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Participatory Co-Creation of an AI-Supported Patient Information System: A Multi-Method Qualitative Study.

Heizmann C, Gleim P, Kellmeyer P

pubmed logopapersMay 15 2025
In radiology and other medical fields, informed consent often rely on paper-based forms, which can overwhelm patients with complex terminology. These forms are also resource-intensive. The KIPA project addresses these challenges by developing an AI-assisted patient information system to streamline the consent process, improve patient understanding, and reduce healthcare workload. The KIPA system uses natural language processing (NLP) to provide real-time, accessible explanations, answer questions, and support informed consent. KIPA follows an 'ethics-by-design' approach, integrating user feedback to align with patient and clinician needs. Interviews and usability testing identified requirements, such as simplified language and support for varying digital literacy. The study presented here explores the participatory co-creation of the KIPA system, focusing on improving informed consent in radiology through a multi-method qualitative approach. Preliminary results suggest that KIPA improves patient engagement and reduces insecurities by providing proactive guidance and tailored information. Future work will extend testing to other stakeholders and assess the impact of the system on clinical workflow.

Does Whole Brain Radiomics on Multimodal Neuroimaging Make Sense in Neuro-Oncology? A Proof of Concept Study.

Danilov G, Kalaeva D, Vikhrova N, Shugay S, Telysheva E, Goraynov S, Kosyrkova A, Pavlova G, Pronin I, Usachev D

pubmed logopapersMay 15 2025
Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feasible, albeit less common, approach in neuro-oncology research. This study aims to evaluate the relationship between WB radiomic features, derived from various neuroimaging modalities in patients with gliomas, and some key baseline characteristics of patients and tumors such as sex, histological tumor type, WHO Grade (2021), IDH1 mutation status, necrosis lesions, contrast enhancement, T/N peak value and metabolic tumor volume. Forty-one patients (average age 50 ± 15 years, 21 females and 20 males) with supratentorial glial tumors were enrolled in this study. A total of 38,720 radiomic features were extracted. Cluster analysis revealed that whole-brain images of biologically different tumors could be distinguished to a certain extent based on their imaging biomarkers. Machine learning capabilities to detect image properties like contrast-enhanced or necrotic zones validated radiomic features in objectifying image semantics. Furthermore, the predictive capability of imaging biomarkers in determining tumor histology, grade and mutation type underscores their diagnostic potential. Whole-brain radiomics using multimodal neuroimaging data appeared to be informative in neuro-oncology, making research in this area well justified.

A monocular endoscopic image depth estimation method based on a window-adaptive asymmetric dual-branch Siamese network.

Chong N, Yang F, Wei K

pubmed logopapersMay 15 2025
Minimally invasive surgery involves entering the body through small incisions or natural orifices, using a medical endoscope for observation and clinical procedures. However, traditional endoscopic images often suffer from low texture and uneven illumination, which can negatively impact surgical and diagnostic outcomes. To address these challenges, many researchers have applied deep learning methods to enhance the processing of endoscopic images. This paper proposes a monocular medical endoscopic image depth estimation method based on a window-adaptive asymmetric dual-branch Siamese network. In this network, one branch focuses on processing global image information, while the other branch concentrates on local details. An improved lightweight Squeeze-and-Excitation (SE) module is added to the final layer of each branch, dynamically adjusting the inter-channel weights through self-attention. The outputs from both branches are then integrated using a lightweight cross-attention feature fusion module, enabling cross-branch feature interaction and enhancing the overall feature representation capability of the network. Extensive ablation and comparative experiments were conducted on medical datasets (EAD2019, Hamlyn, M2caiSeg, UCL) and a non-medical dataset (NYUDepthV2), with both qualitative and quantitative results-measured in terms of RMSE, AbsRel, FLOPs and running time-demonstrating the superiority of the proposed model. Additionally, comparisons with CT images show good organ boundary matching capability, highlighting the potential of our method for clinical applications. The key code of this paper is available at: https://github.com/superchongcnn/AttenAdapt_DE .

Accuracy and Reliability of Multimodal Imaging in Diagnosing Knee Sports Injuries.

