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URFM: A general Ultrasound Representation Foundation Model for advancing ultrasound image diagnosis.

Kang Q, Lao Q, Gao J, Bao W, He Z, Du C, Lu Q, Li K

pubmed logopapersAug 15 2025
Ultrasound imaging is critical for clinical diagnostics, providing insights into various diseases and organs. However, artificial intelligence (AI) in this field faces challenges, such as the need for large labeled datasets and limited task-specific model applicability, particularly due to ultrasound's low signal-to-noise ratio (SNR). To overcome these, we introduce the Ultrasound Representation Foundation Model (URFM), designed to learn robust, generalizable representations from unlabeled ultrasound images, enabling label-efficient adaptation to diverse diagnostic tasks. URFM is pre-trained on over 1M images from 15 major anatomical organs using representation-based masked image modeling (MIM), an advanced self-supervised learning. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a specialized medical vision-language model, to address the low SNR issue. Extensive evaluation shows that URFM outperforms state-of-the-art methods, offering enhanced generalization, label efficiency, and training-time efficiency. URFM's scalability and flexibility signal a significant advancement in diagnostic accuracy and clinical workflow optimization in ultrasound imaging.

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Impression Generation on Multi-institution and Multi-system Data.

Zhong T, Zhao W, Zhang Y, Pan Y, Dong P, Jiang Z, Jiang H, Zhou Y, Kui X, Shang Y, Zhao L, Yang L, Wei Y, Li Z, Zhang J, Yang L, Chen H, Zhao H, Liu Y, Zhu N, Li Y, Wang Y, Yao J, Wang J, Zeng Y, He L, Zheng C, Zhang Z, Li M, Liu Z, Dai H, Wu Z, Zhang L, Zhang S, Cai X, Hu X, Zhao S, Jiang X, Zhang X, Liu W, Li X, Zhu D, Guo L, Shen D, Han J, Liu T, Liu J, Zhang T

pubmed logopapersAug 11 2025
Achieving clinical level performance and widespread deployment for generating radiology impressions encounters a giant challenge for conventional artificial intelligence models tailored to specific diseases and organs. Concurrent with the increasing accessibility of radiology reports and advancements in modern general AI techniques, the emergence and potential of deployable radiology AI exploration have been bolstered. Here, we present ChatRadio-Valuer, the first general radiology diagnosis large language model for localized deployment within hospitals and being close to clinical use for multi-institution and multi-system diseases. ChatRadio-Valuer achieved 15 state-of-the-art results across five human systems and six institutions in clinical-level events (n=332,673) through rigorous and full-spectrum assessment, including engineering metrics, clinical validation, and efficiency evaluation. Notably, it exceeded OpenAI's GPT-3.5 and GPT-4 models, achieving superior performance in comprehensive disease diagnosis compared to the average level of radiology experts. Besides, ChatRadio-Valuer supports zero-shot transfer learning, greatly boosting its effectiveness as a radiology assistant, while ensuring adherence to privacy standards and being readily utilized for large-scale patient populations. Our expeditions suggest the development of localized LLMs would become an imperative avenue in hospital applications.

An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolation.

Uhm KH, Cho H, Hong SH, Jung SW

pubmed logopapersAug 8 2025
Computed tomography (CT) is one of the most widely used non-invasive imaging modalities for medical diagnosis. In clinical practice, CT images are usually acquired with large slice thicknesses due to the high cost of memory storage and operation time, resulting in an anisotropic CT volume with much lower inter-slice resolution than in-plane resolution. Since such inconsistent resolution may lead to difficulties in disease diagnosis, deep learning-based volumetric super-resolution methods have been developed to improve inter-slice resolution. Most existing methods conduct single-image super-resolution on the through-plane or synthesize intermediate slices from adjacent slices; however, the anisotropic characteristic of 3D CT volume has not been well explored. In this paper, we propose a novel cross-view texture transfer approach for CT slice interpolation by fully utilizing the anisotropic nature of 3D CT volume. Specifically, we design a unique framework that takes high-resolution in-plane texture details as a reference and transfers them to low-resolution through-plane images. To this end, we introduce a multi-reference non-local attention module that extracts meaningful features for reconstructing through-plane high-frequency details from multiple in-plane images. Through extensive experiments, we demonstrate that our method performs significantly better in CT slice interpolation than existing competing methods on public CT datasets including a real-paired benchmark, verifying the effectiveness of the proposed framework. The source code of this work is available at https://github.com/khuhm/ACVTT.

