Timely and accurate diagnosis of severe neonatal cerebral lesions is critical for preventing long-term neurological damage and addressing life-threatening conditions. Cranial ultrasound is the primary screening tool, but the process is time-consuming and reliant on operator's proficiency. In this study, a deep-learning powered neonatal cerebral lesions screening system capable of automatically extracting standard views from cranial ultrasound videos and identifying cases with severe cerebral lesions is developed based on 8,757 neonatal cranial ultrasound images. The system demonstrates an area under the curve of 0.982 and 0.944, with sensitivities of 0.875 and 0.962 on internal and external video datasets, respectively. Furthermore, the system outperforms junior radiologists and performs on par with mid-level radiologists, with 55.11% faster examination efficiency. In conclusion, the developed system can automatically extract standard views and make correct diagnosis with efficiency from cranial ultrasound videos and might be useful to deploy in multiple application scenarios.

To explore the value of a deep learning-based model in distinguishing between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) manifesting suspicious microcalcifications on mammography. A total of 294 breast cancer cases (106 DCIS and 188 IDC) from two centers were randomly allocated into training, internal validation and external validation sets in this retrospective study. Clinical variables differentiating DCIS from IDC were identified through univariate and multivariate analyses and used to build a clinical model. Deep learning features were extracted using Resnet101 and selected by minimum redundancy maximum correlation (mRMR) and least absolute shrinkage and selection operator (LASSO). A deep learning model was developed using deep learning features, and a combined model was constructed by combining these features with clinical variables. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of each model. Multivariate logistic regression identified lesion type and BI-RADS category as independent predictors for differentiating DCIS from IDC. The clinical model incorporating these factors achieved an AUC of 0.67, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.63 in the external validation set. In comparison, the deep learning model showed an AUC of 0.97, sensitivity of 0.94 and specificity of 0.92, accuracy of 0.93. For the combined model, the AUC, sensitivity, specificity and accuracy were 0.97, 0.96, 0.92 and 0.95, respectively. The diagnostic efficacy of the deep learning model and combined model was comparable (p>0.05), and both models outperformed the clinical model (p<0.05). Deep learning provides an effective non-invasive approach to differentiate DCIS from IDC presenting as suspicious microcalcifications on mammography.

Midline shift (MLS) is an intracranial pathology characterized by the displacement of brain parenchyma across the skull's midsagittal axis, typically caused by mass effect from space-occupying lesions or traumatic brain injuries. Prompt detection of MLS is crucial, because delays in identification and intervention can negatively impact patient outcomes. The gap we have addressed in this work is the development of a deep learning algorithm that encompasses the full severity range from mild to severe cases of MLS. Notably, in more severe cases, the mass effect often effaces the septum pellucidum, rendering it unusable as a fiducial point of reference. We sought to enable rapid and accurate detection of MLS by leveraging advances in artificial intelligence (AI). Using a cohort of 981 patient CT scans with a breadth of cerebral pathologies from our institution, we manually chose an individual slice from each CT scan primarily based on the presence of the lateral ventricles and annotated 400 of these scans for the lateral ventricles and skull-axis midline by using Roboflow. Finally, we trained an AI model based on the You Only Look Once object detection system to identify MLS in the individual slices of the remaining 581 CT scans. When comparing normal and mild cases to moderate and severe cases of MLS detection, our model yielded an area under the curve of 0.79 with a sensitivity of 0.73 and specificity of 0.72 indicating our model is sensitive enough to capture moderate and severe MLS and specific enough to differentiate them from mild and normal cases. We developed an AI model that reliably identifies the lateral ventricles and the cerebral midline across various pathologies in patient CT scans. Most importantly, our model accurately identifies and stratifies clinically significant and emergent MLS from nonemergent cases. This could serve as a foundational element for a future clinically integrated approach that flags urgent studies for expedited review, potentially facilitating more timely treatment when necessary.

Medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound are essential for accurate diagnosis and treatment planning in modern healthcare. However, noise contamination during image acquisition and processing frequently degrades image quality, obscuring critical diagnostic details and compromising clinical decision-making. Additionally, enhancement techniques such as histogram equalization may inadvertently amplify existing noise artifacts, including salt-and-pepper distortions. This study investigates wavelet transform-based denoising methods for effective noise mitigation in medical images, with the primary objective of identifying optimal combinations of threshold values, decomposition levels, and wavelet types to achieve superior denoising performance and enhanced diagnostic accuracy. Through systematic evaluation across various noise conditions, the research demonstrates that the bior6.8 biorthogonal wavelet with universal thresholding at decomposition levels 2-3 consistently achieves optimal denoising performance, providing significant noise reduction while preserving essential anatomical structures and diagnostic features critical for clinical applications.

