The assessment of lymphovascular invasion (LVI) is crucial in the management of rectal cancer; However, accurately evaluating LVI preoperatively using imaging remains challenging. Recent advances in radiomics have created opportunities for developing more accurate diagnostic tools. This study aimed to develop and validate a deep learning model for predicting LVI in rectal cancer patients using preoperative MR imaging. These cases were randomly divided into a training cohort (n = 233) and an validation cohort (n = 101) at a ratio of 7:3. Based on the pathological reports, the patients were classified into positive and negative groups according to their LVI status. Based on the preoperative MRI T2WI axial images, the regions of interest (ROI) were defined from the tumor itself and the edges of the tumor extending outward by 5 pixels, 10 pixels, 15 pixels, and 20 pixels. The 2D and 3D deep learning features were extracted using the DenseNet121 architecture, and the deep learning models were constructed, including a total of ten models: GTV (the tumor itself), GPTV5 (the tumor itself and the tumor extending outward by 5 pixels), GPTV10, GPTV15, and GPTV20. To assess model performance, we utilized the area under the curve (AUC) and conducted DeLong test to compare different models, aiming to identify the optimal model for predicting LVI in rectal cancer. In the 2D deep learning model group, the 2D GPTV10 model demonstrated superior performance with an AUC of 0.891 (95% confidence interval [CI] 0.850-0.933) in the training cohort and an AUC of 0.841 (95% CI 0.767-0.915) in the validation cohort. The difference in AUC between this model and other 2D models was not statistically significant based on DeLong test (p > 0.05); In the group of 3D deep learning models, the 3D GPTV10 model had the highest AUC, with a training cohort AUC of 0.961 (95% CI 0.940-0.982) and a validation cohort AUC of 0.928 (95% CI 0.881-0.976). DeLong test demonstrated that the performance of the 3D GPTV10 model surpassed other 3D models as well as the 2D GPTV10 model (p < 0.05). The study developed a deep learning model, namely 3D GPTV10, utilizing preoperative MRI data to accurately predict the presence of LVI in rectal cancer patients. By training on the tumor itself and its surrounding margin 10 pixels as the region of interest, this model achieved superior performance compared to other deep learning models. These findings have significant implications for clinicians in formulating personalized treatment plans for rectal cancer patients.

Most machine learning-based image segmentation models produce pixel-wise confidence scores - typically derived from softmax outputs - that represent the model's predicted probability for each class label at every pixel. While this information can be particularly valuable in high-stakes domains such as medical imaging, these (uncalibrated) scores are heuristic in nature and do not constitute rigorous quantitative uncertainty estimates. Conformal prediction (CP) provides a principled framework for transforming heuristic confidence scores into statistically valid uncertainty estimates. However, applying CP directly to image segmentation ignores the spatial correlations between pixels, a fundamental characteristic of image data. This can result in overly conservative and less interpretable uncertainty estimates. To address this, we propose CONSIGN (Conformal Segmentation Informed by Spatial Groupings via Decomposition), a CP-based method that incorporates spatial correlations to improve uncertainty quantification in image segmentation. Our method generates meaningful prediction sets that come with user-specified, high-probability error guarantees. It is compatible with any pre-trained segmentation model capable of generating multiple sample outputs - such as those using dropout, Bayesian modeling, or ensembles. We evaluate CONSIGN against a standard pixel-wise CP approach across three medical imaging datasets and two COCO dataset subsets, using three different pre-trained segmentation models. Results demonstrate that accounting for spatial structure significantly improves performance across multiple metrics and enhances the quality of uncertainty estimates.

A common neurodegenerative disease, Alzheimer's disease requires a precise diagnosis and efficient treatment, particularly in light of escalating healthcare expenses and the expanding use of artificial intelligence in medical diagnostics. Many recent studies shows that the combination of brain Magnetic Resonance Imaging (MRI) and deep neural networks have achieved promising results for diagnosing AD. Using deep convolutional neural networks, this paper introduces a novel deep learning architecture that incorporates multiresidual blocks, specialized spatial attention blocks, grouped query attention, and multi-head attention. The study assessed the model's performance on four publicly accessible datasets and concentrated on identifying binary and multiclass issues across various categories. This paper also takes into account of the explainability of AD's progression and compared with state-of-the-art methods namely Gradient Class Activation Mapping (GradCAM), Score-CAM, Faster Score-CAM, and XGRADCAM. Our methodology consistently outperforms current approaches, achieving 99.66\% accuracy in 4-class classification, 99.63\% in 3-class classification, and 100\% in binary classification using Kaggle datasets. For Open Access Series of Imaging Studies (OASIS) datasets the accuracies are 99.92\%, 99.90\%, and 99.95\% respectively. The Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) dataset was used for experiments in three planes (axial, sagittal, and coronal) and a combination of all planes. The study achieved accuracies of 99.08\% for axis, 99.85\% for sagittal, 99.5\% for coronal, and 99.17\% for all axis, and 97.79\% and 8.60\% respectively for ADNI-2. The network's ability to retrieve important information from MRI images is demonstrated by its excellent accuracy in categorizing AD stages.