Zhu D, Zhang Z, Li W

pubmed logopapersMay 15 2025
Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries. To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis. By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture. The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors' diagnosis results, with an overall error rate of less than 2%. The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images

Sadman Sakib Alif, Nasim Anzum Promise, Fiaz Al Abid, Aniqua Nusrat Zereen

arxiv logopreprintMay 14 2025
Lung cancer is a leading cause of cancer-related deaths globally, where early detection and accurate diagnosis are critical for improving survival rates. While deep learning, particularly convolutional neural networks (CNNs), has revolutionized medical image analysis by detecting subtle patterns indicative of early-stage lung cancer, its adoption faces challenges. These models are often computationally expensive and require significant resources, making them unsuitable for resource constrained environments. Additionally, their lack of transparency hinders trust and broader adoption in sensitive fields like healthcare. Knowledge distillation addresses these challenges by transferring knowledge from large, complex models (teachers) to smaller, lightweight models (students). We propose a knowledge distillation-based approach for lung cancer detection, incorporating explainable AI (XAI) techniques to enhance model transparency. Eight CNNs, including ResNet50, EfficientNetB0, EfficientNetB3, and VGG16, are evaluated as teacher models. We developed and trained a lightweight student model, Distilled Custom Student Network (DCSNet) using ResNet50 as the teacher. This approach not only ensures high diagnostic performance in resource-constrained settings but also addresses transparency concerns, facilitating the adoption of AI-driven diagnostic tools in healthcare.

[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].

Danthez N, De Cournuaud C, Pistocchi S, Aureli V, Giammattei L, Hottinger AF, Schiappacasse L

pubmed logopapersMay 14 2025
Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision and preservation of healthy tissues. It is effective for meningiomas, trigeminal neuralgia (TN), pituitary adenomas, vestibular schwannomas, and arteriovenous malformations. SRS ensures high tumor control rates, particularly for Grade I meningiomas and vestibular schwannomas. For refractory TN, it provides initial pain relief > 80 %. The advent of technologies such as PET-MRI, hypofractionation, and artificial intelligence is further improving treatment precision, but challenges remain, including the management of late side effects and standardization of practice.

Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.

Xu Z, Zhong S, Gao Y, Huo J, Xu W, Huang W, Huang X, Zhang C, Zhou J, Dan Q, Li L, Jiang Z, Lang T, Xu S, Lu J, Wen G, Zhang Y, Li Y

pubmed logopapersMay 14 2025
This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, particularly in cases with discordant US and MG Breast Imaging Reporting and Data System (BI-RADS) classifications. We retrospectively collected image data from 1283 women with breast lesions who underwent both US and MG within one month at two medical centres and categorised them into concordant and discordant BI-RADS classification subgroups. We developed a DL-UM network via integrating US and MG images, and DL networks using US (DL-U) or MG (DL-M) alone, respectively. The performance of DL-UM network for breast lesion diagnosis was evaluated using ROC curves and compared to DL-U and DL-M networks in the external testing dataset. The diagnostic performance of radiologists with different levels of experience under the assistance of DL-UM network was also evaluated. In the external testing dataset, DL-UM outperformed DL-M in sensitivity (0.962 vs. 0.833, P = 0.016) and DL-U in specificity (0.667 vs. 0.526, P = 0.030), respectively. In the discordant BI-RADS classification subgroup, DL-UM achieved an AUC of 0.910. The diagnostic performance of four radiologists improved when collaborating with the DL-UM network, with AUCs increased from 0.674-0.772 to 0.889-0.910, specificities from 52.1%-75.0 to 81.3-87.5% and reducing unnecessary biopsies by 16.1%-24.6%, particularly for junior radiologists. Meanwhile, DL-UM outputs and heatmaps enhanced radiologists' trust and improved interobserver agreement between US and MG, with weighted kappa increased from 0.048 to 0.713 (P < 0.05). The DL-UM network, integrating complementary US and MG features, assisted radiologists in improving breast lesion diagnosis and management, potentially reducing unnecessary biopsies.

Whole-body CT-to-PET synthesis using a customized transformer-enhanced GAN.