Accurate characterization of in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building upon previous efforts to construct spatiotemporal fetal brain MRI atlases, we present the CRL-2025 fetal brain atlas, which is a spatiotemporal (4D) atlas of the developing fetal brain between 21 and 37 gestational weeks. This atlas is constructed from carefully processed MRI scans of 160 fetuses with typically-developing brains using a diffeomorphic deformable registration framework integrated with kernel regression on age. CRL-2025 uniquely includes detailed tissue segmentations, transient white matter compartments, and parcellation into 126 anatomical regions. This atlas offers significantly enhanced anatomical details over the CRL-2017 atlas, and is released along with the CRL diffusion MRI atlas with its newly created tissue segmentation and labels as well as deep learning-based multiclass segmentation models for fine-grained fetal brain MRI segmentation. The CRL-2025 atlas and its associated tools provide a robust and scalable platform for fetal brain MRI segmentation, groupwise analysis, and early neurodevelopmental research, and these materials are publicly released to support the broader research community.

Magnetic Resonance Imaging (MRI) offers unparalleled soft-tissue contrast but is fundamentally limited by long acquisition times. While deep learning-based accelerated MRI can dramatically shorten scan times, the reconstruction from undersampled data introduces ambiguity resulting from an ill-posed problem with infinitely many possible solutions that propagates to downstream clinical tasks. This uncertainty is usually ignored during the acquisition process as acceleration factors are often fixed a priori, resulting in scans that are either unnecessarily long or of insufficient quality for a given clinical endpoint. This work introduces a dynamic, uncertainty-aware acquisition framework that adjusts scan time on a per-subject basis. Our method leverages a probabilistic reconstruction model to estimate image uncertainty, which is then propagated through a full analysis pipeline to a quantitative metric of interest (e.g., patellar cartilage volume or cardiac ejection fraction). We use conformal prediction to transform this uncertainty into a rigorous, calibrated confidence interval for the metric. During acquisition, the system iteratively samples k-space, updates the reconstruction, and evaluates the confidence interval. The scan terminates automatically once the uncertainty meets a user-predefined precision target. We validate our framework on both knee and cardiac MRI datasets. Our results demonstrate that this adaptive approach reduces scan times compared to fixed protocols while providing formal statistical guarantees on the precision of the final image. This framework moves beyond fixed acceleration factors, enabling patient-specific acquisitions that balance scan efficiency with diagnostic confidence, a critical step towards personalized and resource-efficient MRI.

Accurate lung cancer risk prediction remains challenging due to substantial variability across patient populations and clinical settings -- no single model performs best for all cohorts. To address this, we propose a personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for each patient by combining cohort-specific knowledge with modern retrieval and reasoning techniques. Given a patient's CT scan and structured metadata -- including demographic, clinical, and nodule-level features -- the agent first performs cohort retrieval using FAISS-based similarity search across nine diverse real-world cohorts to identify the most relevant patient population from a multi-institutional database. Second, a Large Language Model (LLM) is prompted with the retrieved cohort and its associated performance metrics to recommend the optimal prediction algorithm from a pool of eight representative models, including classical linear risk models (e.g., Mayo, Brock), temporally-aware models (e.g., TDVIT, DLSTM), and multi-modal computer vision-based approaches (e.g., Liao, Sybil, DLS, DLI). This two-stage agent pipeline -- retrieval via FAISS and reasoning via LLM -- enables dynamic, cohort-aware risk prediction personalized to each patient's profile. Building on this architecture, the agent supports flexible and cohort-driven model selection across diverse clinical populations, offering a practical path toward individualized risk assessment in real-world lung cancer screening.

To develop and evaluate a machine-learning (ML) decision-support tool that standardizes selection of intracavitary brachytherapy (ICBT) versus hybrid intracavitary/interstitial brachytherapy (IC/ISBT) in high-dose-rate (HDR) cervical cancer. We retrospectively analyzed 159 HDR brachytherapy plans from 50 consecutive patients treated between April 2022 and June 2024. Brachytherapy techniques (ICBT or IC/ISBT) were determined by an experienced radiation oncologist using CT/MRI-based 3-D image-guided brachytherapy. For each plan, 144 shape- and distance-based geometric features describing the high-risk clinical target volume (HR-CTV), bladder, rectum, and applicator were extracted. Nested five-fold cross-validation combined minimum-redundancy-maximum-relevance feature selection with five classifiers (k-nearest neighbors, logistic regression, naïve Bayes, random forest, support-vector classifier) and two voting ensembles (hard and soft voting). Model performance was benchmarked against single-factor rules (HR-CTV > 30 cm³; maximum lateral HR-CTV-tandem distance > 25 mm). Logistic regression achieved the highest test accuracy 0.849 ± 0.023 and a mean area-under-the-curve (AUC) 0.903 ± 0.033, outperforming the volume rule and matching the distance rule's AUC 0.907 ± 0.057 while providing greater accuracy 0.805 ± 0.114. These differences were not statistically significant. Feature-importance analysis showed that the maximum HR-CTV-tandem lateral distance and the bladder's minimal short-axis length consistently dominated model decisions.​ CONCLUSIONS: A compact ML tool using two readily measurable geometric features can reliably assist clinicians in choosing between ICBT and IC/ISBT, thereby reducing inter-physician variability and promoting standardized HDR cervical brachytherapy technique selection.

