搜索结果：Heart, lung & circulation

Due to silence in early stages, lung cancer has been one of the most leading causes of mortality in cancer patients world-wide. Moreover, major symptoms of lung cancer are hard to differentiate with other respiratory disease symptoms such as COPD, further leading patients to overlook cancer progression in early stages. Thus, to enhance survival rates in lung cancer, early detection from consistent proactive respiratory system monitoring becomes crucial. One of the most prevalent and effective methods for lung cancer monitoring would be low-dose computed tomography(LDCT) chest scans, which led to remarkable enhancements in lung cancer detection or tumor classification tasks under rapid advancements and applications of computer vision based AI models such as EfficientNet or ResNet in image processing. However, though advanced CNN models under transfer learning or ViT based models led to high performing lung cancer detections, due to its intrinsic limitations in terms of correlation dependence and low interpretability due to complexity, expansions of deep learning models to lung cancer treatment analysis or causal intervention analysis simulations are still limited. Therefore, this re

Carbon-ion radiotherapy provides high dose conformity for lung cancer, but its benefit is limited by two sources of uncertainties: interplay between scanned beam delivery and tumor motion, and dose modulation from heterogeneous lung tissue. This study quantifies the separate and combined dosimetric impact of these effects using the GSI TRiP4D treatment planning system. Eighteen lung cancer 4DCT datasets from TCIA were analyzed. A modulation power ($P_{\mathrm{mod}}$) was assigned to lung voxels. Three values were sampled from a Gaussian distribution ($200μ\mathrm{m} \pm 67μ\mathrm{m}$), and an extreme value of $750μ\mathrm{m}$ was tested. Interplay doses were computed by combining scanned-beam delivery with patient-specific respiratory motion. Four scenarios were studied: static, static with modulation, interplay, and interplay with modulation. Metrics included $D95\%$, $V95\%$, homogeneity index (HI), lung $V16\mathrm{Gy}$, and heart $V20\mathrm{Gy}$. Interplay reduced target coverage by $5.2 \pm 1.5$ pp ($D95\%$), $12.1 \pm 5.9$ pp ($V95\%$), and $8.3 \pm 2.4$ pp (HI). Extreme $P_{\mathrm{mod}}$ alone caused small degradations. When combined with interplay, it partially compensat

Hospital readmission within 30 days of discharge is a leading driver of morbidity, mortality, and avoidable healthcare expenditure in congestive heart failure (CHF). Current clinical risk stratification tools rely primarily on non-imaging data and exhibit limited predictive performance. Point-of-care lung ultrasound (LUS) offers a sensitive, noninvasive window into the pulmonary congestion that characterizes CHF decompensation, yet its prognostic utility for readmission prediction remains largely unexplored. We present a pilot feasibility study, the first systematic machine learning study using B-mode LUS acquired during hospitalization to predict 30-day CHF readmission. Quantitative spatiotemporal embeddings are extracted from a pretrained Temporal Shift Module (TSM) ResNet-18 encoder, and interpretable biomarker features are separately evaluated. Through structured ablations over lung view, temporal representation, multi-view fusion, and cross-lung augmentation, we identify the key imaging factors driving readmission risk. Our findings reveal that (1) dependent lower-lung regions (Left-3, Right-3) carry the strongest prognostic signal, consistent with their greater susceptibility

Lung mask creation lacks well-defined criteria and standardized guidelines, leading to a high degree of subjectivity between annotators. In this study, we assess the underestimation of lung regions on chest X-ray segmentation masks created according to the current state-of-the-art method, by comparison with total lung volume evaluated on computed tomography (CT). We show, that lung X-ray masks created by following the contours of the heart, mediastinum, and diaphragm significantly underestimate lung regions and exclude substantial portions of the lungs from further assessment, which may result in numerous clinical errors.

This study evaluates publicly available deep-learning based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels, pathology categories, and lung sides, and to identify limitations impacting their use in preoperative planning in lung transplantation. This retrospective study included 32 patients who underwent chest CT scans at Duke University Health System between 2017 and 2019 (total of 3,645 2D axial slices). Patients with standard axial CT scans were selected based on the presence of two or more lung pathologies of varying severity. Lung segmentation was performed using three previously developed deep learning models: Unet-R231, TotalSegmentator, MedSAM. Performance was assessed using quantitative metrics (volumetric similarity, Dice similarity coefficient, Hausdorff distance) and a qualitative measure (four-point clinical acceptability scale). Unet-R231 consistently outperformed TotalSegmentator and MedSAM in general, for different severity levels, and pathology categories (p<0.05). All models showed significant performance declines from mild to moderate-to-severe cases, particularly in volumetric similarity (p

