Journal of imaging informatics in medicine最新文献

{"title":"Multidimensional Dual Encoding Network For Liver Lesion Classification From Multi-Phase Magnetic Resonance Imaging.","authors":"Xinjun An, Jindong Sun, Yixin Zhang, Jian Jiang, Yanjun Peng","doi":"10.1007/s10278-025-01698-x","DOIUrl":"https://doi.org/10.1007/s10278-025-01698-x","url":null,"abstract":"<p><p>Liver cancer has a high mortality rate and is a serious threat to human life. The study of automated methods for analyzing liver cancer is very helpful to doctors in making a diagnosis. The existing methods tend to ignore the information correlation between multiple modalities of magnetic resonance imaging and do not design networks for multiple modalities and liver lesions. These methods are deficient in liver lesion classification and prediction performance, limiting development of the field. Therefore, we consider the information correlation between the multimodalities and design a multidimensional dual encoding network that can make full use of the information between the eight modalities to improve the classification and the prediction performance of liver lesions. It consists of a multidimensional information extraction, a dual encoder, and a classification structure. Firstly, a method for the application of multimodal data is designed, and the multidimensional information extraction module is used to extract two-dimensional (2D) and three-dimensional (3D) information from all modalities. Then, the dual encoder is used to improve feature extraction and pass multi-scale information to the classification structure. Finally, two differently connected networks were used to train the model for joint prediction, improving the final results. In this paper, a multiphase magnetic resonance imaging dataset containing 498 images was used for the experiments. The method was validated by ablation studies and comparisons with state-of-the-art (SOTA) methods, achieving balanced F1 scores, Cohen_Kappa, accuracy, and area under curve of 0.781, 0.731, 0.779, and 0.944, respectively.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noninvasive Multi-Omics Radiomic Model Integrating scRNA-seq and Bulk RNA-seq for Hepatocellular Carcinoma Prognosis.","authors":"Yiping Gao, Yifan Miao, Hongfa Cai, Shuangqing Chen","doi":"10.1007/s10278-025-01668-3","DOIUrl":"https://doi.org/10.1007/s10278-025-01668-3","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) is characterized by high heterogeneity and a complex tumor microenvironment, which challenges conventional prognostic approaches. We developed a machine learning (ML)-based radiomic prognostic model that integrates single-cell and bulk RNA sequencing to improve risk stratification in HCC patients. scRNA-seq analysis was performed, excluding cells with < 200 or > 7500 detected genes or > 20% mitochondrial genes. Dimensionality reduction And clustering identified 2317 co-heterogeneous genes across six cell types. A nine-gene TME signature, based on the intersection with TCGA data, was used to stratify survival risk. We screened radiomic features strongly correlated with TME scores and developed a support vector machine model. Feature importance was assessed by SHAP analysis, and model performance was validated using Cox regression and nomogram analysis. Patients with higher TME risk scores had significantly reduced survival (HR: 2.13, 95% CI: 1.42-3.21, p < 0.001). The SVM model, based on four selected radiomic features, achieved high prognostic accuracy (area under the curve (AUC) = 0.85; C-index = 0.78), and its predictions aligned with nomogram survival estimates. By integrating molecular and imaging data, this radiomic model shows promising prognostic performance and may provide a non-invasive framework for HCC patient stratification.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Visualization of Pancreatic Cystic Lesions on Magnetic Resonance Cholangiopancreatography Using Super-Resolution Deep Learning Reconstruction.","authors":"Jun Kanzawa, Koichiro Yasaka, Yusuke Asari, Mai Sato, Saori Koshino, Yuki Sonoda, Shigeru Kiryu, Osamu Abe","doi":"10.1007/s10278-025-01708-y","DOIUrl":"https://doi.org/10.1007/s10278-025-01708-y","url":null,"abstract":"<p><p>To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) in enhancing the visualization of pancreatic cystic lesions (PCLs) on magnetic resonance cholangiopancreatography (MRCP). This retrospective study included 85 patients who underwent MRCP, comprising 52 patients with PCLs and 33 without. Images reconstructed using SR-DLR were compared with original images. Quantitative metrics included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the common bile duct (CBD) and PCLs, as well as full width at half maximum (FWHM), edge rise distance (ERD), and edge rise slope (ERS) of the CBD and main pancreatic duct (MPD). Qualitative evaluation was conducted by three radiologists, assessing the depiction of PCLs and the MPD, image sharpness, noise, artifacts, overall image quality, and