Role of AI in Clinical Decision-Making: An Analysis of FDA Medical Device Approvals.

Fernando P, Lyell D, Wang Y, Magrabi F

pubmed logopapersAug 7 2025
The U.S. Food and Drug Administration (FDA) plays an important role in ensuring safety and effectiveness of AI/ML-enabled devices through its regulatory processes. In recent years, there has been an increase in the number of these devices cleared by FDA. This study analyzes 104 FDA-approved ML-enabled medical devices from May 2021 to April 2023, extending previous research to provide a contemporary perspective on this evolving landscape. We examined clinical task, device task, device input and output, ML method and level of autonomy. Most approvals (n = 103) were via the 510(k) premarket notification pathway, indicating substantial equivalence to existing devices. Devices predominantly supported diagnostic tasks (n = 81). The majority of devices used imaging data (n = 99), with CT and MRI being the most common modalities. Device autonomy levels were distributed as follows: 52% assistive (requiring users to confirm or approve AI provided information or decision), 27% autonomous information, and 21% autonomous decision. The prevalence of assistive devices indicates a cautious approach to integrating ML into clinical decision-making, favoring support rather than replacement of human judgment.

Segmenting Whole-Body MRI and CT for Multiorgan Anatomic Structure Delineation.

Häntze H, Xu L, Mertens CJ, Dorfner FJ, Donle L, Busch F, Kader A, Ziegelmayer S, Bayerl N, Navab N, Rueckert D, Schnabel J, Aerts HJWL, Truhn D, Bamberg F, Weiss J, Schlett CL, Ringhof S, Niendorf T, Pischon T, Kauczor HU, Nonnenmacher T, Kröncke T, Völzke H, Schulz-Menger J, Maier-Hein K, Hering A, Prokop M, van Ginneken B, Makowski MR, Adams LC, Bressem KK

pubmed logopapersAug 6 2025
<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 develop and validate MRSegmentator, a retrospective cross-modality deep learning model for multiorgan segmentation of MRI scans. Materials and Methods This retrospective study trained MRSegmentator on 1,200 manually annotated UK Biobank Dixon MRI sequences (50 participants), 221 in-house abdominal MRI sequences (177 patients), and 1228 CT scans from the TotalSegmentator-CT dataset. A human-in-the-loop annotation workflow leveraged cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomic structures. The model's performance was evaluated on 900 MRI sequences from 50 participants in the German National Cohort (NAKO), 60 MRI sequences from AMOS22 dataset, and 29 MRI sequences from TotalSegmentator-MRI. Reference standard manual annotations were used for comparison. Metrics to assess segmentation quality included Dice Similarity Coefficient (DSC). Statistical analyses included organ-and sequence-specific mean ± SD reporting and two-sided <i>t</i> tests for demographic effects. Results 139 participants were evaluated; demographic information was available for 70 (mean age 52.7 years ± 14.0 [SD], 36 female). Across all test datasets, MRSegmentator demonstrated high class wise DSC for well-defined organs (lungs: 0.81-0.96, heart: 0.81-0.94) and organs with anatomic variability (liver: 0.82-0.96, kidneys: 0.77-0.95). Smaller structures showed lower DSC (portal/splenic veins: 0.64-0.78, adrenal glands: 0.56-0.69). The average DSC on the external testing using NAKO data, ranged from 0.85 ± 0.08 for T2-HASTE to 0.91 ± 0.05 for in-phase sequences. The model generalized well to CT, achieving mean DSC of 0.84 ± 0.12 on AMOS CT data. Conclusion MRSegmentator accurately segmented 40 anatomic structures on MRI and generalized to CT; outperforming existing open-source tools. Published under a CC BY 4.0 license.

