Journal of imaging informatics in medicine最新文献_第10页

Multimodal Masked Autoencoder Based on Adaptive Masking for Vitiligo Stage Classification. 基于自适应掩模的多模态掩模自编码器用于白癜风分期分类。

Journal of imaging informatics in medicine Pub Date : 2025-04-29 DOI: 10.1007/s10278-025-01521-7

Fan Xiang, Zhiming Li, Shuying Jiang, Chunying Li, Shuli Li, Tianwen Gao, Kaiqiao He, Jianru Chen, Junpeng Zhang, Junran Zhang

{"title":"Multimodal Masked Autoencoder Based on Adaptive Masking for Vitiligo Stage Classification.","authors":"Fan Xiang, Zhiming Li, Shuying Jiang, Chunying Li, Shuli Li, Tianwen Gao, Kaiqiao He, Jianru Chen, Junpeng Zhang, Junran Zhang","doi":"10.1007/s10278-025-01521-7","DOIUrl":"https://doi.org/10.1007/s10278-025-01521-7","url":null,"abstract":"Vitiligo, a prevalent skin condition characterized by depigmentation, presents challenges in staging due to its inherent complexity. Multimodal skin images can provide complementary information, and in this study, the integration of clinical images of vitiligo and those obtained under Wood's lamp is conducive to the classification of vitiligo stages. However, difficulties in annotating multimodal data and the scarcity of multimodal data limit the performance of deep learning models in related classification tasks. To address these issues, a Multimodal Masked Autoencoder (Multi-MAE) based on adaptive masking is proposed in annotating multimodal data and the problem of multimodal data scarcity, and enhances the model's ability to extract characteristics from multimodal data. Specifically, an image reconstruction task is constructed to diminish reliance on annotated multimodal data, and a pre-training strategy is employed to alleviate the scarcity of multimodal data. Experimental results demonstrate that the proposed model achieves a vitiligo stage classification accuracy of 95.48% on a dataset of unlabeled dermatological images, an improvement of 5.16%, 4.51%, 3.87%, 2.58%, 4.51%, 4.51%, 3.87%, and 2.58% over that of MobileNet, DenseNet, VGG, ResNet-50, BEIT, MaskFeat, SimMIM, and MAE, respectively. These results verify the effectiveness of the proposed Multi-MAE model in assessing the stable and active vitiligo stages, making it a suitable clinical aid for evaluating the severity of vitiligo lesions.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144035423","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}

引用次数: 0

Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures. 利用集成深度学习架构推进肺栓塞检测。

Journal of imaging informatics in medicine Pub Date : 2025-04-25 DOI: 10.1007/s10278-025-01506-6

Can Berk Biret, Sukru Gurbuz, Erhan Akbal, Mehmet Baygin, Evren Ekingen, Serdar Derya, I Okan Yıldırım, Ilknur Sercek, Sengul Dogan, Turker Tuncer

{"title":"Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.","authors":"Can Berk Biret, Sukru Gurbuz, Erhan Akbal, Mehmet Baygin, Evren Ekingen, Serdar Derya, I Okan Yıldırım, Ilknur Sercek, Sengul Dogan, Turker Tuncer","doi":"10.1007/s10278-025-01506-6","DOIUrl":"https://doi.org/10.1007/s10278-025-01506-6","url":null,"abstract":"The main aim of this study is to introduce a new hybrid deep learning model for biomedical image classification. We propose a novel convolutional neural network (CNN), named HybridNeXt, for detecting pulmonary embolism (PE) from computed tomography (CT) images. To evaluate the HybridNeXt model, we created a new dataset consisting of two classes: (1) PE and (2) control. The HybridNeXt architecture combines different advanced CNN blocks, including MobileNet, ResNet, ConvNeXt, and Swin Transformer. We specifically designed this model to combine the strengths of these well-known CNNs. The architecture also includes stem, downsampling, and output stages. By adjusting the parameters, we developed a lightweight version of HybridNeXt, suitable for clinical use. To further improve the classification performance and demonstrate transfer learning capability, we proposed a deep feature engineering (DFE) method using a multilevel discrete wavelet transform (MDWT). This DFE model has three main phases: (i) feature extraction from raw images and wavelet bands, (ii) feature selection using iterative neighborhood component analysis (INCA), and (iii) classification using a k-nearest neighbors (kNN) classifier. We first trained HybridNeXt on the training images, creating a pretrained HybridNeXt model. Then, using this pretrained model, we extracted features and applied the proposed DFE method for classification. The HybridNeXt model achieved a test accuracy of 90.14%, while our DFE model improved accuracy to 96.35%. Overall, the results confirm that our HybridNeXt architecture is highly accurate and effective for biomedical image classification. The presented HybridNeXt and HybridNeXt-based DFE methods can potentially be applied to other image classification tasks.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065465","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}