Late Gadolinium Enhancement (LGE) imaging remains the gold standard for assessing myocardial fibrosis and scarring, with left ventricular (LV) LGE presence and extent serving as a predictor of major adverse cardiac events (MACE). Despite its clinical significance, LGE-based LV scar quantification is not used routinely due to the labor-intensive manual segmentation and substantial inter-observer variability. We developed ScarNet that synergistically combines a transformer-based encoder in Medical Segment Anything Model (MedSAM), which we fine-tuned with our dataset, and a convolution-based decoder in U-Net with tailored attention blocks to automatically segment myocardial scar boundaries while maintaining anatomical context. This network was trained and fine-tuned on an existing database of 401 ischemic cardiomyopathy patients (4,137 2D LGE images) with expert segmentation of myocardial and scar boundaries in LGE images, validated on 100 patients (1,034 2D LGE images) during training, and tested on unseen set of 184 patients (1,895 2D LGE images). Ablation studies were conducted to validate each architectural component's contribution. In 184 independent testing patients, ScarNet achieved accurate scar boundary segmentation (median DICE=0.912 [interquartile range (IQR): 0.863-0.944], concordance correlation coefficient [CCC]=0.963), significantly outperforming both MedSAM (median DICE=0.046 [IQR: 0.043-0.047], CCC=0.018) and nnU-Net (median DICE=0.638 [IQR: 0.604-0.661], CCC=0.734). For scar volume quantification, ScarNet demonstrated excellent agreement with manual analysis (CCC=0.995, percent bias=-0.63%, CoV=4.3%) compared to MedSAM (CCC=0.002, percent bias=-13.31%, CoV=130.3%) and nnU-Net (CCC=0.910, percent bias=-2.46%, CoV=20.3%). Similar trends were observed in the Monte Carlo simulations with noise perturbations. The overall accuracy was highest for SCARNet (sensitivity=95.3%; specificity=92.3%), followed by nnU-Net (sensitivity=74.9%; specificity=69.2%) and MedSAM (sensitivity=15.2%; specificity=92.3%). ScarNet outperformed MedSAM and nnU-Net for predicting myocardial and scar boundaries in LGE images of patients with ischemic cardiomyopathy. The Monte Carlo simulations demonstrated that ScarNet is less sensitive to noise perturbations than other tested networks.

Dextrocardia is a congenital anomaly arising during fetal development, characterised by the abnormal positioning of the heart on the right side of the chest, instead of its usual anatomical location on the left. This paper describes a machine learning-based method to automatically assess ultrasound (US) transverse videos to detect dextrocardia by analysing the Situs and four-chamber (4CH) views. The method processes ultrasound video sweeps that users capture, which include the Situs and 4CH views. The automated analysis method consists of three stages. First, four fetal anatomical structures (chest, spine, stomach and heart) are automatically segmented using SegFormer. Second, a quality assessment (QA) module verifies that the video includes informative frames. Thirdly, the orientation of the stomach and heart relative to the fetal chest (either right or left side) is determined to assess dextrocardia. The method utilises a Transformer-based segmentation model to perform segmentation of the fetal anatomy. Segmentation performance was evaluated using the Dice coefficient, and fetal anatomy centroid estimation accuracy using root mean squared error (RMSE). Dextrocardia was classified based on a frame-based classification score (FBCS). The datasets consist of 142 pairs of Situs and 4CH US (284 frames in total) for training; and 14 US videos (7 normal, 7 dextrocardia, 2,916 frames total) for testing. The method achieved a Dice score of 0.968, 0.958, 0.953, 0.949 for chest, spine, stomach and heart segmentation, respectively, and anatomy centroid RMSE of 0.23mm, 0.34mm, 0.25mm, 0.39mm for the same structures. The QA rejected 172 frames. The assessment for dextrocardia achieved a FBCS of 0.99 with a standard deviation of 0.01 for normal and 0.02 for dextrocardia videos. Our automated method demonstrates accurate segmentation and reliable detection of dextrocardia from US videos. Due to the simple acquisition protocol and its robust analytical pipeline, our method is suitable for healthcare providers who are non-cardiac experts. It has the potential to facilitate earlier and more consistent prenatal identification of dextrocardia during screening, particularly in settings with limited access to experts in fetal echocardiography.