Foundation models for radiology-the position of the AI for Health Imaging (AI4HI) network.

de Almeida JG, Alberich LC, Tsakou G, Marias K, Tsiknakis M, Lekadir K, Marti-Bonmati L, Papanikolaou N

pubmed logopapersAug 6 2025
Foundation models are large models trained on big data which can be used for downstream tasks. In radiology, these models can potentially address several gaps in fairness and generalization, as they can be trained on massive datasets without labelled data and adapted to tasks requiring data with a small number of descriptions. This reduces one of the limiting bottlenecks in clinical model construction-data annotation-as these models can be trained through a variety of techniques that require little more than radiological images with or without their corresponding radiological reports. However, foundation models may be insufficient as they are affected-to a smaller extent when compared with traditional supervised learning approaches-by the same issues that lead to underperforming models, such as a lack of transparency/explainability, and biases. To address these issues, we advocate that the development of foundation models should not only be pursued but also accompanied by the development of a decentralized clinical validation and continuous training framework. This does not guarantee the resolution of the problems associated with foundation models, but it enables developers, clinicians and patients to know when, how and why models should be updated, creating a clinical AI ecosystem that is better capable of serving all stakeholders. CRITICAL RELEVANCE STATEMENT: Foundation models may mitigate issues like bias and poor generalization in radiology AI, but challenges persist. We propose a decentralized, cross-institutional framework for continuous validation and training to enhance model reliability, safety, and clinical utility. KEY POINTS: Foundation models trained on large datasets reduce annotation burdens and improve fairness and generalization in radiology. Despite improvements, they still face challenges like limited transparency, explainability, and residual biases. A decentralized, cross-institutional framework for clinical validation and continuous training can strengthen reliability and inclusivity in clinical AI.

Monitoring ctDNA in Aggressive B-cell Lymphoma: A Prospective Correlative Study of ctDNA Kinetics and PET-CT Metrics.

Vimalathas G, Hansen MH, Cédile OML, Thomassen M, Møller MB, Dahlmann SK, Kjeldsen MLG, Hildebrandt MG, Nielsen AL, Naghavi-Behzad M, Edenbrandt L, Nyvold CG, Larsen TS

pubmed logopapersAug 4 2025
Positron emission tomography-computed tomography (PET-CT) is recommended for response evaluation in aggressive large B-cell lymphoma (LBCL) but cannot detect minimal residual disease (MRD). Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for real-time disease monitoring. This study evaluated longitudinal ctDNA monitoring as an MRD marker in LBCL. In this prospective, single-center study, 14 newly diagnosed LBCL patients receiving first-line immunochemotherapy underwent frequent longitudinal blood sampling. A 53-gene targeted sequencing panel quantified ctDNA and evaluated its kinetics, correlating it with clinical parameters and PET-CT, including total metabolic tumor volume (TMTV) calculated using AI-based analysis via RECOMIA. Baseline ctDNA was detected in 11 out of 14 patients (79%), with a median variant allele frequency of 6.88% (interquartile range: 1.19-10.20%). ctDNA levels correlated significantly with TMTV (ρ = 0.91, p < 0.0001) and lactate dehydrogenase. Circulating tumor DNA kinetics, including after one treatment cycle, mirrored PET-CT metabolic changes and identified relapsing or refractory cases. This study demonstrates ctDNA-based MRD monitoring in LBCL using a fixed targeted assay with an analytical sensitivity of at least 10-3. The kinetics of ctDNA reflects the clinical course and PET-CT findings, underscoring its complementary potential to PET-CT.

Moving Beyond CT Body Composition Analysis: Using Style Transfer for Bringing CT-Based Fully-Automated Body Composition Analysis to T2-Weighted MRI Sequences.

Haubold J, Pollok OB, Holtkamp M, Salhöfer L, Schmidt CS, Bojahr C, Straus J, Schaarschmidt BM, Borys K, Kohnke J, Wen Y, Opitz M, Umutlu L, Forsting M, Friedrich CM, Nensa F, Hosch R

