Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention最新文献

{"title":"RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches.","authors":"Shawn Mathew, Saad Nadeem, Arie Kaufman","doi":"10.1007/978-3-032-05127-1_43","DOIUrl":"10.1007/978-3-032-05127-1_43","url":null,"abstract":"<p><p>Fourteen million colonoscopies are performed annually just in the U.S. However, the videos from these colonoscopies are not saved due to storage constraints (each video from a high-definition colonoscope camera can be in tens of gigabytes). Instead, a few relevant individual frames are saved for documentation/reporting purposes and these are the frames on which most current colonoscopy AI models are trained on. While developing new unsupervised domain translation methods for colonoscopy (e.g. to translate between real optical and virtual/CT colonoscopy), it is thus typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, additional contiguous frames are added with a modified deep learning architecture to train a new model from scratch for temporal consistency. This transition to temporally-consistent deep learning models, however, requires significantly more computational and memory resources for training. In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy: haustral fold segmentation (indicative of missed surface) and realistic colonoscopy simulator video generation. We also release a first-of-its kind temporal dataset for colonoscopy for the above use cases. The datasets, accompanying code, and pretrained models will be made available on our Computational Endoscopy Platform GitHub (https://github.com/nadeemlab/CEP).</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15969 ","pages":"446-455"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12906703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models.","authors":"Bidur Khanal, Sandesh Pokhrel, Sanjay Bhandari, Ramesh Rana, Nikesh Shrestha, Ram B Gurung, Cristian Linte, Angus Watson, Yash R Shrestha, Binod Bhattarai","doi":"10.1007/978-3-032-05127-1_23","DOIUrl":"10.1007/978-3-032-05127-1_23","url":null,"abstract":"<p><p>Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language. Existing VLMs demonstrate an impressive ability to comprehend medical images and text queries to generate detailed, descriptive diagnostic medical reports. However, hallucination-the tendency to generate descriptions that are inconsistent with the visual content-remains a significant issue in VLMs, with particularly severe implications in the medical field. To facilitate VLM research on gastrointestinal (GI) image analysis and study hallucination, we curate a multimodal image-text GI dataset: Gut-VLM. This dataset is created using a two-stage pipeline: first, descriptive medical reports of Kvasir-v2 images are generated using ChatGPT, which introduces some hallucinated or incorrect texts. In the second stage, medical experts systematically review these reports, and identify and correct potential inaccuracies to ensure high-quality, clinically reliable annotations. Unlike traditional datasets that contain only descriptive texts, our dataset also features tags identifying hallucinated sentences and their corresponding corrections. A common approach to reducing hallucination in VLM is to finetune the model on a small-scale, problem-specific dataset. However, we take a different strategy using our dataset. Instead of finetuning the VLM solely for generating textual reports, we finetune it to detect and correct hallucinations, an approach we call hallucination-aware finetuning. Our results show that this approach is better than simply finetuning for descriptive report generation. Additionally, we conduct an extensive evaluation of state-of-the-art VLMs across several metrics, establishing a benchmark.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15969 ","pages":"235-245"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13007979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Transformers and Higher-Order Graph Matching for Cell Tracking in Serial Tissue Sections.","authors":"Mostafa Karami, Sahand Hamzehei, David Arce, Gianna Raimondi, Linnaea Ostroff, Sheida Nabavi","doi":"10.1007/978-3-032-05162-2_9","DOIUrl":"10.1007/978-3-032-05162-2_9","url":null,"abstract":"<p><p>Reliable 3D reconstruction of tissue architecture from sequential 2D multiplex images is challenging due to the noise and distortions introduced by ultrathin (50 nm) slicing and complex alignment procedures. Conventional cell tracking methods often fail under such conditions, resulting in inaccurate linkage of cells across sections. To bridge this gap, we propose a Bayesian Transformer framework that incorporates uncertainty-aware feature embeddings and higher-order graph matching with belief propagation. By tracking cells across consecutive