the connection of PCLs and MPD. Quantitative and qualitative metrics were compared using paired t-test and the Wilcoxon signed rank test. SR-DLR significantly enhanced SNR and CNR (p < 0.001). Image sharpness was also enhanced, as shown by lower ERD and higher ERS in both CBD and MPD, together with reduced FWHM of the MPD (p < 0.005). Qualitative assessments indicated improved depiction of PCLs and image sharpness with SR-DLR across all readers (p ≤ 0.017). Most readers also reported improved visualization of the MPD and reduced noise, and overall quality. There was no statistically significant difference in determining the connectivity between PCLs and the MPD. SR-DLR significantly enhances image quality in MRCP, improving visualization of PCLs. These findings suggest that SR-DLR can contribute to appropriate management of PCLs.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NCA-EVA: An Innovative Ensemble-Based Approach for Alzheimer's Disease Detection from Magnetic Resonance Imaging.","authors":"Esra Yüzgeç Özdemir, Canan Koç, Fatih Özyurt","doi":"10.1007/s10278-025-01706-0","DOIUrl":"https://doi.org/10.1007/s10278-025-01706-0","url":null,"abstract":"<p><p>Alzheimer's disease is a progressive neurodegenerative disorder that is challenging to diagnose at an early stage. Affecting over 55 million people worldwide, its prevalence is expected to rise sharply by 2030. The use of artificial intelligence (AI) techniques has become increasingly important to improve the speed and accuracy of diagnosis. In this study, we propose the NCA-Enhanced Voting Algorithm for Alzheimer's Classification (NCA-EVA) to support computer-aided diagnosis. A total of 66 models were trained for four-class data and six models for two-class data. The proposed method successfully classified all four stages of Alzheimer's disease, achieving 98.97% accuracy in four-class classification and 99.87% accuracy in binary classification. Moreover, with a processing time of just 1.26 s, NCA-EVA is approximately 1200 times faster than a comparable study using NCA-based feature selection. These findings demonstrate that Alzheimer's diagnosis can be performed both quickly and with high accuracy, and the proposed approach has potential applications in other healthcare data analysis tasks.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network.","authors":"Fubao Zhu, Yang Zhang, Gengmin Liang, Jiaofen Nan, Yanting Li, Chuang Han, Danyang Sun, Zhiguo Wang, Chen Zhao, Wenxuan Zhou, Jian He, Yi Xu, Iokfai Cheang, Xu Zhu, Yanli Zhou, Weihua Zhou","doi":"10.1007/s10278-025-01705-1","DOIUrl":"https://doi.org/10.1007/s10278-025-01705-1","url":null,"abstract":"<p><p>Early and accurate diagnosis of pulmonary hypertension (PH), including differentiating pre-capillary from post-capillary PH, is crucial for guiding effective clinical management. This study developed and validated a deep learning-based diagnostic model to classify patients into non-PH, pre-capillary PH, or post-capillary PH categories. A retrospective dataset from 204 patients (112 pre-capillary PH, 32 post-capillary PH, and 60 non-PH controls) was collected at the First Affiliated Hospital of Nanjing Medical University, with diagnoses confirmed by right heart catheterization (RHC). Patients were randomly divided into training (186 patients, 90%) and testing sets (18 patients, 10%) stratified by diagnostic category. We trained and evaluated the model using 35 repeated random splits. The proposed deep learning model combined graph convolutional networks (GCN), convolutional neural networks (CNN), and Transformers to analyze multimodal data, including cine short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Across test splits, the model achieved an overall area under the receiver operating characteristic curve (AUC) of 0.814 ± 0.06 and accuracy (ACC) of 0.734 ± 0.06 (mean ± SD). Class-specific AUCs were 0.745 ± 0.11 for non-PH, 0.863 ± 0.06 for pre-capillary PH, and 0.834 ± 0.10 for post-capillary PH, indicating good discriminative ability. This study demonstrated three-class PH classification using multimodal inputs. By fusing imaging and clinical data, the model may support accurate and timely clinical decision-making in PH.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.","authors":"Sebastian Buzdugan, Moona Mazher, Domenec Puig","doi":"10.1007/s10278-025-01692-3","DOIUrl":"https://doi.org/10.1007/s10278-025-01692-3","url":null,"abstract":"<p><p>Glioblastoma (GBM) remains one of the most formidable brain malignancies, characterized by a heterogeneous genetic profile that significantly influences patient prognosis. Per the 2021 WHO central nervous system classification, GBM is defined as an isocitrate dehydrogenase (IDH) wild-type diffuse astrocytic tumor. We analyzed two multi-institutional cohorts, UPENN-GBM (644 patients) and UCSF-PDGM (420 patients); after excluding the 116 and 42 IDH-mutant records, 528 and 378 wild-type cases remained for modelling. MGMT promoter methylation, present in 43% of