Lung cancer remains among the deadliest types of cancer in recent decades, and early lung nodule detection is crucial for improving patient outcomes. The limited availability of annotated medical imaging data remains a bottleneck in developing accurate computer-aided diagnosis (CAD) systems. Self-supervised learning can help leverage large amounts of unlabeled data to develop more robust CAD systems. With the recent advent of transformer-based architecture and their ability to generalize to unseen tasks, there has been an effort within the healthcare community to adapt them to various medical downstream tasks. Thus, we propose a novel "LungNodule-SSM" method, which utilizes selfsupervised learning with DINOv2 as a backbone to enhance lung nodule detection and classification without annotated data. Our methodology has two stages: firstly, the DINOv2 model is pre-trained on unlabeled CT scans to learn robust feature representations, then secondly, these features are fine-tuned using transformer-based architectures for lesionlevel detection and accurate lung nodule diagnosis. The proposed method has been evaluated on the challenging LUNA 16 dataset, consisting of 888 CT scans, and com

In recent years, advancements in deep learning techniques have considerably enhanced the efficiency and accuracy of medical diagnostics. In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of lung sounds and lung diseases is proposed. Our proposed model leverages MTL with four different deep learning models such as 2D CNN, ResNet50, MobileNet and Densenet to extract relevant features from the lung sound recordings. The ICBHI 2017 Respiratory Sound Database was employed in the current study. The MTL for MobileNet model performed better than the other models considered, with an accuracy of74\% for lung sound analysis and 91\% for lung diseases classification. Results of the experimentation demonstrate the efficacy of our approach in classifying both lung sounds and lung diseases concurrently. In this study,using the demographic data of the patients from the database, risk level computation for Chronic Obstructive Pulmonary Disease is also carried out. For this computation, three machine learning algorithms namely Logistic Regression, SVM and Random Forest classifierswere employed. Among these ML algorithms, the Random Forest classifier h

Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness. We benchmark five feature extractors (Curia, Curia-2, DINOv3, Radiomics2D, Radiomics3D), seven classification heads (TabPFN, TabICL, XGBoost, CatBoost, Random Forest, logistic regression, Ridge), and three segmentation regimes on five tasks: tumor volume and stage classification, 2-year survival prediction, histology classification, and age prediction. Models are trained on LUNG1 (n=338) and evaluated on an internal test set (n=84) and the external LUNG2 cohort (n=211), with worst-case cross-cohort performance as the primary metric. The dominant design factor is task-dependent: segmentation drives volume and stage classification, while classifier choice drives survival, histology, and age prediction. Radiomics is competitive for tumor volume, tumor stage and survival (partly due to label-derivation effects for the former); Curia variants reach comparable peak scores for survival; DINOv3 falls slightly short across tasks. Patch and slice aggr

Purpose: Radiation-induced lymphopenia is an increasingly recognized toxicity in lung radiotherapy and has been linked to radiation exposure to circulating lymphocytes (CL). In intensity-modulated proton therapy (IMPT), prolonged pencil beam scanning (PBS) delivery may increase CL dose. We recently developed a patient-specific pin ridge filter (pRF) framework that enables ultra-fast proton delivery with a single beam energy. This study evaluated whether pRF-based lung stereotactic body radiotherapy (SBRT) plans delivered at conventional (pRFCONV) and FLASH dose rates (pRFFLASH) improve immune sparing using time-resolved blood dose accumulation and CL survival modeling. Methods: pRF plans were created for 10 lung SBRT patients previously treated with IMPT. PBS delivery simulations modeled spot delivery, scanning, and energy switching. Blood dose-volume histograms (bDVHs) were calculated with the hematological dose framework. CL survival fractions (SF) were estimated from bDVHs with saturation and linear-quadratic models derived from in-vitro survival data for CD4/CD8 CL. Results: Compared with IMPT, pRFCONV/pRFFLASH plans reduced delivery time (mean reductions: 85.3/99.9%) and irrad

Accurate quantification of lung disease severity from chest imaging is critical for clinical decision-making and resource allocation. We propose a tri-modal deep learning framework, TMF-RSE (Tri-Modal Fusion with Regional Semantics and Evidential Uncertainty), that combines appearance features from two-dimensional chest inputs, structural features from lung segmentation masks, and semantic features from vision-language models (VLMs) for severity quantification. Our approach employs complementary fusion mechanisms that integrate semantic guidance, structural priors, and hierarchical interactions across modalities. The model employs evidential regression to provide both severity predictions and uncertainty estimates. Experiments on the Per-COVID-19 CT and RALO datasets show that TMF-RSE outperforms recent transformer-based baselines, achieving MAE of 4.02 and Pearson correlation of 0.9629 on Per-COVID-19 validation, and 0.339 MAE / 0.973 PC on RALO geographic extent.