pubmed logopapersAug 1 2025
Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography (CT) over magnetic resonance imaging (MRI). This study presents a deep learning approach for automatic BCA using MR T2-weighted sequences. Initial BCA segmentations (10 body regions and 4 body parts) were generated by mapping CT segmentations from body and organ analysis (BOA) model to synthetic MR images created using an in-house trained CycleGAN. In total, 30 synthetic data pairs were used to train an initial nnU-Net V2 in 3D, and this preliminary model was then applied to segment 120 real T2-weighted MRI sequences from 120 patients (46% female) with a median age of 56 (interquartile range, 17.75), generating early segmentation proposals. These proposals were refined by human annotators, and nnU-Net V2 2D and 3D models were trained using 5-fold cross-validation on this optimized dataset of real MR images. Performance was evaluated using Sørensen-Dice, Surface Dice, and Hausdorff Distance metrics including 95% confidence intervals for cross-validation and ensemble models. The 3D ensemble segmentation model achieved the highest Dice scores for the body region classes: bone 0.926 (95% confidence interval [CI], 0.914-0.937), muscle 0.968 (95% CI, 0.961-0.975), subcutaneous fat 0.98 (95% CI, 0.971-0.986), nervous system 0.973 (95% CI, 0.965-0.98), thoracic cavity 0.978 (95% CI, 0.969-0.984), abdominal cavity 0.989 (95% CI, 0.986-0.991), mediastinum 0.92 (95% CI, 0.901-0.936), pericardium 0.945 (95% CI, 0.924-0.96), brain 0.966 (95% CI, 0.927-0.989), and glands 0.905 (95% CI, 0.886-0.921). Furthermore, body part 2D ensemble model reached the highest Dice scores for all labels: arms 0.952 (95% CI, 0.937-0.965), head + neck 0.965 (95% CI, 0.953-0.976), legs 0.978 (95% CI, 0.968-0.988), and torso 0.99 (95% CI, 0.988-0.991). The overall average Dice across body parts (2D = 0.971, 3D = 0.969, P = ns) and body regions (2D = 0.935, 3D = 0.955, P < 0.001) ensemble models indicates stable performance across all classes. The presented approach facilitates efficient and automated extraction of BCA parameters from T2-weighted MRI sequences, providing precise and detailed body composition information across various regions and body parts.

SAM-Med3D: A Vision Foundation Model for General-Purpose Segmentation on Volumetric Medical Images.

Wang H, Guo S, Ye J, Deng Z, Cheng J, Li T, Chen J, Su Y, Huang Z, Shen Y, zzzzFu B, Zhang S, He J

pubmed logopapersJul 31 2025
Existing volumetric medical image segmentation models are typically task-specific, excelling at specific targets but struggling to generalize across anatomical structures or modalities. This limitation restricts their broader clinical use. In this article, we introduce segment anything model (SAM)-Med3D, a vision foundation model (VFM) for general-purpose segmentation on volumetric medical images. Given only a few 3-D prompt points, SAM-Med3D can accurately segment diverse anatomical structures and lesions across various modalities. To achieve this, we gather and preprocess a large-scale 3-D medical image segmentation dataset, SA-Med3D-140K, from 70 public datasets and 8K licensed private cases from hospitals. This dataset includes 22K 3-D images and 143K corresponding masks. SAM-Med3D, a promptable segmentation model characterized by its fully learnable 3-D structure, is trained on this dataset using a two-stage procedure and exhibits impressive performance on both seen and unseen segmentation targets. We comprehensively evaluate SAM-Med3D on 16 datasets covering diverse medical scenarios, including different anatomical structures, modalities, targets, and zero-shot transferability to new/unseen tasks. The evaluation demonstrates the efficiency and efficacy of SAM-Med3D, as well as its promising application to diverse downstream tasks as a pretrained model. Our approach illustrates that substantial medical resources can be harnessed to develop a general-purpose medical AI for various potential applications. Our dataset, code, and models are available at: https://github.com/uni-medical/SAM-Med3D.

The evolving role of multimodal imaging, artificial intelligence and radiomics in the radiologic assessment of immune related adverse events.

Das JP, Ma HY, DeJong D, Prendergast C, Baniasadi A, Braumuller B, Giarratana A, Khonji S, Paily J, Shobeiri P, Yeh R, Dercle L, Capaccione KM

pubmed logopapersJul 28 2025
Immunotherapy, in particular checkpoint blockade, has revolutionized the treatment of many advanced cancers. Imaging plays a critical role in assessing both treatment response and the development of immune toxicities. Both conventional imaging and molecular imaging techniques can be used to evaluate multisystemic immune related adverse events (irAEs), including thoracic, abdominal and neurologic irAEs. As artificial intelligence (AI) proliferates in medical imaging, radiologic assessment of irAEs will become more efficient, improving the diagnosis, prognosis, and management of patients affected by immune-related toxicities. This review addresses some of the advancements in medical imaging including the potential future role of radiomics in evaluating irAEs, which may facilitate clinical decision-making and improvements in patient care.
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