GBM cases, correlates with enhanced survival outcomes, demonstrating a median survival of 504 days versus 329 days in unmethylated cases. In this study, we present a novel integration of imaging phenotypes, clinical characteristics, and molecular markers through the application of advanced machine learning methodologies, including Random Forest, XGBoost, LightGBM, and an optimized dense neural network (Dense NN). This integrative approach aims to refine survival prediction in GBM patients. MRI data were meticulously processed using the MRIPreprocessor tool and the radiomics Python library, facilitating the extraction of high-dimensional radiomic features. Our findings reveal that the proposed custom Dense NN model outperformed traditional tree-based algorithms, with the Dense NN achieving a concordance index (CI) of 0.86 on the UPENN-GBM dataset and 0.83 on the UCSF-PDGM dataset. The optimized Dense NN architecture features three hidden layers with 256, 128, and 64 units respectively, employing ReLU activation, L1/L2 regularization to mitigate overfitting, batch normalization to stabilize training, and dropout for improved generalization. This specific configuration was determined through hyperparameter tuning using techniques like RandomizedSearchCV. This integrative, non-invasive methodology provides a more nuanced assessment of tumor biology, thereby advancing the development of personalized therapeutic strategies. Our results underscore the transformative potential of artificial intelligence in delineating disease trajectories and optimizing treatment paradigms. Moreover, this research establishes a robust framework for future investigations in glioblastoma survival prediction, illustrating the efficacy of combining clinical, genetic, and imaging data to enhance prognostic accuracy within precision medicine paradigms for GBM patients.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Detection of Pulmonary Edema in Chest X-rays Using Deep Learning Ensembles with Attention Mechanism.","authors":"Waseem Abbasi, Afshan Shahzadi, Abeer Aljohani","doi":"10.1007/s10278-025-01710-4","DOIUrl":"https://doi.org/10.1007/s10278-025-01710-4","url":null,"abstract":"<p><p>Pulmonary edema, defined by the abnormal presence of excess fluid within the lungs, is a severe medical emergency that mandates accurate and immediate diagnosis. The use of classical diagnostic techniques-inspection, palpation, percussion, and auscultation-tends to be subjective and highly dependent on the clinician's experience, potentially resulting in variability in diagnosis and possible delays in treatment. This work provides a deep learning approach to the automatic diagnosis of pulmonary edema from chest X-ray images based on the NIH Chest X-ray dataset. The model based on the proposed CNN obtained a validation loss of 0.3350, an accuracy of 90%, and an F1-score of 0.91. The cross-validation further proved the model to be robust, with a total accuracy of 87%. These findings illustrate the performance of the model in the effective classification of pulmonary edema, hence facilitating quicker and more accurate clinical decision-making. Feature learning and representation were achieved with CNNs, boosted with attention and data augmentation strategies to favor generalization across patient populations and image variations. The integration of transparency aids like attention maps is imperative to validate the model's decision-making process, meeting the key criteria for clinical approval. In summary, this research provides a prospective solution to the early diagnosis of pulmonary edema, further leading to enhanced diagnostic processes and better patient care.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability-Aware Semi-supervised Mutual Learning for Acute Ischemic Stroke Lesion Segmentation.","authors":"Shiwei Hu, Hongqing Zhu, Ziying Wang, Ning Chen, Kai Chen, Zhong Zheng, Weiping Lu, Ying Wang, Bingcang Huang","doi":"10.1007/s10278-025-01707-z","DOIUrl":"https://doi.org/10.1007/s10278-025-01707-z","url":null,"abstract":"<p><p>For patients with acute ischemic stroke (AIS), rapid and accurate lesion localization is critical for improving treatment outcomes. However, automatic stroke lesion segmentation remains highly challenging due to the scarcity of large-scale annotated datasets. Recently, semi-supervised learning (SSL) has achieved remarkable progress in medical image segmentation, yet its performance is still hindered by unreliable pseudo-labels. To address this issue, we propose a novel SSL framework, termed reliability-aware mutual learning (RAML), which employs two subnetworks with a shared encoder, a primary decoder, and an auxiliary decoder. Specifically, RAML introduces uncertain region relearning (URR) regularization, which leverages prediction uncertainty from both subnetworks to identify and refine unreliable regions in labeled images. For unlabeled images, reliability-aware mutual pseudo-supervision (RMPS) regularization is designed to enable cross-supervision based on reliable pseudo-labels. Furthermore, feature difference learning (FDL) regularization is incorporated to promote prediction diversity across subnetworks. Experiments on two acute ischemic stroke datasets and the Left Atrium dataset demonstrate the effectiveness of the proposed RAML in semi-supervised segmentation tasks. The code for this project is available at https://github.com/EricMedimuist/RAML.