sections, our method facilitates the 3D reconstruction of volumetric tissue organization, even in highly noise-prone scenarios. The methodology begins with a standard segmentation step, followed by feature extraction that computes morphological, shape, and texture descriptors, as well as deep CNN embeddings. These rich, uncertainty-sensitive representations reduce errors caused by both registration artifacts and morphological variability. We validate the effectiveness of the proposed approach on a private multiplex dataset of fixed tissue sections and further demonstrate its generalizability on public time-lapse microscopy videos, showcasing adaptability to diverse datasets. Experimental comparisons reveal that our method outperforms baseline tracking techniques, achieving higher accuracy and more consistent cell linkages across multiple serial sections. The code used in this research with sample dataset are publicly available at https://github.com/NabaviLab/bayesian-transformer-cell-tracking.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15971 ","pages":"87-97"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13198737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation.","authors":"Sebastian Diaz, Benjamin Billot, Neel Dey, Molin Zhang, Esra Abaci Turk, P Ellen Grant, Polina Golland, Elfar Adalsteinsson","doi":"10.1007/978-3-032-04981-0_52","DOIUrl":"10.1007/978-3-032-04981-0_52","url":null,"abstract":"<p><p>Fetal motion is a critical indicator of neurological development and intrauterine health, yet its quantification remains challenging, particularly at earlier gestational ages (GA). Current methods track fetal motion by predicting the location of annotated landmarks on 3D echo planar imaging (EPI) time-series, primarily in third-trimester fetuses. The predicted landmarks enable simplification of the fetal body for downstream analysis. While these methods perform well within their training age distribution, they consistently fail to generalize to early GAs due to significant anatomical changes in both mother and fetus across gestation, as well as the difficulty of obtaining annotated early GA EPI data. In this work, we develop a cross-population data augmentation framework that enables pose estimation models to robustly generalize to younger GA clinical cohorts using only annotated images from older GA cohorts. Specifically, we introduce a fetal-specific augmentation strategy that simulates the distinct intrauterine environment and fetal positioning of early GAs. Our experiments find that cross-population augmentation yields reduced variability and significant improvements across both older GA and challenging early GA cases. By enabling more reliable pose estimation across gestation, our work potentially facilitates early clinical detection and intervention in challenging 4D fetal imaging settings. Code is available at https://github.com/sebodiaz/cross-population-pose.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15966 ","pages":"549-559"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13021261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147577317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fetuses Made Simple: Modeling and Tracking of Fetal Shape and Pose.","authors":"Yingcheng Liu, Peiqi Wang, Sebastian Diaz, Esra Abaci Turk, Benjamin Billot, P Ellen Grant, Polina Golland","doi":"10.1007/978-3-032-05141-7_19","DOIUrl":"10.1007/978-3-032-05141-7_19","url":null,"abstract":"<p><p>Analyzing fetal body motion and shape is paramount in prenatal diagnostics and monitoring. Existing methods for fetal MRI analysis mainly rely on anatomical keypoints or volumetric body segmentations. Keypoints simplify body structure to facilitate motion analysis, but may ignore important details of full-body shape. Body segmentations capture complete shape information but complicate temporal analysis due to large non-local fetal movements. To address these limitations, we construct a 3D articulated statistical fetal body model based on the Skinned Multi-Person Linear Model (SMPL). Our algorithm iteratively estimates body pose in the image space and body shape in the canonical pose space. This approach improves robustness to MRI motion artifacts and intensity distortions, and reduces the impact of incomplete surface observations due to challenging fetal poses. We train our model on segmentations and keypoints derived from 19, 816 MRI volumes across 53 subjects. Our model captures body shape and motion across time series and provides intuitive visualization. Furthermore, it enables automated anthropometric measurements traditionally difficult to obtain from segmentations and keypoints. When tested on unseen fetal body shapes, our method yields a surface alignment error of 3.2 mm for 3 mm MRI voxel size. To our knowledge, this represents the first 3D articulated statistical fetal body model, paving the way for enhanced fetal motion and shape analysis in prenatal diagnostics. The code is available at https://github.com/MedicalVisionGroup/fetal-smpl.