引用次数: 0

Gaussian Function Model for Task-Specific Evaluation in Medical Imaging: A Theoretical Investigation. 高斯函数模型在医学影像任务特定评估：理论研究。

Journal of imaging informatics in medicine Pub Date : 2025-04-24 DOI: 10.1007/s10278-025-01511-9

Sho Maruyama

{"title":"Gaussian Function Model for Task-Specific Evaluation in Medical Imaging: A Theoretical Investigation.","authors":"Sho Maruyama","doi":"10.1007/s10278-025-01511-9","DOIUrl":"https://doi.org/10.1007/s10278-025-01511-9","url":null,"abstract":"In medical image diagnosis, understanding image characteristics is crucial for selecting and optimizing imaging systems and advancing their development. Objective image quality assessments, based on specific diagnostic tasks, have become a standard in medical image analysis, bridging the gap between experimental observations and clinical applications. However, conventional task-based assessments often rely on ideal observer models that assume target signals have circular shapes with well-defined edges. This simplification rarely reflects the true complexity of lesion morphology, where edges exhibit variability. This study proposes a more practical approach by employing a Gaussian distribution to represent target signal shapes. This study explicitly derives the task function for Gaussian signals and evaluates the detectability index through simulations based on head computed tomography (CT) images with low-contrast lesions. Detectability indices were calculated for both circular and Gaussian signals using non-prewhitening and Hotelling observer models. The results demonstrate that Gaussian signals consistently exhibit lower detectability indices compared to circular signals, with differences becoming more pronounced for larger signal sizes. Simulated images closely resembling actual CT images confirm the validity of these calculations. These findings quantitatively clarify the influence of signal shape on detection performance, highlighting the limitations of conventional circular models. Thus, it provides a theoretical framework for task-based assessments in medical imaging, offering improved accuracy and clinical relevance for future advancements in the field.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047620","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}

引用次数: 0

Correction: Deep Learning-Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors. 修正：基于深度学习的x射线位置估计，自动设置x射线质因数。

Journal of imaging informatics in medicine Pub Date : 2025-04-24 DOI: 10.1007/s10278-025-01476-9

C F Del Cerro, R C Gimenez, J Garcia-Blas, K Sosenko, J M Ortega, M Desco, M Abella

引用次数: 0

Automated Neonatal Hip Ultrasound System for Diagnosing Developmental Dysplasia of Hips Using Assistive AI. 使用辅助人工智能诊断髋关节发育不良的自动新生儿髋关节超声系统。

Journal of imaging informatics in medicine Pub Date : 2025-04-23 DOI: 10.1007/s10278-025-01498-3

Young Seop Lee, Young Jae Kim, Jeong Won Ryu, Su Yeol Lee, Kwang Gi Kim

{"title":"Automated Neonatal Hip Ultrasound System for Diagnosing Developmental Dysplasia of Hips Using Assistive AI.","authors":"Young Seop Lee, Young Jae Kim, Jeong Won Ryu, Su Yeol Lee, Kwang Gi Kim","doi":"10.1007/s10278-025-01498-3","DOIUrl":"https://doi.org/10.1007/s10278-025-01498-3","url":null,"abstract":"This study aims to develop and evaluate an artificial intelligence (AI)-based diagnostic system for the diagnosis of developmental dysplasia of the hip (DDH) in infant hip ultrasonography. The Graf algorithm was employed to develop an automated model for diagnosing DDH, resulting in a DDH-assisted AI model with an average Graf angle error rate of 0.21 compared to expert diagnostics. NASNetMobile achieved the highest Area Under the Curve (AUC) of 0.864 (95% CI, 0.850-0.878), closely followed by MobileNetV1, DenseNet121, EfficientNetV2B0, NASNetMobile, and ResNet50. UnestedUNet demonstrated the highest overall performance, achieving Dice coefficients of 0.794 and a runtime of 40.078 ms, demonstrating its strong segmentation accuracy with moderate computational demands. DeepLabV3Plus, a handheld ultrasound device integrated with a smartphone, demonstrated a robust and efficient segmentation performance. This study highlights the transformative potential of integrating AI into portable ultrasound devices, enabling accurate, efficient, and accessible diagnostic solutions.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030947","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}

引用次数: 0

Improving Radiology Report Conciseness and Structure via Local Large Language Models. 利用局部大语言模型改进放射学报告的简洁性和结构。

Journal of imaging informatics in medicine Pub Date : 2025-04-21 DOI: 10.1007/s10278-025-01510-w

