated for their ability to classify and grade papilloedema severity. Main outcomes and measuresTwo classification tasks were performed: (1) distinguishing papilloedema from normal discs and ODD; (2) grading papilloedema severity (mild/moderate vs. severe). Model performance was evaluated using area under the curve (AUC), precision, recall, F1 score, and confusion matrices for all six models. ResultsAmazon Rekognition outperformed the other platforms, achieving the highest AUC (0.90) and F1 score (0.81) in distinguishing papilloedema from normal/ODD. For papilloedema severity grading, Amazon Rekognition also performed best, with an AUC of 0.90 and F1 score of 0.79. Google Vertex and Medic Mind demonstrated good performance but had slightly lower accuracy and higher misclassification rates. Conclusions and relevanceThis evaluation of three widely available AutoML platforms using NIR images obtained from standard OCT shows promise in distinguishing and grading papilloedema. These models provide an accessible, scalable solution for clinical teams without coding expertise to feasibly develop intelligent diagnostic systems to recognise and characterise papilloedema. Further external validation and prospective testing is needed to confirm their clinical utility and applicability in diverse settings. Key PointsQuestion: Can clinician-led, code-free deep learning models using automated machine learning (AutoML) accurately differentiate papilloedema from pseudopapilloedema using optic disc imaging? Findings: Three widely available AutoML platforms were used to develop models that successfully distinguish the presence and severity of papilloedema on optic disc imaging, with Amazon Rekognition demonstrating the highest performance. Meaning: AutoML may assist clinical teams, even those with limited coding expertise, in diagnosing papilloedema, potentially reducing the need for invasive investigations.

To review the latest developments in self-supervised deep learning (DL) techniques for magnetic resonance imaging (MRI) reconstruction, emphasizing their potential to overcome the limitations of supervised methods dependent on fully sampled k-space data. While DL has significantly advanced MRI, supervised approaches require large amounts of fully sampled k-space data for training-a major limitation given the impracticality and expense of acquiring such data clinically. Self-supervised learning has emerged as a promising alternative, enabling model training using only undersampled k-space data, thereby enhancing feasibility and driving research interest. We conducted a comprehensive literature review to synthesize recent progress in self-supervised DL for MRI reconstruction. The analysis focused on methods and architectures designed to improve image quality, reduce scanning time, and address data scarcity challenges, drawing from peer-reviewed publications and technical innovations in the field. Self-supervised DL holds transformative potential for MRI reconstruction, offering solutions to data limitations while maintaining image quality and accelerating scans. Key challenges include robustness across diverse anatomies, standardization of validation, and clinical integration. Future research should prioritize hybrid methodologies, domain-specific adaptations, and rigorous clinical validation. This review consolidates advancements and unresolved issues, providing a foundation for next-generation medical imaging technologies.

To evaluate the depiction capability of fine lung nodules and airways using high-resolution settings on ultra-high-resolution energy-integrating detector CT (UHR-CT), incorporating large matrix sizes, thin-slice thickness, and iterative reconstruction (IR)/deep-learning reconstruction (DLR), using cadaveric human lungs and corresponding histological images. Images of 20 lungs were acquired using conventional CT (CCT), UHR-CT, and photon-counting detector CT (PCD-CT). CCT images were reconstructed with a 512 matrix and IR (CCT-512-IR). UHR-CT images were reconstructed with four settings by varying the matrix size and the reconstruction method: UHR-512-IR, UHR-1024-IR, UHR-2048-IR, and UHR-1024-DLR. Two imaging settings of PCD-CT were used: PCD-512-IR and PCD-1024-IR. CT images were visually evaluated and compared with histology. Overall, 6769 nodules (median: 1321 µm) and 92 airways (median: 851 µm) were evaluated. For nodules, UHR-2048-IR outperformed CCT-512-IR, UHR-512-IR, and UHR-1024-IR (p < 0.001). UHR-1024-DLR showed no significant difference from UHR-2048-IR in the overall nodule score after Bonferroni correction (uncorrected p = 0.043); however, for nodules > 1000 μm, UHR-2048-IR demonstrated significantly better scores than UHR-1024-DLR (p = 0.003). For airways, UHR-1024-IR and UHR-512-IR showed significant differences (p < 0.001), with no notable differences among UHR-1024-IR, UHR-2048-IR, and UHR-1024-DLR. UHR-2048-IR detected nodules and airways with median diameters of 604 µm and 699 µm, respectively. No significant difference was observed between UHR-512-IR and PCD-512-IR (p > 0.1). PCD-1024-IR outperformed UHR-CTs for nodules > 1000 μm (p ≤ 0.001), while UHR-1024-DLR outperformed PCD-1024-IR for airways > 1000 μm (p = 0.005). UHR-2048-IR demonstrated the highest scores among the evaluated EID-CT images. UHR-CT showed potential for detecting submillimeter nodules and airways. With the 512 matrix, UHR-CT demonstrated performance comparable to PCD-CT. Question There are scarce data evaluating the depiction capabilities of ultra-high-resolution energy-integrating detector CT (UHR-CT) for fine structures, nor any comparisons with photon-counting detector CT (PCD-CT). Findings UHR-CT depicted nodules and airways with median diameters of 604 µm and 699 µm, showing no significant difference from PCD-CT with the 512 matrix. Clinical relevance High-resolution imaging is crucial for lung diagnosis. UHR-CT has the potential to contribute to pulmonary nodule diagnosis and airway disease evaluation by detecting fine opacities and airways.

Magnetic resonance imaging (MRI) is a crucial tool for visualizing orbital structures and detecting eye pathologies. However, manual segmentation of orbital anatomy is challenging due to the complexity and variability of the structures. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNNs), offer promising solutions for automated segmentation in medical imaging. This study aimed to train and evaluate a U-Net-based model for the automated segmentation of key orbital structures. This retrospective study included 117 patients with various orbital pathologies who underwent orbital MRI. Manual segmentation was performed on four anatomical structures: the ocular bulb, ocular tumors, retinal detachment, and the optic nerve. Following the UNet autoconfiguration by nnUNet, we conducted a five-fold cross-validation and evaluated the model's performances using Dice Similarity Coefficient (DSC) and Relative Absolute Volume Difference (RAVD) as metrics. nnU-Net achieved high segmentation performance for the ocular bulb (mean DSC: 0.931) and the optic nerve (mean DSC: 0.820). Segmentation of ocular tumors (mean DSC: 0.788) and retinal detachment (mean DSC: 0.550) showed greater variability, with performance declining in more challenging cases. Despite these challenges, the model achieved high detection rates, with ROC AUCs of 0.90 for ocular tumors and 0.78 for retinal detachment. This study demonstrates nnU-Net's capability for accurate segmentation of orbital structures, particularly the ocular bulb and optic nerve. However, challenges remain in the segmentation of tumors and retinal detachment due to variability and artifacts. Future improvements in deep learning models and broader, more diverse datasets may enhance segmentation performance, ultimately aiding in the diagnosis and treatment of orbital pathologies.