Journal of imaging informatics in medicine最新文献

Insufficient clinical information provided in radiology requests, coupled with the cumbersome nature of electronic health records (EHRs), poses significant challenges for radiologists in extracting pertinent clinical data and compiling detailed radiology reports. Considering the challenges and time involved in navigating electronic medical records (EMR), an automated method to accurately compress the text while maintaining key semantic information could significantly enhance the efficiency of radiologists' workflow. The purpose of this study is to develop and demonstrate an automated tool for clinical note summarization with the goal of extracting the most pertinent clinical information for the radiological assessments. We adopted a transfer learning methodology from the natural language processing domain to fine-tune a transformer model for abstracting clinical reports. We employed a dataset consisting of 1000 clinical notes from 970 patients who underwent knee MRI, all manually summarized by radiologists. The fine-tuning process involved a two-stage approach starting with self-supervised denoising and then focusing on the summarization task. The model successfully condensed clinical notes by 97% while aligning closely with radiologist-written summaries evidenced by a 0.9 cosine similarity and a ROUGE-1 score of 40.18. In addition, statistical analysis, indicated by a Fleiss kappa score of 0.32, demonstrated fair agreement among specialists on the model's effectiveness in producing more relevant clinical histories compared to those included in the exam requests. The proposed model effectively summarized clinical notes for knee MRI studies, thereby demonstrating potential for improving radiology reporting efficiency and accuracy.

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.

Breast cancer has remained one of the most frequent and life-threatening cancers in females globally, putting emphasis on better diagnostics in its early stages to solve the problem of therapy effectiveness and survival. This work enhances the assessment of breast cancer by employing progressive residual networks (PRN) and ResNet-50 within the framework of Progressive Residual Multi-Class Support Vector Machine-Net. Built on concepts of deep learning, this creative integration optimizes feature extraction and raises the bar for classification effectiveness, earning an almost perfect 99.63% on our tests. These findings indicate that PRMS-Net can serve as an efficient and reliable diagnostic tool for early breast cancer detection, aiding radiologists in improving diagnostic accuracy and reducing false positives. The separation of the data into different segments is possible to determine the architecture's reliability using the fivefold cross-validation approach. The total variability of precision, recall, and F1 scores clearly depicted in the box plot also endorse the competency of the model for marking proper sensitivity and specificity-highly required for combating false positive and false negative cases in real clinical practice. The evaluation of error distribution strengthens the model's rationale by giving validation of practical application in medical contexts of image processing. The high levels of feature extraction sensitivity together with highly sophisticated classification methods make PRMS-Net a powerful tool that can be used in improving the early detection of breast cancer and subsequent patient prognosis.

Effective radiotherapy planning requires precise delineation of organs at risk (OARs), but the traditional manual method is laborious and subject to variability. This study explores using convolutional neural networks (CNNs) for automating OAR segmentation in pelvic CT images, focusing on the bladder, prostate, rectum, and femoral heads (FHs) as an efficient alternative to manual segmentation. Utilizing the Medical Open Network for AI (MONAI) framework, we implemented and compared U-Net, ResU-Net, SegResNet, and Attention U-Net models and explored different loss functions to enhance segmentation accuracy. Our study involved 240 patients for prostate segmentation and 220 patients for the other organs. The models' performance was evaluated using metrics such as the Dice similarity coefficient (DSC), Jaccard index (JI), and the 95th percentile Hausdorff distance (95thHD), benchmarking the results against expert segmentation masks. SegResNet outperformed all models, achieving DSC values of 0.951 for the bladder, 0.829 for the prostate, 0.860 for the rectum, 0.979 for the left FH, and 0.985 for the right FH (p < 0.05 vs. U-Net and ResU-Net). Attention U-Net also excelled, particularly for bladder and rectum segmentation. Experiments with loss functions on SegResNet showed that Dice loss consistently delivered optimal or equivalent performance across OARs, while DiceCE slightly enhanced prostate segmentation (DSC = 0.845, p = 0.0138). These results indicate that advanced CNNs, especially SegResNet, paired with optimized loss functions, provide a reliable, efficient alternative to manual methods, promising improved precision in radiotherapy planning.

The number of Mycobacterium avium-intracellulare complex pulmonary disease patients is increasing globally. Distinguishing Mycobacterium avium-intracellulare complex pulmonary disease from pulmonary tuberculosis is difficult due to similar manifestations and characteristics. We aimed to build and validate a machine learning model using clinical data and computed tomography features to differentiate them. This multi-centered, retrospective study included 169 patients diagnosed with Mycobacterium avium-intracellulare complex and pulmonary tuberculosis from date to date. Data were analyzed, and logistic regression, random forest, and support vector machine models were established and validated. Performance was evaluated using receiver operating characteristic and precision-recall curves. In total, 84 patients with Mycobacterium avium-intracellulare complex pulmonary disease and 85 with pulmonary tuberculosis were analyzed. Patients with Mycobacterium avium-intracellulare complex pulmonary disease were older. Hemoptysis rate, cavity number and morphology, bronchiectasis type, and distribution differed. The support vector machine model performed better. In the training set, the area under the curve was 0.960, and in the validation set it was 0.885. The precision-recall curve showed high accuracy and low recall for the support vector machine model. The support vector machine learning-based model, which integrates clinical data and computed tomography imaging features, exhibited excellent diagnostic performance and can assist in differentiating Mycobacterium avium-intracellulare complex pulmonary disease from pulmonary tuberculosis.