The International Society of Ultrasound advocates Intrapartum Ultrasound (US) Imaging in Obstetrics and Gynecology (ISUOG) to monitor labour progression through changes in fetal head position. Two reliable ultrasound-derived parameters that are used to predict outcomes of instrumental vaginal delivery are the angle of progression (AoP) and head-symphysis distance (HSD). In this work, as part of the Intrapartum Ultrasounds Grand Challenge (IUGC) 2024, we propose an automated fetal biometry measurement pipeline to reduce intra- and inter-observer variability and improve measurement reliability. Our pipeline consists of three key tasks: (i) classification of standard planes (SP) from US videos, (ii) segmentation of fetal head and pubic symphysis from the detected SPs, and (iii) computation of the AoP and HSD from the segmented regions. We perform sparse sampling to mitigate class imbalances and reduce spurious correlations in task (i), and utilize ensemble-based deep learning methods for task (i) and (ii) to enhance generalizability under different US acquisition settings. Finally, to promote robustness in task iii) with respect to the structural fidelity of measurements, we retain the largest connected components and apply ellipse fitting to the segmentations. Our solution achieved ACC: 0.9452, F1: 0.9225, AUC: 0.983, MCC: 0.8361, DSC: 0.918, HD: 19.73, ASD: 5.71, $\Delta_{AoP}$: 8.90 and $\Delta_{HSD}$: 14.35 across an unseen hold-out set of 4 patients and 224 US frames. The results from the proposed automated pipeline can improve the understanding of labour arrest causes and guide the development of clinical risk stratification tools for efficient and effective prenatal care.

Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for diagnosis and analysis. Variations in equipment and acquisition settings can further exacerbate differences in data distribution and noise levels, reducing the generalizability of pre-trained models. This work presents a self-supervised ultrasound video super-resolution algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive optimization process of a neural network that enhances the resolution of given ultrasound videos without requiring paired training data while simultaneously removing noise. Quantitative and visual evaluations demonstrate that DUP outperforms existing super-resolution algorithms, leading to substantial improvements for downstream applications.

In cone beam computed tomography(CBCT)-guided adaptive radiotherapy, rapid and precise segmentation of organs-at-risk(OARs)is essential for accurate dose verification and online replanning. The quality of CBCT images obtained with current onboard CBCT imagers and clinical imaging protocols, however, is often compromised by artifacts such as scatter and motion, particularly for thoracic CBCTs. These artifacts not only degrade image contrast but also obscure anatomical boundaries, making accurate segmentation on CBCT images significantly more challenging compared to planning CT images. To address these persistent challenges, we propose a novel multi-channel conditional consistency diffusion model(MCCDM)for segmentation of OARs in thoracic CBCT images (CBCT-MCCDM), which harnesses its domain transfer capabilities to improve segmentation accuracy across different imaging modalities. By jointly training the MCCDM with CT images and their corresponding masks, our framework enables an end-to-end mapping learning process that generates accurate segmentation of OARs.&#xD;This CBCT-MCCDM was used to delineate esophagus, heart, the left and right lungs, and spinal cord on CBCT images from each patient with lung cancer. We quantitatively evaluated our approach by comparing model-generated contours with ground truth contours from 33 patients with lung cancer treated with 5-fraction stereotactic body radiation therapy (SBRT), demonstrating its potential to enhance segmentation accuracy despite the presence of challenging CBCT artifacts. The proposed method was evaluated using average Dice similarity coefficients (DSC), sensitivity, specificity, 95th Percentile Hausdorff Distance (HD95), and mean surface distance (MSD) for each of the five OARs. The method achieved average DSC values of 0.82, 0.88, 0.95, 0.96, and 0.96 for the esophagus, heart, left lung, right lung, and spinal cord, respectively. Sensitivity values were 0.813, 0.922, 0.956, 0.958, and 0.929, respectively, while specificity values were 0.991, 0.994, 0.996, 0.996, and 0.995, respectively. We compared the proposed method with two state-of-art methods, CBCT-only method and U-Net, and demonstrated that the proposed CBCT-MCCDM.