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15970 ","pages":"189-198"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13036607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147597330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time SLAM-Based Correction and 3D Visualization for Fluorescence Lifetime Imaging.","authors":"Murali Krishnamoorthy, Haoyin Zhou, Katherine Frazee, Rahul Pal, Jayender Jagadeesan, Anand T N Kumar","doi":"10.1007/978-3-032-05141-7_47","DOIUrl":"10.1007/978-3-032-05141-7_47","url":null,"abstract":"<p><p>Fluorescence lifetime (FLT) imaging has been shown to distinguish tumors from normal tissue with high accuracy. However, the practical utility of FLT imaging is hindered by slow acquisition speeds and depth-dependent inaccuracies. To address these challenges, we introduce FLT-SLAM, a novel algorithm that combines rapid FLT imaging with simultaneous localization and mapping (SLAM) for real-time 3D surface reconstruction and depth-corrected FLT estimation. Using a stereo laparoscope, our approach extracts real-time depth information to improve accuracy, while achieving acquisition speeds exceeding 5 Hz. FLT maps are overlaid onto large-scale 3D surface models generated by SLAM, improving visualization and spatial awareness. We validate FLT-SLAM through phantom and ex-vivo tissue measurements, and show that it reduces FLT estimation errors by nearly 20%, thereby demonstrating its potential to enhance real-time, depth-corrected FLT imaging for surgical applications.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15970 ","pages":"488-497"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13200512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148038744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented Reality-based Guidance with Deformable Registration in Head and Neck Tumor Resection.","authors":"Qingyun Yang, Fangjie Li, Jiayi Xu, Zixuan Liu, Sindhura Sridhar, Whitney Jin, Jennifer Du, Jon Heiselman, Michael Miga, Michael Topf, Jie Ying Wu","doi":"10.1007/978-3-032-05114-1_5","DOIUrl":"10.1007/978-3-032-05114-1_5","url":null,"abstract":"<p><p>Head and neck squamous cell carcinoma has one of the highest rates of recurrence. Recurrence rates can be reduced by accurate localization of positive margins. While frozen section analysis of resected specimens provides accurate intraoperative margin assessment, complex 3D anatomy and significant shrinkage of resected specimens complicate margin relocation from the specimen back to the post-resection cavity. We propose a novel deformable registration framework that uses both the pre-resection external surface and the post-resection cavity of the specimen to incorporate thickness information. In tongue specimens, the proposed framework improved the target registration error (TRE) by up to 33% as compared to using the post-resection cavity alone. We found distinct deformation behaviors in skin, buccal, and tongue specimens, highlighting the need for tailored deformation strategies. Notably, tongue specimens hold the highest clinical need for improvement among head and neck specimens. To further aid intraoperative visualization, we also integrated this framework into an augmented reality-based guidance system. This system can automatically overlay the deformed 3D specimen mesh with positive margin annotation onto the post-resection cavity. The integrated system improved a surgeon and a trainee's average relocation error from 9.8 mm to 4.8 mm in a pilot study. Our implementation code for AR guidance and generating the target point cloud is available at https://github.com/vu-maple-lab/Head-and-Neck-Tumor-Resection-Guidance.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15968 ","pages":"45-54"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13479356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148804030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Sight to Skill: A Surgeon-Centered Augmented Reality System for Ureteroscopy Training.","authors":"Jumanh Atoum, Fangjie Li, Ayberk Acar, Nicholas L Kavoussi, Jie Ying Wu","doi":"10.1007/978-3-032-05141-7_21","DOIUrl":"10.1007/978-3-032-05141-7_21","url":null,"abstract":"<p><p>The number of kidney stone cases in the U.S. has tripled since 1980. Unfortunately, 23% of kidney stone removal surgeries require repeat procedures within 20 months. The high repeat surgery rate is often due to surgeons missing stones in the initial treatment. Effective training can reduce the need for re-operation, yet learning opportunities in the operating room (OR) are limited, as patient care must take