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"U-Swing: An Adaptive U-Net and Swin Fusion for WB-MRI Whole Spine Bone Marrow Segmentation.","authors":"George G Botis, Theodoros Panagiotis Vagenas, Nikolas Robotis, Vassilis Koutoulidis, Lia A Moulopoulos, George K Matsopoulos","doi":"10.1007/s10278-025-01701-5","DOIUrl":"https://doi.org/10.1007/s10278-025-01701-5","url":null,"abstract":"<p><p>Whole-body MRI (WB-MRI) is a non-invasive imaging technique offering comprehensive anatomical coverage and high-resolution contrast, ideal for evaluating multi-system diseases without ionizing radiation. Recent advancements in parallel imaging have enhanced its utility in oncology and non-oncology applications. WB-MRI is routinely used for cancer staging, including in multiple myeloma (MM), prostate, and colorectal cancer, as well as in evaluating cancer predisposition syndromes and inflammatory conditions. In MM, WB-MRI is crucial for assessing bone marrow involvement and monitoring treatment response. However, manual analysis of WB-MRI for bone marrow (BM) diseases is time-consuming and prone to data loss, limiting its clinical utility. Tumor load in MM is spatially heterogeneous, requiring detailed BM feature extraction-such as size, volume, intensity, and texture-across the entire bone marrow space. Current guidelines, including Myeloma Response Assessment and Diagnosis System (MY-RADS), offer limited interpretation analysis, and automated methods for comprehensive BM characterisation remain underexplored. These goals rely on automated BM segmentation as a foundational step. This study introduces U-Swing, a hybrid deep learning model combining Swin Transformer (SM) and U-Net Modules (UM) designed for WB-MRI whole spine bone marrow segmentation. U-Swing incorporates dynamic feature fusion of the SMs and UMs via U-Swing Patch Fusion and hierarchical optimization through Stage-Wise U-Swing Adaptation (SUA). The model demonstrated superior performance in WB-MRI bone marrow segmentation using T1-weighted turbo spin-echo (T1W-TSE) sequences, achieving a Dice Similarity (DS) score of 0.928, a Hausdorff Distance (HD95) of 3.919 mm, and an Average Symmetric Surface Distance (ASSD) of 0.281 mm, outperforming model architectures such as U-Net, Swin-UNETR, and UNETR.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DECTGoutSys: Reducing False Positive Gout Diagnoses via a Machine Vision Pipeline for Crystal Tophi Identification+Classification in Dual-Energy Computed Tomography (DECT).","authors":"Riel Castro-Zunti, Yunjung Choi, Younhee Choi, Hee Suk Chae, Gong Yong Jin, Eun Hae Park, Seok-Bum Ko","doi":"10.1007/s10278-025-01703-3","DOIUrl":"https://doi.org/10.1007/s10278-025-01703-3","url":null,"abstract":"<p><p>Gout is the world's foremost chronic inflammatory arthritis. Dual-energy computed tomography (DECT) images tophi-monosodium urate (MSU) crystal deposits that indicate gout-as an easily recognizable green color, facilitating high sensitivity. However, tophi-like regions (\"artifacts\") may be found in healthy controls, degrading specificity. To mitigate false positives, we propose the first automated system to localize MSU-presenting crystal deposits from DECT and classify them as gouty tophi or artifacts. Our solution, developed using 47 gout and 27 control patient scans, is three-stage. First, a computer vision algorithm crops green regions of interest (RoIs) from a patient's DECT scan frames and filters obvious false positives. Next, extracted RoIs are classified as tophi or artifact via one of three fine-tuned deep learning models; one model is trained to predict \"small\" RoIs, another \"medium,\" and the third predicts \"large\" RoIs. Size thresholds are based on pixel area quartile statistics. Patient-level gout versus control classification is made via a machine learning system trained using a suite of features calculated from the outcomes of the RoI classifiers. Using 6-fold cross-validation, the proposed pipeline achieved a patient-level diagnostic accuracy, sensitivity, and specificity of 91.89%, 87.23%, and 100.00%. Using confidence values derived from the majority vote of RoI predictions, the best area under the receiver operator characteristics curve (ROC AUC) is 97.16%. The best RoI-level classifiers achieved mean tophus versus artifact accuracy, sensitivity, specificity, and ROC AUC of 89.61%, 85.42%, 93.70%, and 92.72%. Results demonstrate that machine/deep learning facilitates high-specificity gout diagnoses while maintaining respectable sensitivity.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}