Lung cancer is an extremely lethal disease primarily due to its late-stage diagnosis and significant mortality rate, making it the major cause of cancer-related demises globally. Machine Learning (ML) and Convolution Neural network (CNN) based Deep Learning (DL) techniques are primarily used for precise segmentation and classification of cancerous nodules in the CT (Computed Tomography) or MRI images. This study introduces an innovative approach to lung nodule segmentation by utilizing the Segment Anything Model (SAM) combined with transfer learning techniques. Precise segmentation of lung nodules is crucial for the early detection of lung cancer. The proposed method leverages Bounding Box prompts and a vision transformer model to enhance segmentation performance, achieving high accuracy, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics. The integration of SAM and Transfer Learning significantly improves Computer-Aided Detection (CAD) systems in medical imaging, particularly for lung cancer diagnosis. The findings demonstrate the proposed model effectiveness in precisely segmenting lung nodules from CT scans, underscoring its potential to advance early de

Cross-sectional imaging of the lungs, or pulmonary imaging, has proven to be an incredibly valuable tool in a wide range of pulmonary diseases. The vast majority of lung imaging is done with CT, as it is fast enough to freeze respiratory motion and provides high spatial resolution to visualize fine structure of the lungs. MRI of the lungs is inherently challenging due to the presence of large local magnetic field gradients, relatively low proton density, and motion. The benefits of performing MRI for lung imaging include no ionizing radiation, opportunities for multiple contrasts, and integration with other MRI also offers the opportunity to obtain multiple tissue contrasts. The most common lung MRI techniques are structural T1-weighted scans, but also emerging are functional contrasts such as ventilation and perfusion, as well as other MRI contrast mechanisms including T2-weighting and diffusion-weighting. Finally, lung MRI can be combined with other MRI scanning techniques, including cardiac MRI, abdominal MRI, whole-body MRI, and PET/MRI, for increasing examination efficiency by only requiring a single scan session and providing more comprehensive assessment that includes evalua

This paper addresses the problem of pathological lung segmentation, a significant challenge in medical image analysis, particularly pronounced in cases of peripheral opacities (severe fibrosis and consolidation) because of the textural similarity between lung tissue and surrounding areas. To overcome these challenges, this paper emphasizes the use of CycleGAN for unpaired image-to-image translation, in order to provide an augmentation method able to generate fake pathological images matching an existing ground truth. Although previous studies have employed CycleGAN, they often neglect the challenge of shape deformation, which is crucial for accurate medical image segmentation. Our work introduces an innovative strategy that incorporates additional loss functions. Specifically, it proposes an L1 loss based on the lung surrounding which shape is constrained to remain unchanged at the transition from the healthy to pathological domains. The lung surrounding is derived based on ground truth lung masks available in the healthy domain. Furthermore, preprocessing steps, such as cropping based on ribs/vertebra locations, are applied to refine the input for the CycleGAN, ensuring that the n

Objective: This work proposes a semi-supervised training approach for detecting lung and heart sounds simultaneously with only one trained model and in invariance to the auscultation point. Methods: We use open-access data from the 2016 Physionet/CinC Challenge, the 2022 George Moody Challenge, and from the lung sound database HF_V1. We first train specialist single-task models using foreground ground truth (GT) labels from different auscultation databases to identify background sound events in the respective lung and heart auscultation databases. The pseudo-labels generated in this way were combined with the ground truth labels in a new training iteration, such that a new model was subsequently trained to detect foreground and background signals. Benchmark tests ensured that the newly trained model could detect both, lung, and heart sound events in different auscultation sites without regressing on the original task. We also established hand-validated labels for the respective background signal in heart and lung sound auscultations to evaluate the models. Results: In this work, we report for the first time results for i) a multi-class prediction for lung sound events and ii) for s