Accurately diagnosing various types of breast lesions is critical for assessing breast cancer risk and predicting patient outcomes, which necessitates a fine-grained classification approach. While convolutional neural networks (CNNs) are predominantly employed in fine-grained classification tasks for breast lesions, they often struggle to effectively capture and model the intricate relationships between local and global features, an aspect that is vital for achieving high classification accuracy. Additionally, Color Doppler Flow Imaging (CDFI) and Strain Elastography (SE) are two important ultrasound imaging techniques widely used in the diagnosis of breast lesions. However, their specific contributions to fine-grained classification have not been thoroughly investigated. In this paper, we introduce a Triple Morphological Feature Attention Network (TMAN) designed to enhance fine-grained classification of breast ultrasound images. The TMAN architecture comprises three key modules: Local Margin Attention (LMA), Structured Texture Attention (STA), and Fusion Attention (FA), each focused on extracting distinct morphological features. TMAN achieved an average accuracy of 74.40%, precision of 73.18%, and specificity of 96.02%, surpassing state-of-the-art methods. The findings reveal that incorporating CDFI significantly improved classification for malignant subtypes with a 10% accuracy boost, while SE had a negligible impact. These findings highlight the effectiveness of TMAN in extracting nuanced morphological features and advancing precision in breast ultrasound diagnosis. The source code is accessible at https://github.com/windywindyw/TMAN .

Echocardiogram analysis plays a crucial role in assessing and diagnosing cardiac function, providing essential data to support medical diagnoses of heart disease. A key task, accurately identifying and segmenting the left ventricle (LV) in echocardiograms, remains challenging and labor-intensive. Current automated cardiac segmentation methods often lack the necessary accuracy and reproducibility, while semi-automated or manual annotations are excessively time-consuming. To address these limitations, we propose a novel segmentation framework, semi-and self-supervised learning with dual attention (SSL-DA) for echocardiogram segmentation. We start with a temporal masking network for pre-training. This network captures valuable information, such as echocardiogram periodicity. It also provides optimized initialization parameters for LV segmentation. We then employ a semi-supervised network to automatically segment the left ventricle, enhancing the model's learning with channel and spatial attention mechanisms to capture global channel dependencies and spatial dependencies across annotations. We evaluated SSL-DA on the publicly available EchoNet-Dynamic dataset, achieving a Dice similarity coefficient of 93.34% (95% CI, 93.23-93.46%), outperforming most prior CNN-based models. To further assess the generalization ability of SSL-DA, we conducted ablation experiments on the CAMUS dataset. Experimental results confirm that SSL-DA can quickly and accurately segment the left ventricle in echocardiograms, showing its potential for robust clinical application.

Dual-energy subtraction (DES) chest X-ray images (CXRs) are often affected by motion artifacts resulting from patients' voluntary or involuntary movements, even in clinical settings. Additionally, the mediastinum and upper abdominal regions in low-energy (LE) CXRs are susceptible to signal insufficiency due to inadequate input photon numbers. Current image processing techniques for removing motion artifacts and statistical noise from DES-CXRs are insufficient, and potential algorithms for these tasks remain largely unexplored. We propose a framework based on paired cycle-consistency adversarial generative networks to effectively remove motion artifacts and statistical noise from DES-CXRs. The proposed method incorporates ensemble discriminators, differentiable augmentation, anti-aliased convolution layers, and a basic 8-layer U-Net generator. This method was trained and tested using a clinical image dataset comprising data of 600 examinations of individuals who underwent dual-energy chest X-ray imaging for diagnostic purposes, using a sixfold cross-validation approach. It demonstrated a remarkable improvement in motion artifact suppression in terms of an analysis of full width at the 10-percent maximum improved from 0.216 ± 0.0720 to 0.200 ± 0.0783 for the left lung region of interests including the cardiac region. Furthermore, it outperformed the method in a previous study in terms of a peak signal-to-noise ratio of 50.7 ± 3.68, structural similarity index of 0.997 ± 0.0152 for LE images, and Fréchet inception distance of 85.0 ± 3.52 for bone-suppressed DES images. The proposed method significantly outperforms existing techniques for removing motion artifacts and statistical noise and shows strong potential for clinical applications in chest X-ray imaging.

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.

Ovarian cancer remains one of the most challenging cancers to diagnose due to its non-specific symptoms, lack of reliable screening tests, and the complexity of detecting abnormalities. Accurate subtype classification is crucial for personalised treatment and improved patient outcomes. In this study, we developed a machine learning pipeline fine-tuning pre-trained computer vision models to classify ovarian cancer subtypes from whole slide images (WSI). Using targeted tissue masks for necrosis, stroma, and tumour regions as a proof of concept, we demonstrated the efficacy of tiling masked regions to transform a complex detection-then-classification problem into a simpler classification task. Our method achieved high accuracy in tile-level classification, with a subsequent extension to subtype classification via majority voting on tiled images. Precision exceeds 90% across subtypes, which highlights the potential of scalable, automated systems to assist in ovarian cancer diagnostics. These findings contribute to the broader field of computational pathology, paving the way for enhanced diagnostic consistency and accessibility in clinical settings.