Journal of imaging informatics in medicine最新文献

{"title":"Radiological and Biological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer, Dictionary Version PM1.0.","authors":"Mohammad R Salmanpour, Sajad Amiri, Sara Gharibi, Ahmad Shariftabrizi, Yixi Xu, William B Weeks, Arman Rahmim, Ilker Hacihaliloglu","doi":"10.1007/s10278-025-01585-5","DOIUrl":"https://doi.org/10.1007/s10278-025-01585-5","url":null,"abstract":"<p><p>Artificial intelligence (AI) can advance medical diagnostics, but interpretability limits its clinical use. This work links standardized quantitative Radiomics features (RF) extracted from medical images with clinical frameworks like PI-RADS, ensuring AI models are understandable and aligned with clinical practice. We investigate the connection between visual semantic features defined in PI-RADS and associated risk factors, moving beyond abnormal imaging findings, and establishing a shared framework between medical and AI professionals by creating a standardized radiological/biological RF dictionary. Six interpretable and seven complex classifiers, combined with nine interpretable feature selection algorithms (FSA), were applied to RFs extracted from segmented lesions in T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) multiparametric MRI sequences to predict TCIA-UCLA scores, grouped as low-risk (scores 1-3) and high-risk (scores 4-5). We then utilized the created dictionary to interpret the best predictive models. Combining sequences with FSAs including ANOVA F-test, Correlation Coefficient, and Fisher Score, and utilizing logistic regression, identified key features: The 90th percentile from T2WI, (reflecting hypo-intensity related to prostate cancer risk; Variance from T2WI (lesion heterogeneity; shape metrics including Least Axis Length and Surface Area to Volume ratio from ADC, describing lesion shape and compactness; and Run Entropy from ADC (texture consistency). This approach achieved the highest average accuracy of 0.78 ± 0.01, significantly outperforming single-sequence methods (p-value < 0.05). The developed dictionary for Prostate-MRI (PM1.0) serves as a common language and fosters collaboration between clinical professionals and AI developers to advance trustworthy AI solutions that support reliable/interpretable clinical decisions.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556415","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":"Can Whole-Thyroid-Based CT Radiomics Model Achieve the Performance of Lesion-Based Model in Predicting the Thyroid Nodules Malignancy? - A Comparative Study.","authors":"Wenxia Yuan, Jiayang Wu, Wenfeng Mai, Hengguo Li, Zhenyu Li","doi":"10.1007/s10278-025-01584-6","DOIUrl":"https://doi.org/10.1007/s10278-025-01584-6","url":null,"abstract":"<p><p>Machine learning is now extensively implemented in medical imaging for preoperative risk stratification and post-therapeutic outcome assessment, enhancing clinical decision-making. Numerous studies have focused on predicting whether thyroid nodules are benign or malignant using a nodule-based approach, which is time-consuming, inefficient, and overlooks the impact of the peritumoral region. To evaluate the effectiveness of using the whole-thyroid as the region of interest in differentiating between benign and malignant thyroid nodules, exploring the potential application value of the entire thyroid. This study enrolled 1121 patients with thyroid nodules between February 2017 and May 2023. All participants underwent contrast-enhanced CT scans prior to surgical intervention. Radiomics features were extracted from arterial phase images, and feature dimensionality reduction was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Four machine learning models were trained on the selected features within the training cohort and subsequently evaluated on the independent validation cohort. The diagnostic performance of whole-thyroid versus nodule-based radiomics models was compared through receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) metrics. The nodule-based logistic regression model achieved an AUC of 0.81 in the validation set, with sensitivity, specificity, and accuracy of 78.6%, 69.4%, and 75.6%, respectively. The whole-thyroid-based random forest model attained an AUC of 0.80, with sensitivity, specificity, and accuracy of 90.0%, 51.9.