The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images. A total of 3,537 X-ray images, including 1,871 fracture and 1,666 non-fracture cases, were collected from three healthcare centers. Radiomic features were extracted using the PyRadiomics library, and deep features were derived from the bottleneck layer of an autoencoder. Both feature modalities underwent reliability assessment via Intraclass Correlation Coefficient (ICC) and cosine similarity. Feature selection methods, including ANOVA, Mutual Information (MI), Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE), were applied to optimize the feature set. Classifiers such as XGBoost, CatBoost, Random Forest, and a Voting Classifier were used to evaluate diagnostic performance. The dataset was divided into training (70%) and testing (30%) sets, and metrics such as accuracy, sensitivity, and AUC-ROC were used for evaluation. The combined radiomic and deep feature approach consistently outperformed standalone methods. The Voting Classifier paired with MI achieved the highest performance, with a test accuracy of 95%, sensitivity of 94%, and AUC-ROC of 96%. The end-to-end model achieved competitive results with an accuracy of 93% and AUC-ROC of 94%. SHAP analysis and t-SNE visualizations confirmed the interpretability and robustness of the selected features. This hybrid framework demonstrates the potential for integrating radiomic and deep features to enhance diagnostic performance for wrist and forearm fractures, providing a reliable and interpretable solution suitable for clinical applications.

A layman in health systems is a person who doesn't have any knowledge about health data i.e., X-ray, MRI, CT scan, and health examination reports, etc. The motivation behind the proposed invention is to help laymen to make medical images understandable. The health model is trained using a neural network approach that analyses user health examination data; predicts the type and level of the disease and advises precaution to the user. Cellular Automata (CA) technology has been integrated with the neural networks to segment the medical image. The CA analyzes the medical images pixel by pixel and generates a robust threshold value which helps to efficiently segment the image and identify accurate abnormal spots from the medical image. The proposed method has been trained and experimented using 10000+ medical images which are taken from various open datasets. Various text analysis measures i.e., BLEU, ROUGE, and WER are used in the research to validate the produced report. The BLEU and ROUGE calculate a similarity to decide how the generated text report is closer to the original report. The BLEU and ROUGE scores of the experimented images are approximately 0.62 and 0.90, claims that the produced report is very close to the original report. The WER score 0.14, claims that the generated report contains the most relevant words. The overall summary of the proposed research is that it provides a fruitful medical report with accurate disease and precautions to the laymen.

To evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thromboembolic pulmonary hypertension (CTEPH) and chronic thromboembolic disease (CTED). A single-center, retrospective analysis of 350 sequential patients with right heart catheterization (RHC)-confirmed CTEPH, CTED, and normal controls was conducted. Parameters such as the main pulmonary artery diameter (MPAd), the ratio of MPA to ascending aorta diameter (MPAd/AAd), the ratio of right to left ventricle diameter (RVd/LVd), and the ratio of RV to LV volume (RVv/LVv) were evaluated using automated AI software and compared with manual analysis. The reliability was assessed through an intraclass correlation coefficient (ICC) analysis. The diagnostic accuracy was determined using receiver-operating characteristic (ROC) curves. Compared to CTED and control groups, CTEPH patients were significantly more likely to have elevated automatic CTPA metrics (all p < 0.001, respectively). Automated MPAd, MPAd/Aad, and RVv/LVv had a strong correlation with mPAP (r = 0.952, 0.904, and 0.815, respectively, all p < 0.001). The automated and manual CTPA analyses showed strong concordance. For the CTEPH and CTED categories, the optimal area under the curve (AU-ROC) reached 0.939 (CI: 0.908-0.969). In the CTEPH and control groups, the best AU-ROC was 0.970 (CI: 0.953-0.988). In the CTED and control groups, the best AU-ROC was 0.782 (CI: 0.724-0.840). Automated AI-driven CTPA analysis provides a dependable approach for evaluating patients with CTEPH, CTED, and normal controls, demonstrating excellent consistency and efficiency. Question Guidelines do not advocate for applying treatment protocols for CTEPH to patients with CTED; early detection of the condition is crucial. Findings Automated CTPA analysis was feasible in 100% of patients with good agreement and would have added information for early detection and identification. Clinical relevance Automated AI-driven CTPA analysis provides a reliable approach demonstrating excellent consistency and efficiency. Additionally, these noninvasive imaging findings may aid in treatment stratification and determining optimal intervention directed by RHC.

The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models can use non-relevant parts of images, also called spurious correlations or shortcuts, to obtain high performance on benchmark datasets but fail in real-world scenarios. In this work, we challenge the capacity of convolutional neural networks (CNN) to classify chest X-rays and eye fundus images while masking out clinically relevant parts of the image. We show that all models trained on the PadChest dataset, irrespective of the masking strategy, are able to obtain an area under the curve (AUC) above random. Moreover, the models trained on full images obtain good performance on images without the region of interest (ROI), even superior to the one obtained on images only containing the ROI. We also reveal a possible spurious correlation in the Chákṣu dataset while the performances are more aligned with the expectation of an unbiased model. We go beyond the performance analysis with the usage of the explainability method SHAP and the analysis of embeddings. We asked a radiology resident to interpret chest X-rays under different masking to complement our findings with clinical knowledge.