Iryna Hartsock, Cyrillo Araujo, Les Folio, Ghulam Rasool

{"title":"Improving Radiology Report Conciseness and Structure via Local Large Language Models.","authors":"Iryna Hartsock, Cyrillo Araujo, Les Folio, Ghulam Rasool","doi":"10.1007/s10278-025-01510-w","DOIUrl":"https://doi.org/10.1007/s10278-025-01510-w","url":null,"abstract":"Radiology reports are often lengthy and unstructured, posing challenges for referring physicians to quickly identify critical imaging findings while increasing risk of missed information. This retrospective study aimed to enhance radiology reports by making them concise and well-structured, with findings organized by relevant organs. To achieve this, we utilized private large language models (LLMs) deployed locally within our institution's firewall, ensuring data security and minimizing computational costs. Using a dataset of 814 radiology reports from seven board-certified body radiologists at [-blinded for review-], we tested five prompting strategies within the LangChain framework. After evaluating several models, the Mixtral LLM demonstrated superior adherence to formatting requirements compared to alternatives like Llama. The optimal strategy involved condensing reports first and then applying structured formatting based on specific instructions, reducing verbosity while improving clarity. Across all radiologists and reports, the Mixtral LLM reduced redundant word counts by more than 53%. These findings highlight the potential of locally deployed, open-source LLMs to streamline radiology reporting. By generating concise, well-structured reports, these models enhance information retrieval and better meet the needs of referring physicians, ultimately improving clinical workflows.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059558","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}

引用次数: 0

Diagnosing Severe Low-Gradient vs Moderate Aortic Stenosis with Artificial Intelligence Based on Echocardiography Images. 基于超声心动图的人工智能诊断重度低梯度主动脉瓣狭窄与中度主动脉瓣狭窄。

Journal of imaging informatics in medicine Pub Date : 2025-04-21 DOI: 10.1007/s10278-025-01497-4

Michał Wrzosek, Mikolaj Buchwald, Patryk Czernik, Szymon Kupinski, Karina Zatorska, Anna Jasińska, Dariusz Zakrzewski, Juliusz Pukacki, Cezary Mazurek, Robert Pękal, Tomasz Hryniewiecki

引用次数: 0

A Hybrid Model-Based Clinicopathological Features and Radiomics Based on Conventional MRI for Predicting Lymph Node Metastasis and DFS in Cervical Cancer. 基于临床病理特征和基于常规MRI放射组学的混合模型预测宫颈癌淋巴结转移和DFS

Journal of imaging informatics in medicine Pub Date : 2025-04-18 DOI: 10.1007/s10278-024-01371-9

Mingke Tian, Fengying Qin, Xinyan Sun, Huiting Pang, Tao Yu, Yue Dong

{"title":"A Hybrid Model-Based Clinicopathological Features and Radiomics Based on Conventional MRI for Predicting Lymph Node Metastasis and DFS in Cervical Cancer.","authors":"Mingke Tian, Fengying Qin, Xinyan Sun, Huiting Pang, Tao Yu, Yue Dong","doi":"10.1007/s10278-024-01371-9","DOIUrl":"https://doi.org/10.1007/s10278-024-01371-9","url":null,"abstract":"This study aimed to improve the accuracy of the diagnosis of lymph node metastasis (LNM) and prediction of patient prognosis in cervical cancer patients using a hybrid model based on MRI and clinical aspects. We retrospectively analyzed routine MR data from 485 patients with pathologically confirmed cervical cancer from January 2014 to June 2021. The data were divided into a training cohort (N = 261), internal cohort (N = 113), and external validation cohort (n = 111). A total of 2194 features were extracted from each ROI from T2WI and CE-T1WI. The clinical model (M1) was built with clinicopathological features including squamous cell carcinoma antigen, MRI-reported LNM, maximal tumor diameter (MTD). The radiomics model (M2) was built with four radiomics features. The hybrid model (M3) was constructed with squamous cell carcinoma antigen, MRI-reported LNM, MTD which consists of M1 and four radiomics features which consist of M2. GBDT algorithms were used to create the scores of M1 (clinical-score, C-score), M2 (radiomic score, R-score), and M3 (hybrid-score, H-score). M3 showed good performance in the training cohort (AUCs, M3 vs. M1 vs. M2, 0.917 vs. 0.830 vs. 0.788), internal validation cohorts (AUCs, M3 vs. M1 vs. M2, 0.872 vs. 0.750 vs. 0.739), and external validation cohort (AUCs, M3 vs. M1 vs. M2, 0.907 vs. 0.811 vs. 0.785). In addition, higher scores were significantly associated with worse disease-free survival (DFS) in the training cohort and the internal validation cohort (C-score, P = 0.001; R-score, P = 0.002; H-score, P = 0.006). Radiomics models can accurately predict LNM status in patients with cervical cancer. The hybrid model, which incorporates clinical and radiomics features, is a novel way to enhance diagnostic performance and predict the prognosis of cervical cancer.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045593","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}