%, and 80.1%, respectively. The AUC advantage ratios on the LR, DT, RF, and SVM models are approximately - 2.47%, 0.00%, - 4.76%, and - 4.94%, respectively. The Delong test showed no significant differences among the four machine learning models regarding the region of interest defined by either the thyroid primary lesion or the whole thyroid. There was no significant difference in distinguishing between benign and malignant thyroid nodules using either a nodule-based or whole-thyroid-based strategy for ROI outlining. We hypothesize that the whole-thyroid approach provides enhanced diagnostic capability for detecting papillary thyroid carcinomas (PTCs) with ill-defined margins.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562545","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":"Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.","authors":"Sahithi Kantu, Hema Sai Kaja, Vaishnavi Kukkala, Salah A Aly, Khaled Sayed","doi":"10.1007/s10278-025-01589-1","DOIUrl":"https://doi.org/10.1007/s10278-025-01589-1","url":null,"abstract":"<p><p>Brain tumors pose a critical health threat requiring timely and accurate classification for effective treatment. Traditional MRI analysis is labor-intensive and prone to variability, necessitating reliable automated solutions. This study explores lightweight deep learning models for multi-class brain tumor classification across four categories: glioma, meningioma, pituitary tumors, and no tumor. We investigate the performance of MobileNetV3 and SqueezeNet individually, and a feature-fusion hybrid model that combines their embedding layers. We utilized a publicly available MRI dataset containing 7023 images with a consistent internal split (65% training, 17% validation, 18% test) to ensure reliable evaluation. MobileNetV3 offers deep semantic understanding through its expressive features, while SqueezeNet provides minimal computational overhead. Their feature-level integration creates a balanced approach between diagnostic accuracy and deployment efficiency. Experiments conducted with consistent hyperparameters and preprocessing showed MobileNetV3 achieved the highest test accuracy (99.31%) while maintaining a low parameter count (3.47M), making it suitable for real-world deployment. Grad-CAM visualizations were employed for model explainability, highlighting tumor-relevant regions and helping visualize the specific areas contributing to predictions. Our proposed models outperform several baseline architectures like VGG16 and InceptionV3, achieving high accuracy with significantly fewer parameters. These results demonstrate that well-optimized lightweight networks can deliver accurate and interpretable brain tumor classification.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562546","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":"Photon-Counting Detector CT Allows Abdominal Virtual Monoenergetic Imaging at Lower Kiloelectron Volt Level with Lower Noise Using Lower Radiation Dose: A Prospective Matched Study Compared to Energy-Integrating Detector CT.","authors":"Huan Zhang, Yangfan Hu, Lingyun Wang, Yue Xing, Zhihan Xu, Junjie Lu, Jiarui Yang, Bei Ding, Fei Yuan, Jingyu Zhong","doi":"10.1007/s10278-025-01593-5","DOIUrl":"https://doi.org/10.1007/s10278-025-01593-5","url":null,"abstract":"<p><p>Our study aimed to assess the image quality of lower kiloelectron volt (keV) level abdominal virtual monoenergetic imaging (VMI) with lower radiation dose on photon-counting detector computed tomography (PCD-CT), in comparison to energy-integrating detector computed tomography (EID-CT). We prospectively included three matched groups, each with 59 participants, to undergo contrast-enhanced abdominal CT scans using EID-CT with full-dose (EID_FD), PCD-CT with full-dose (PCD_FD), and PCD-CT with low-dose (PCD_LD) protocols, respectively. The data of portal-venous phase were reconstructed into VMI at 40, 50, 60, and 70 keV, respectively. The standard deviation of CT values in liver parenchyma was measured as image noise. The signal-to-noise ratio (SNR) of liver parenchyma and contrast-to-noise ratio (CNR) of liver-portal vein were calculated. Three radiologists assessed the image noise, vessel sharpness, and overall quality, and rated the hepatic lesion conspicuity if possible. Our study found that the PCD_LD significantly reduced the radiation dose than EID_FD or PCD_FD (p < 0.001). The noise was significantly decreased by PCD_FD and PCD_LD compared to EID_FD, but SNR values were significantly increased (p ≤ 0.006). The CNR values were significantly increased by PCD_FD and PCD_LD compared to EID_FD in VMI at 40 keV and 50 keV (p ≤ 0.010). The ratings of image noise, vessel sharpness, overall quality, and lesion conspicuity were significantly greater in PCD_FD and PCD_LD compared to EID_FD (p ≤ 0.001). There was no significant difference detected in rating of lesion conspicuity between PCD_FD and PCD_LD (p ≥ 0.259). In conclusion, PCD-CT allows abdominal VMI with lower keV and lower noise using lower radiation dose, to provide better visualization of the hepatic lesions.