Accurate and efficient medical image segmentation is crucial but challenging due to anatomical variability and high computational demands on volumetric data. Recent hybrid CNN-Transformer architectures achieve state-of-the-art results but add significant complexity. In this paper, we propose MLRU++, a Multiscale Lightweight Residual UNETR++ architecture designed to balance segmentation accuracy and computational efficiency. It introduces two key innovations: a Lightweight Channel and Bottleneck Attention Module (LCBAM) that enhances contextual feature encoding with minimal overhead, and a Multiscale Bottleneck Block (M2B) in the decoder that captures fine-grained details via multi-resolution feature aggregation. Experiments on four publicly available benchmark datasets (Synapse, BTCV, ACDC, and Decathlon Lung) demonstrate that MLRU++ achieves state-of-the-art performance, with average Dice scores of 87.57% (Synapse), 93.00% (ACDC), and 81.12% (Lung). Compared to existing leading models, MLRU++ improves Dice scores by 5.38% and 2.12% on Synapse and ACDC, respectively, while significantly reducing parameter count and computational cost. Ablation studies evaluating LCBAM and M2B further confirm the effectiveness of the proposed architectural components. Results suggest that MLRU++ offers a practical and high-performing solution for 3D medical image segmentation tasks. Source code is available at: https://github.com/1027865/MLRUPP

Faithfulness and interpretability are essential for deploying deep neural networks (DNNs) in safety-critical domains such as medical imaging. B-cos networks offer a promising solution by replacing standard linear layers with a weight-input alignment mechanism, producing inherently interpretable, class-specific explanations without post-hoc methods. While maintaining diagnostic performance competitive with state-of-the-art DNNs, standard B-cos models suffer from severe aliasing artifacts in their explanation maps, making them unsuitable for clinical use where clarity is essential. In this work, we address these limitations by introducing anti-aliasing strategies using FLCPooling (FLC) and BlurPool (BP) to significantly improve explanation quality. Our experiments on chest X-ray datasets demonstrate that the modified $\text{B-cos}_\text{FLC}$ and $\text{B-cos}_\text{BP}$ preserve strong predictive performance while providing faithful and artifact-free explanations suitable for clinical application in multi-class and multi-label settings. Code available at: GitHub repository (url: https://github.com/mkleinma/B-cos-medical-paper).

Magnetic resonance imaging (MRI) involves a trade-off between imaging time, signal-to-noise ratio (SNR), and spatial resolution. Reducing the imaging time often leads to a lower SNR or resolution. Deep-learning-based reconstruction (DLR) methods have been introduced to address these limitations. Image-domain super-resolution DLR enables high resolution without additional image scans. High-quality images can be obtained within a shorter timeframe by appropriately configuring DLR parameters. It is necessary to maximize the performance of super-resolution DLR to enable efficient use in MRI. We evaluated the performance of a vendor-provided super-resolution DLR method (PIQE) on a Canon 3 T MRI scanner using an edge phantom and clinical brain images from eight patients. Quantitative assessment included structural similarity index (SSIM), peak SNR (PSNR), root mean square error (RMSE), and full width at half maximum (FWHM). FWHM was used to quantitatively assess spatial resolution and image sharpness. Visual evaluation using a five-point Likert scale was also performed to assess perceived image quality. Image domain super-resolution DLR reduced scan time by up to 70 % while preserving the structural image quality. Acquisition matrices of 0.87 mm/pixel or finer with a zoom ratio of ×2 yielded SSIM ≥0.80, PSNR ≥35 dB, and non-significant FWHM differences compared to full-resolution references. In contrast, aggressive downsampling (zoom ratio 3 from low-resolution matrices) led to image degradation including truncation artifacts and reduced sharpness. These results clarify the optimal use of PIQE as an image-domain super-resolution method and provide practical guidance for its application in clinical MRI workflows.