引用次数: 0

Revisiting Anatomy of Anorectal Malformations with a Symbolic AI Segmentation Method Applied on Diffusion MRI: The Lumbosacral Plexus Development and Microarchitecture Is Different in High and Low Types. 弥散MRI符号AI分割法重述肛肠畸形解剖：腰骶神经丛发育与微结构高低型不同

Journal of imaging informatics in medicine Pub Date : 2025-04-18 DOI: 10.1007/s10278-024-01378-2

J Goulin, G La Barbera, A Delmonte, E Bonnot, L Berteloot, C Lozach, S Beaudoin, T Blanc, C Cretolle, C O Muller, P Meignan, Q Peyrot, E Mille, J B Marret, M Zerah, N Boddaert, P Gori, I Bloch, S Sarnacki

{"title":"Revisiting Anatomy of Anorectal Malformations with a Symbolic AI Segmentation Method Applied on Diffusion MRI: The Lumbosacral Plexus Development and Microarchitecture Is Different in High and Low Types.","authors":"J Goulin, G La Barbera, A Delmonte, E Bonnot, L Berteloot, C Lozach, S Beaudoin, T Blanc, C Cretolle, C O Muller, P Meignan, Q Peyrot, E Mille, J B Marret, M Zerah, N Boddaert, P Gori, I Bloch, S Sarnacki","doi":"10.1007/s10278-024-01378-2","DOIUrl":"https://doi.org/10.1007/s10278-024-01378-2","url":null,"abstract":"Anorectal malformations (ARMs) are congenital anomalies of the distal part of the hindgut often associated with sacral and/or spinal anomalies. We investigated anatomical and microstructural properties of the lumbosacral plexus of ARM patients from imaging data. Twenty-five patients (16 males), median age 4 months (2-49), 13 high and 12 low ARM, underwent 3 Tesla magnetic resonance imaging with diffusion tensor sequences (dMRI) before repair. A 3D model was built from manual segmentation and used to guide novel AI algorithms for the segmentation of the nervous pelvic network. Volume and diffusion parameters were obtained for each root (L5 to S4) and compared among patients with high and low ARMs using a nonparametric Wilcoxon test. Comparison was also made between the groups with (n = 9) or without (n = 16) sacral and/or spinal cord anomalies. When compared with low ARMS, high ARMs exhibited the following: a smaller volume of S1, S2, and S3 roots and of S1 and S3 for patients without sacral and/or spinal cord abnormalities; an overall significant alteration of the roots micro-architecture reflected by a diminution of the fractional anisotropy and an increase of the axial diffusivity and radial diffusivity measures. This first analysis of the lumbosacral plexus from dMRI in children with ARMs shows differences in the development and microarchitecture of the lumbosacral nerve roots between high and low ARMs. This observation supports the hypothesis that high ARMs may result from a more regional developmental abnormality than low ARMs and open new ways to visualize and assess the lumbosacral plexus in children and adults.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059134","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}

引用次数: 0

Optimized Feature Selection and Deep Neural Networks to Improve Heart Disease Prediction. 优化特征选择和深度神经网络改善心脏病预测。

Journal of imaging informatics in medicine Pub Date : 2025-04-16 DOI: 10.1007/s10278-025-01435-4

Changming Tan, Zhaoshun Yuan, Feng Xu, Dang Xie

{"title":"Optimized Feature Selection and Deep Neural Networks to Improve Heart Disease Prediction.","authors":"Changming Tan, Zhaoshun Yuan, Feng Xu, Dang Xie","doi":"10.1007/s10278-025-01435-4","DOIUrl":"https://doi.org/10.1007/s10278-025-01435-4","url":null,"abstract":"Heart disease remains a significant health threat due to its high mortality rate and increasing prevalence. Early prediction using basic physical markers from routine exams is crucial for timely diagnosis and intervention. However, manual analysis of large datasets can be labor-intensive and error-prone. Our goal is to rapidly and reliably anticipate cardiac disease using a variety of body signs. This research presents a unique model for heart disease prediction. We provide a system for predicting cardiac disease that blends the deep convolutional neural network with a feature selection technique based on the LinearSVC. This integrated feature selection method selects a subset of characteristics that are strongly linked with heart disease. We feed these features into the deep conventual neural network that we constructed. Also to improve the speed of the predictor and avoid gradient varnishing or explosion, the network's hyperparameters were tuned using the random search algorithm. The proposed method was evaluated using the UCI and MIT datasets. The predictor is evaluated using a number of indicators, such as accuracy, recall, precision, and F1 score. The results demonstrate that our model attains accuracy rates of 98.16%, 98.2%, 95.38%, and 97.84% in the UCI dataset, with an average MCC score of 90%. These results affirm the efficacy and reliability of the proposed technique to predict heart disease.","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065402","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}

引用次数: 0