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546693","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":"Habitat-Derived Radiomic Features of Planning Target Volume to Determine the Local Recurrence After Radiotherapy in Patients with Gliomas: A Feasibility Study.","authors":"Yixin Wang, Lin Lin, Zongtao Hu, Hongzhi Wang","doi":"10.1007/s10278-025-01591-7","DOIUrl":"https://doi.org/10.1007/s10278-025-01591-7","url":null,"abstract":"<p><p>To develop a machine learning-based predictive model for local recurrence after radiotherapy in patients with gliomas, with interpretability enhanced through SHapley Additive exPlanations (SHAP). We retrospectively enrolled 145 patients with pathologically confirmed gliomas who underwent brain radiotherapy (training: validation = 102:43). Physiological and structural magnetic resonance imaging (MRI) were used to define habitat regions. A total of 2153 radiomic features were extracted from each MRI sequence in each habitat region, respectively. Relief and Recursive Feature Elimination were used for radiomic feature selection. Support vector machine (SVM) and random forest models incorporating clinical and radiomic features were constructed for each habitat region. The SHAP method was used to explain the predictive model. In the training cohort and validation cohort, the Physiological_Habitat1 (e-THRIVE)_radiomic SVM model demonstrated the best AUC of 0.703 (95% CI 0.569-0.836) and 0.670 (95% CI 0.623-0.717) compared to the other radiomic models. The SHAP summary plot and SHAP force plot were used to interpret the best-performing Physiological_Habitat1 (e-THRIVE)_radiomic SVM model. Radiomic features derived from the Physiological_Habitat1 (e-THRIVE) were predictive of local recurrence in glioma patients following radiotherapy. The SHAP method provided insights into how the tumor microenvironment might influence the effectiveness of radiotherapy in postoperative gliomas.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546692","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":"Efficient Cerebral Infarction Segmentation Using U-Net and U-Net3 + Models.","authors":"Esra Yuce, Muhammet Emin Sahin, Hasan Ulutas, Mustafa Fatih Erkoç","doi":"10.1007/s10278-025-01587-3","DOIUrl":"https://doi.org/10.1007/s10278-025-01587-3","url":null,"abstract":"<p><p>Cerebral infarction remains a leading cause of mortality and long-term disability globally, underscoring the critical importance of early diagnosis and timely intervention to enhance patient outcomes. This study introduces a novel approach to cerebral infarction segmentation using a novel dataset comprising MRI scans of 110 patients, retrospectively collected from Yozgat Bozok University Research Hospital. Two convolutional neural network architectures, the basic U-Net and the advanced U-Net3 + , are employed to segment infarction regions with high precision. Ground-truth annotations are generated under the supervision of an experienced radiologist, and data augmentation techniques are applied to address dataset limitations, resulting in 6732 balanced images for training, validation, and testing. Performance evaluation is conducted using metrics such as the dice score, Intersection over Union (IoU), pixel accuracy, and specificity. The basic U-Net achieved superior performance with a dice score of 0.8947, a mean IoU of 0.8798, a pixel accuracy of 0.9963, and a specificity of 0.9984, outperforming U-Net3 + despite its simpler architecture. U-Net3 + , with its complex structure and advanced features, delivered competitive results, highlighting the potential trade-off between model complexity and performance in medical imaging tasks. This study underscores the significance of leveraging deep learning for precise and efficient segmentation of cerebral infarction. The results demonstrate the capability of CNN-based architectures to support medical decision-making, offering a promising pathway for advancing stroke diagnosis and treatment planning.