Lung nodule malignancy prediction has been enhanced by advanced deep-learning techniques and effective tricks. Nevertheless, current methods are mainly trained with cross-entropy loss using one-hot categorical labels, which results in difficulty in distinguishing those nodules with closer progression labels. Interestingly, we observe that clinical text information annotated by radiologists provides us with discriminative knowledge to identify challenging samples. Drawing on the capability of the contrastive language-image pre-training (CLIP) model to learn generalized visual representations from text annotations, in this paper, we propose CLIP-Lung, a textual knowledge-guided framework for lung nodule malignancy prediction. First, CLIP-Lung introduces both class and attribute annotations into the training of the lung nodule classifier without any additional overheads in inference. Second, we designed a channel-wise conditional prompt (CCP) module to establish consistent relationships between learnable context prompts and specific feature maps. Third, we align image features with both class and attribute features via contrastive learning, rectifying false positives and false negativ

In the recent past with the rapid surge of COVID-19 infections, lung ultrasound has emerged as a fast and powerful diagnostic tool particularly for continuous and periodic monitoring of the lung. There have been many attempts towards severity classification, segmentation and detection of key landmarks in the lung. Leveraging the progress, an automated lung ultrasound video analysis package is presented in this work, which can provide summary of key frames in the video, flagging of the key frames with lung infection and options to automatically detect and segment the lung landmarks. The integrated package is implemented as an open-source web application and available in the link https://github.com/anitoanto/alus-package.

In this paper, we propose CPSNet, a label-free deep unfolding framework for lung ultrasound image analysis. CPSNet unfolds a Cauchy proximal splitting algorithm into a forward-backward style network architecture, incorporating skip connections to iteratively enhance noisy Radon domain images. We introduce the Radon-Based Neighbor Reconstruction Loss, a novel loss function that enforces reconstruction consistency between subsampled and reconstructed image pairs in the Radon domain, while applying regularization terms to enhance robustness against noise. Integrating the Cauchy penalty into the loss function preserves the statistical influence of the prior while enabling the network to learn flexibly. Trained in an unsupervised manner without ground truth images, CPSNet is evaluated using structural similarity index, Proxy peak signal-to-noise Ratio, and relative L2 norm, demonstrating stable and effective performance in lung ultrasound image reconstruction. Applied to B-line detection, CPSNet achieves greater stability, adaptability and efficiency compared to traditional and object detection methods, effectively preserving line structures and minimizing false detections. This study u

This study aimed to evaluate the performance of a novel unsupervised deep learning-based framework for automated infections lesion segmentation from CT images of Covid patients. In the first step, two residual networks were independently trained to identify the lung tissue for normal and Covid patients in a supervised manner. These two models, referred to as DL-Covid and DL-Norm for Covid-19 and normal patients, respectively, generate the voxel-wise probability maps for lung tissue identification. To detect Covid lesions, the CT image of the Covid patient is processed by the DL-Covid and DL-Norm models to obtain two lung probability maps. Since the DL-Norm model is not familiar with Covid infections within the lung, this model would assign lower probabilities to the lesions than the DL-Covid. Hence, the probability maps of the Covid infections could be generated through the subtraction of the two lung probability maps obtained from the DL-Covid and DL-Norm models. Manual lesion segmentation of 50 Covid-19 CT images was used to assess the accuracy of the unsupervised lesion segmentation approach. The Dice coefficients of 0.985 and 0.978 were achieved for the lung segmentation of nor

This study estimated the planning quality of intensity-modulated radiotherapy in 42 lung cancer cases to provide preliminary data for the development of a planning quality assurance algorithm. Organs in or near the thoracic cavity (ipsilateral lung, contralateral lung, heart, liver, esophagus, spinal cord, and bronchus) were selected as organs at risk (OARs). Radiotherapy plans were compared using the conformity index (CI), coverage index (CVI), and homogeneity index (HI) of the planning target volume (PTV), OAR-PTV distance and OAR-PTV overlap volume, and the V10Gy, V20Gy, and equivalent uniform dose (EUD) of the OARs. The CI, CVI, and HI of the PTV were 0.54 - 0.89 , 0.90 - 1.00 , and 0.11 - 0.41, respectively. The mean EUDs (V10Gy, V20Gy) of the ipsilateral lung, contralateral lung, esophagus, cord, liver, heart, and bronchus were 8.07 Gy (28.06, 13.17), 2.59 Gy (6.53, 1.18), 7.02 Gy (26.17, 12.32), 3.56 Gy (13.56, 4.48), 0.72 Gy (2.15, 0.91), 5.14 Gy (19.68, 8.62), and 10.56 Gy (36.08, 19.79), respectively. EUDs tended to decrease as OAR-PTV distance increased and OAR-PTV overlap volume decreased. Because the plans in this study were from a single department, relatively few peo