priority. Augmented reality (AR) can improve the effectiveness of training in the OR by providing real-time visual feedback, but the interface must be carefully designed not to interfere with clinical workflow. Building on prior design guidelines and AR training tools, we design and evaluate the effectiveness of enhancing training through three AR gaze markers. Our AR training system tracks the expert's eye gaze and projects the marker onto the trainee's head-mounted display to provide visual guidance. Eight trainees performed a simulated ureteroscopy task of identifying kidney stones in high-fidelity kidney phantoms while guided by an expert. We record the number of stones they found, time, and eye-gaze metrics. At the end of each trial, trainees provide subjective feedback on task load and performance through the NASA-TLX questionnaire. Results show that while some gaze markers increased perceived mental demand, they enhanced engagement and performance. Gaze metrics revealed that marker shape affects cognitive load, as measured by the fixation-to-saccades ratio. By translating prior design principles into an AR-based guidance system, this work supports intraoperative training and highlights AR's potential in surgical education.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15970 ","pages":"209-218"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13480462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model.","authors":"Swakshar Deb, Nian Wu, Frederick H Epstein, Miaomiao Zhang","doi":"10.1007/978-3-032-05114-1_62","DOIUrl":"10.1007/978-3-032-05114-1_62","url":null,"abstract":"<p><p>This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos. A multi-level motion feature guided diffusion model, equipped with a specialized Spatio-Temporal Motion Encoder (STME) to extract hierarchical coarse-to-fine motion conditioning, is then developed to improve synthesis quality and fidelity. We evaluate our method, MFD-V2V, on a comprehensive cardiac dataset, demonstrating superior performance over the state-of-the-art in both quantitative metrics and qualitative assessments. Furthermore, we show the benefits of our synthesized cine CMRs improving downstream clinical and analytical tasks, underscoring the broader impact of our approach. Our code is publicly available at https://github.com/SwaksharDeb/MFD-V2V.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15968 ","pages":"648-658"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13491193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148804023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distribution-Guided Multi-Tracer Brain PET Synthesis from Structural MRI with Class-Conditioned Weighted Diffusion.","authors":"Minhui Yu, David S Lalush, Derek C Monroe, Kelly S Giovanello, Weili Lin, Pew-Thian Yap, Jason P Mihalik, Mingxia Liu","doi":"10.1007/978-3-032-04965-0_15","DOIUrl":"10.1007/978-3-032-04965-0_15","url":null,"abstract":"<p><p>Multi-tracer positron emission tomography (PET), which assesses key neurological biomarkers such as tau pathology, neuroinflammatory, <math><mi>β</mi></math> -amyloid deposition, and glucose metabolism, plays a vital role in diagnosing neurological disorders by providing complementary insights into the brain's molecular and functional state. Acquiring multi-tracer PET scans remains challenging due to high costs, radiation exposure, and limited tracer availability. Recent studies have attempted to synthesize multi-tracer PET images from structural MRI. However, these approaches typically either rely on direct mappings to individual tracers or lack distributional constraints, leading to inconsistencies in image quality across tracers. To this end, we propose a normalized diffusion framework (NDF) to generate high-quality multi-tracer PET images from a single MRI through a distribution-guided class-conditioned weighted diffusion model. Specifically, a diffusion model conditioned on MRI and tracer-specific class labels is trained to synthesize PET images of multiple tracers, and a pre-trained normalizing flow model refines these outputs by mapping them into a shared distribution space. This mapping ensures that the subject-specific high-level features across different PET tracers are preserved, resulting in more consistent and accurate synthesis. Experiments on a total of 425 subjects with multi-tracer PET scans demonstrate that our NDF outperforms current state-of-the-art methods, indicating its potential for advancing multi-tracer PET synthesis.</p>","PeriodicalId":94280,"journal":{"name":"Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention","volume":"15963 ","pages":"152-162"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12747406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145866927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}