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) present similar clinical symptoms, but their treatment options and clinical prognosis differ significantly. Therefore, we aimed to discriminate between PD and PSP based on multi-level indices of resting-state functional magnetic resonance imaging (rs-fMRI) via the machine learning approach. A total of 58 PD and 52 PSP patients were prospectively enrolled in this study. Participants were randomly allocated to a training set and a validation set in a 7:3 ratio. Various rs-fMRI indices were extracted, followed by a comprehensive feature screening for each index. We constructed fifteen distinct combinations of indices and selected four machine learning algorithms for model development. Subsequently, different validation templates were employed to assess the classification results and investigate the relationship between the most significant features and clinical assessment scales. The classification performance of logistic regression (LR) and support vector machine (SVM) models, based on multiple index combinations, was significantly superior to that of other machine learning models and combinations when utilizing automatic anatomical labeling (AAL) templates. This has been verified across different templates. The utilization of multiple rs-fMRI indices significantly enhances the performance of machine learning models and can effectively achieve the automatic identification of PD and PSP at the individual level.

Thyroid eye disease (TED) is a common, complex orbital disorder characterized by soft-tissue changes visible on imaging. Artificial intelligence (AI) offers promises for improving TED diagnosis and treatment; however, no systematic review has yet characterized the research landscape, key challenges, and future directions. We followed PRISMA guidelines to search multiple databases until January, 2025, for studies applying AI to computed tomography (CT), magnetic resonance imaging, and nuclear, facial or retinal imaging in TED patients. Using the APPRAISE-AI tool, we assessed study quality and included 41 studies covering various AI applications. Sample sizes ranged from 33 to 2,288 participants, predominantly East Asian. CT and facial imaging were the most common modalities, reported in 16 and 13 articles, respectively. Studies addressed clinical tasks-diagnosis, activity assessment, severity grading, and treatment prediction-and technical tasks-classification, segmentation, and image generation-with classification being the most frequent. Researchers primarily employed deep-learning models, such as residual network (ResNet) and Visual Geometry Group (VGG). Overall, the majority of the studies were of moderate quality. Image-based AI shows strong potential to improve diagnostic accuracy and guide personalized treatment strategies in TED. Future research should prioritize robust study designs, the creation of public datasets, multimodal imaging integration, and interdisciplinary collaboration to accelerate clinical translation.

Accurate and efficient medical image segmentation is crucial but challenging due to anatomical variability and high computational demands on volumetric data. Recent hybrid CNN-Transformer architectures achieve state-of-the-art results but add significant complexity. In this paper, we propose MLRU++, a Multiscale Lightweight Residual UNETR++ architecture designed to balance segmentation accuracy and computational efficiency. It introduces two key innovations: a Lightweight Channel and Bottleneck Attention Module (LCBAM) that enhances contextual feature encoding with minimal overhead, and a Multiscale Bottleneck Block (M2B) in the decoder that captures fine-grained details via multi-resolution feature aggregation. Experiments on four publicly available benchmark datasets (Synapse, BTCV, ACDC, and Decathlon Lung) demonstrate that MLRU++ achieves state-of-the-art performance, with average Dice scores of 87.57% (Synapse), 93.00% (ACDC), and 81.12% (Lung). Compared to existing leading models, MLRU++ improves Dice scores by 5.38% and 2.12% on Synapse and ACDC, respectively, while significantly reducing parameter count and computational cost. Ablation studies evaluating LCBAM and M2B further confirm the effectiveness of the proposed architectural components. Results suggest that MLRU++ offers a practical and high-performing solution for 3D medical image segmentation tasks. Source code is available at: https://github.com/1027865/MLRUPP

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Crossview Local-and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-of-the-art infection detection techniques.