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532500","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":"Evaluation of Cone-Beam Computed Tomography Images with Artificial Intelligence.","authors":"Tugba Arı, Ibrahim Sevki Bayrakdar, Özer Çelik, Elif Bilgir, Alican Kuran, Kaan Orhan","doi":"10.1007/s10278-025-01595-3","DOIUrl":"https://doi.org/10.1007/s10278-025-01595-3","url":null,"abstract":"<p><p>This study aims to evaluate the success of artificial intelligence models developed using convolutional neural network-based algorithms on CBCT images. Labeling was done by segmentation method for 15 different conditions including caries, restorative filling material, root-canal filling material, dental implant, implant supported crown, crown, pontic, impacted tooth, supernumerary tooth, residual root, osteosclerotic area, periapical lesion, radiolucent jaw lesion, radiopaque jaw lesion, and mixed appearing jaw lesion on the data set consisting of 300 CBCT images. In model development, the Mask R-CNN architecture and ResNet 101 model were used as a transfer learning method. The success metrics of the model were calculated with the confusion matrix method. When the F1 scores of the developed models were evaluated, the most successful dental implant was found to be 1, and the lowest F1 score was found to be a mixed appearing jaw lesion. F1 scores were respectively dental implant, root canal filling material, implant supported crown, restorative filling material, radiopaque jaw lesion, crown, pontic, impacted tooth, caries, residual tooth root, radiolucent jaw lesion, osteosclerotic area, periapical lesion, supernumerary tooth, for mixed appearing jaw lesion; 1 is 0.99, 0.98, 0.98, 0.97, 0.96, 0.96, 0.95, 0.94, 0.94, 0.94, 0.90, 0.90, 0.87, and 0.8. Interpreting CBCT images is a time-consuming process and requires expertise. In the era of digital transformation, artificial intelligence-based systems that can automatically evaluate images and convert them into report format as a decision support mechanism will contribute to reducing the workload of physicians, thus increasing the time allocated to the interpretation of pathologies.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532512","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 CT-Based Delta-Radiomics: Predicting Lymphovascular and Perineural Invasion in Rectal Cancer Preoperatively.","authors":"Chunlong Fu, Zebin Yang, Kangfei Shan, Zhenzhu Pang, Chijun Ma, Jieping Xu, Weidhua Zhu, Yanqing Hu, Chaohui Huang, Jihong Sun, Long Zhou, Fenhua Zhao","doi":"10.1007/s10278-025-01574-8","DOIUrl":"https://doi.org/10.1007/s10278-025-01574-8","url":null,"abstract":"<p><p>To construct and validate a multi-phase contrast-enhanced computed tomography delta-radiomics signature for preoperatively predicting lymphovascular invasion (LVI) and perineural invasion (PNI) in patients with rectal cancer (RC). This study retrospectively enrolled 519 patients with RC between January 2017 and December 2022, with patients assigned to the training (n = 363) or validation (n = 156) sets. Radiomic features were extracted from routine scanning (A0), the arterial phase (A1), and the venous phase (A2). Delta-1 and Delta-2 radiomic signatures were derived by subtracting radiomic features acquired from A0 images from those of A2 and A1, respectively. Subsequently, Delta-3 and Delta-4 radiomic features were obtained by performing image subtraction between the A0 images and A2 and A1 images, then extracting the radiomic features from the resulting residual images. A delta-radiomics model was constructed using the Least Absolute Shrinkage and Selection Operator method. Model performance was evaluated using receiver operating characteristic, calibration, and decision curves. Delta-1-Delta-4 models exhibited moderate predictive performance for LVI and PNI in patients with RC, with area under the curve (AUC) values of 0.73, 0.73, 0.67, and 0.68, respectively. The combined model (C-Delta-12) showed the best predictive performance (AUC, 0.81; accuracy, 0.76; sensitivity, 0.86; specificity, 0.65). Calibration curves confirmed high goodness of fit, and decision curve analysis confirmed the clinical value. Integrating delta-radiomics signature and clinical predictors into a radiomics prediction model enables accurate and non-invasive risk assessments of PNI and LVI in RC. Stratifying patients based on their PNI and LVI status may facilitate more individualised treatment.