Xu B, Nie Z, He J, Li A, Wu T

pubmed logopapersMay 14 2025
Positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET-CT) is a multi-modality medical imaging technique widely used for screening and diagnosis of lesions and tumors, in which, CT can provide detailed anatomical structures, while PET can show metabolic activities. Nevertheless, it has disadvantages such as long scanning time, high cost, and relatively high radiation doses.&#xD;&#xD;Purpose: We propose a deep learning model for the whole-body CT-to-PET synthesis task, generating high-quality synthetic PET images that are comparable to real ones in both clinical relevance and diagnostic value.&#xD;&#xD;Material: We collect 102 pairs of 3D CT and PET scans, which are sliced into 27,240 pairs of 2D CT and PET images ( training: 21,855 pairs, validation: 2,810, testing: 2,575 pairs).&#xD;&#xD;Methods: We propose a Transformer-enhanced Generative Adversarial Network (GAN) for whole-body CT-to-PET synthesis task. The CPGAN model uses residual blocks and Fully Connected Transformer Residual (FCTR) blocks to capture both local features and global contextual information. A customized loss function incorporating structural consistency is designed to improve the quality of synthesized PET images.&#xD;&#xD;Results: Both quantitative and qualitative evaluation results demonstrate effectiveness of the CPGAN model. The mean and standard variance of NRMSE,PSNR and SSIM values on test set are (16.90 ± 12.27) × 10-4, 28.71 ± 2.67 and 0.926 ± 0.033, respectively, outperforming other seven state-of-the-art models. Three radiologists independently and blindly evaluated and gave subjective scores to 100 randomly chosen PET images (50 real and 50 synthetic). By Wilcoxon signed rank test, there are no statistical differences between the synthetic PET images and the real ones.&#xD;&#xD;Conclusions: Despite the inherent limitations of CT images to directly reflect biological information of metabolic tissues, CPGAN model effectively synthesizes satisfying PET images from CT scans, which has potential in reducing the reliance on actual PET-CT scans.

Clinical utility of ultrasound and MRI in rheumatoid arthritis: An expert review.

Kellner DA, Morris NT, Lee SM, Baker JF, Chu P, Ranganath VK, Kaeley GS, Yang HH

pubmed logopapersMay 14 2025
Musculoskeletal ultrasound (MSUS) and magnetic resonance imaging (MRI) are advanced imaging techniques that are increasingly important in the diagnosis and management of rheumatoid arthritis (RA) and have significantly enhanced the rheumatologist's ability to assess RA disease activity and progression. This review serves as a five-year update to our previous publication on the contemporary role of imaging in RA, emphasizing the continued importance of MSUS and MRI in clinical practice and their expanding utility. The review examines the role of MSUS in diagnosing RA, differentiating RA from mimickers, scoring systems and quality control measures, novel longitudinal approaches to disease monitoring, and patient populations that may benefit most from MSUS. It also examines the role of MRI in diagnosing pre-clinical and early RA, disease activity monitoring, research and clinical trials, and development of alternative scoring approaches utilizing artificial intelligence. Finally, the role of MRI in RA diagnosis and management is summarized, and selected practice points offer key tips for integrating MSUS and MRI into clinical practice.

Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results

Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas, Matthias Stefan May, Zhaohong Pan, Xiang Zhou, Xiaokun Liang, Franciskus Xaverius Erick, Andrea Prenner, Cedric Hemon, Valentin Boussot, Jean-Louis Dillenseger, Jean-Claude Nunes, Abdul Qayyum, Moona Mazher, Steven A Niederer, Kaisar Kushibar, Carlos Martin-Isla, Petia Radeva, Karim Lekadir, Theodore Barfoot, Luis C. Garcia Peraza Herrera, Ben Glocker, Tom Vercauteren, Lucas Gago, Justin Englemann, Joy-Marie Kleiss, Anton Aubanell, Andreu Antolin, Javier Garcia-Lopez, Miguel A. Gonzalez Ballester, Adrian Galdran

arxiv logopreprintMay 13 2025
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation. This is why we created the Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS), which highlights the critical role of multiple annotators in establishing a more comprehensive ground truth, emphasizing that segmentation is inherently subjective and that leveraging inter-annotator variability is essential for robust model evaluation. Seven teams participated in the challenge, submitting a variety of DL models evaluated using metrics such as Dice Similarity Coefficient (DSC), Expected Calibration Error (ECE), and Continuous Ranked Probability Score (CRPS). By incorporating consensus and dissensus ground truth, we assess how DL models handle uncertainty and whether their confidence estimates align with true segmentation performance. Our findings reinforce the importance of well-calibrated models, as better calibration is strongly correlated with the quality of the results. Furthermore, we demonstrate that segmentation models trained on diverse datasets and enriched with pre-trained knowledge exhibit greater robustness, particularly in cases deviating from standard anatomical structures. Notably, the best-performing models achieved high DSC and well-calibrated uncertainty estimates. This work underscores the need for multi-annotator ground truth, thorough calibration assessments, and uncertainty-aware evaluations to develop trustworthy and clinically reliable DL-based medical image segmentation models.
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