Limited-angle computed tomography (LACT) offers patient-friendly benefits, such as rapid scanning and reduced radiation exposure. However, the incompleteness of data in LACT often causes notable artifacts, posing challenges for precise medical interpretation. Although numerous approaches have been introduced to reconstruct LACT images into complete computed tomography (CT) scans, they focus on improving image quality and operate separately from lesion segmentation models, often overlooking essential lesion-specific information. This is because reconstruction models are primarily optimized to satisfy overall image quality rather than local lesion-specific regions, in a non-end-to-end setup where each component is optimized independently and may not contribute to reaching the global minimum of the overall objective function. To address this problem, we propose LA-Seg, a transformer-based segmentation model using the sinogram domain of LACT data. The LA-Seg method uses an auxiliary reconstruction task to estimates incomplete sinogram regions to enhance segmentation robustness. Applying transformers adapted from video prediction models captures the spatial structure and sequential patterns in sinograms and reconstructs features in incomplete regions using a disentangled representation guided by distinctive patterns. We propose contrastive abnormal feature loss to distinguish between normal and abnormal regions better. The experimental results demonstrate that LA-Seg consistently surpasses existing medical segmentation approaches in diverse LACT conditions. The source code is provided at https://github.com/jhyoon964/LA-Seg.

Chat Generative Pre-trained Transformer (ChatGPT)-4V, a large language model developed by OpenAI, has been explored for its potential application in radiology. This study assesses ChatGPT-4V's diagnostic performance in identifying various types of intracranial hemorrhages in non-contrast cranial computed tomography (CT) images. Intracranial hemorrhages were presented to ChatGPT using the clearest 2D imaging slices. The first question, "Q1: Which imaging technique is used in this image?" was asked to determine the imaging modality. ChatGPT was then prompted with the second question, "Q2: What do you see in this image and what is the final diagnosis?" to assess whether the CT scan was normal or showed pathology. For CT scans containing hemorrhage that ChatGPT did not interpret correctly, a follow-up question-"Q3: There is bleeding in this image. Which type of bleeding do you see?"-was used to evaluate whether this guidance influenced its response. ChatGPT accurately identified the imaging technique (Q1) in all cases but demonstrated difficulty diagnosing epidural hematoma (EDH), subdural hematoma (SDH), and subarachnoid hemorrhage (SAH) when no clues were provided (Q2). When a hemorrhage clue was introduced (Q3), ChatGPT correctly identified EDH in 16.7% of cases, SDH in 60%, and SAH in 15.6%, and achieved 100% diagnostic accuracy for hemorrhagic cerebrovascular disease. Its sensitivity, specificity, and accuracy for Q2 were 23.6%, 92.5%, and 57.4%, respectively. These values improved substantially with the clue in Q3, with sensitivity rising to 50.9% and accuracy to 71.3%. ChatGPT also demonstrated higher diagnostic accuracy in larger hemorrhages in EDH and SDH images. Although the model performs well in recognizing imaging modalities, its diagnostic accuracy substantially improves when guided by additional contextual information. These findings suggest that ChatGPT's diagnostic performance improves with guided prompts, highlighting its potential as a supportive tool in clinical radiology.

Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for successful AI technology implementation into clinical workflows. This study aimed to systematically assess and synthesize the facilitators and barriers to AI implementation reported in studies evaluating AI solutions in routine medical imaging. We conducted a systematic review of 6 medical databases. Using a qualitative content analysis, we extracted the reported facilitators and barriers, outcomes, and moderators in the implementation process of AI. Two reviewers analyzed and categorized the data separately. We then used epistemic network analysis to explore their relationships across different stages of AI implementation. Our search yielded 13,756 records. After screening, we included 38 original studies in our final review. We identified 12 key dimensions and 37 subthemes that influence the implementation of AI in health care workflows. Key dimensions included evaluation of AI use and fit into workflow, with frequency depending considerably on the stage of the implementation process. In total, 20 themes were mentioned as both facilitators and barriers to AI implementation. Studies often focused predominantly on performance metrics over the experiences or outcomes of clinicians. This systematic review provides a thorough synthesis of facilitators and barriers to successful AI implementation in medical imaging. Our study highlights the usefulness of AI technologies in clinical care and the fit of their integration into routine clinical workflows. Most studies did not directly report facilitators and barriers to AI implementation, underscoring the importance of comprehensive reporting to foster knowledge sharing. Our findings reveal a predominant focus on technological aspects of AI adoption in clinical work, highlighting the need for holistic, human-centric consideration to fully leverage the potential of AI in health care. PROSPERO CRD42022303439; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022303439. RR2-10.2196/40485.