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510194","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":"Harnessing Generative AI for Lung Nodule Spiculation Characterization.","authors":"Yiyang Wang, Charmi Patel, Roselyne Tchoua, Jacob Furst, Daniela Raicu","doi":"10.1007/s10278-025-01573-9","DOIUrl":"https://doi.org/10.1007/s10278-025-01573-9","url":null,"abstract":"<p><p>Spiculation, characterized by irregular, spike-like projections from nodule margins, serves as a crucial radiological biomarker for malignancy assessment and early cancer detection. These distinctive stellate patterns strongly correlate with tumor invasiveness and are vital for accurate diagnosis and treatment planning. Traditional computer-aided diagnosis (CAD) systems are limited in their capability to capture and use these patterns given their subtlety, difficulty in quantifying them, and small datasets available to learn these patterns. To address these challenges, we propose a novel framework leveraging variational autoencoders (VAE) to discover, extract, and vary disentangled latent representations of lung nodule images. By gradually varying the latent representations of non-spiculated nodule images, we generate augmented datasets containing spiculated nodule variations that, we hypothesize, can improve the diagnostic classification of lung nodules. Using the National Institutes of Health/National Cancer Institute Lung Image Database Consortium (LIDC) dataset, our results show that incorporating these spiculated image variations into the classification pipeline significantly improves spiculation detection performance up to 7.53%. Notably, this enhancement in spiculation detection is achieved while preserving the classification performance of non-spiculated cases. This approach effectively addresses class imbalance and enhances overall classification outcomes. The gradual attenuation of spiculation characteristics demonstrates our model's ability to both capture and generate clinically relevant semantic features in an algorithmic manner. These findings suggest that the integration of semantic-based latent representations into CAD models not only enhances diagnostic accuracy but also provides insights into the underlying morphological progression of spiculated nodules, enabling more informed and clinically meaningful AI-driven support systems.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510195","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":"A New Aortic Valve Calcium Scoring Framework for Automatic Calcification Detection in Echocardiography.","authors":"Mervenur Cakir, Elif Baykal Kablan, Murat Ekinci, Mursel Sahin","doi":"10.1007/s10278-025-01576-6","DOIUrl":"https://doi.org/10.1007/s10278-025-01576-6","url":null,"abstract":"<p><p>Aortic valve calcium scoring is an essential tool for diagnosing, treating, monitoring, and assessing the risk of aortic stenosis. The current gold standard for determining the aortic valve calcium score is computed tomography (CT). However, CT is costly and exposes patients to ionizing radiation, making it less ideal for frequent monitoring. Echocardiography, a safer and more affordable alternative that avoids radiation, is more widely accessible, but its variability between and within experts leads to subjective interpretations. Given these limitations, there is a clear need for an automated, objective method to measure the aortic valve calcium score from echocardiography, which could reduce costs and improve patient safety. In this paper, we first employ the YOLOv5 method to detect the region of interest in the aorta within echocardiography images. Building on this, we propose a novel approach that combines UNet and diffusion model architectures to segment calcified areas within the identified region, forming the foundation for automated aortic valve calcium scoring. This architecture leverages UNet's localization capabilities and the diffusion model's strengths in capturing fine-grained structures, enhancing both segmentation accuracy and consistency. The proposed method achieves 85.08% precision, 80.01% recall, and 71.13% Dice score on a novel dataset comprising 160 echocardiography images from 86 distinct patients. This system enables cardiologists to focus more on critical aspects of diagnosis by providing a faster, more objective, and cost-effective method for aortic valve calcium scoring and eliminating the risk of radiation exposure.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500036","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}