Endobronchial ultrasound (EBUS) is a widely used imaging modality for evaluating thoracic lymph nodes (LNs), particularly in the staging of lung cancer. Artificial intelligence (AI)-assisted EBUS has emerged as a promising tool to enhance diagnostic accuracy. However, its effectiveness in differentiating benign from malignant thoracic LNs remains uncertain. This meta-analysis aimed to evaluate the diagnostic performance of AI-assisted EBUS compared to the pathological reference standards. A systematic search was conducted across PubMed, Embase, and Web of Science for studies assessing AI-assisted EBUS in differentiating benign and malignant thoracic LNs. The reference standard included pathological confirmation via EBUS-guided transbronchial needle aspiration, surgical resection, or other histological/cytological validation methods. Sensitivity, specificity, diagnostic likelihood ratios, and diagnostic odds ratio (OR) were pooled using a random-effects model. The area under the receiver operating characteristic curve (AUROC) was summarized to evaluate diagnostic accuracy. Subgroup analyses were conducted by study design, lymph node location, and AI model type. Twelve studies with a total of 6,090 thoracic LNs were included. AI-assisted EBUS showed a pooled sensitivity of 0.75 (95% confidence interval [CI]: 0.60-0.86, I² = 97%) and specificity of 0.88 (95% CI: 0.83-0.92, I² = 96%). The positive and negative likelihood ratios were 6.34 (95% CI: 4.41-9.08) and 0.28 (95% CI: 0.17-0.47), respectively. The pooled diagnostic OR was 22.38 (95% CI: 11.03-45.38), and the AUROC was 0.90 (95% CI: 0.88-0.93). The subgroup analysis showed higher sensitivity but lower specificity in retrospective studies compared to prospective ones (sensitivity: 0.87 vs. 0.42; specificity: 0.80 vs. 0.93; both p < 0.001). No significant differences were found by lymph node location or AI model type. AI-assisted EBUS shows promise in differentiating benign from malignant thoracic LNs, particularly those with high specificity. However, substantial heterogeneity and moderate sensitivity highlight the need for cautious interpretation and further validation. PROSPERO CRD42025637964.

The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the urgency advanced predictive tools. This study aimed to develop a machine learning-based predictive model integrating quantitative CT (qCT) radiomics and clinical features to assess the risk of lung fibrosis in COVID-19 patients. A total of 204 patients with confirmed COVID-19 pneumonia were included in the study. Of these, 93 patients were assigned to the development cohort (74 for training and 19 for internal validation), while 111 patients from three independent hospitals constituted the external validation cohort. Chest CT images were analyzed using qCT software. Clinical data and laboratory parameters were obtained from electronic health records. Least absolute shrinkage and selection operator (LASSO) regression with 5-fold cross-validation was used to select the most predictive features. Twelve machine learning algorithms were independently trained. Their performances were evaluated by receiver operating characteristic (ROC) curves, area under the curve (AUC) values, sensitivity, and specificity. Seventy-eight features were extracted and reduced to ten features for model development. These included two qCT radiomics signatures: (1) whole lung_reticulation (%) interstitial lung disease (ILD) texture analysis, (2) interstitial lung abnormality (ILA)_Num of lung zones ≥ 5%_whole lung_ILA. Among 12 machine learning algorithms evaluated, the support vector machine (SVM) model demonstrated the best predictive performance, with AUCs of 0.836 (95% CI: 0.830-0.842) in the training cohort, 0.796 (95% CI: 0.777-0.816) in the internal validation cohort, and 0.797 (95% CI: 0.691-0.873) in the external validation cohort. The integration of CT radiomics, clinical and laboratory variables using machine learning provides a robust tool for predicting pulmonary fibrosis progression in COVID-19 patients, facilitating early risk assessment and intervention.

Subspecialty knowledge barriers have limited the adoption of large language models (LLMs) in oncology. We introduce Woollie, an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung, breast, prostate, pancreatic, and colorectal cancers, with external validation using University of California, San Francisco (UCSF) data. Woollie surpasses ChatGPT in medical benchmarks and excels in eight non-medical benchmarks. Analyzing 39,319 radiology impression notes from 4002 patients, it achieved an overall area under the receiver operating characteristic curve (AUROC) of 0.97 for cancer progression prediction on MSK data, including a notable 0.98 AUROC for pancreatic cancer. On UCSF data, it achieved an overall AUROC of 0.88, excelling in lung cancer detection with an AUROC of 0.95. As the first oncology specific LLM validated across institutions, Woollie demonstrates high accuracy and consistency across cancer types, underscoring its potential to enhance cancer progression analysis.

Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhythmic death prognostication is challenging in patients with hypertrophic cardiomyopathy (HCM), a setting where current clinical guidelines show low performance and inconsistent accuracy. Here, we present a deep learning approach, MAARS (Multimodal Artificial intelligence for ventricular Arrhythmia Risk Stratification), to forecast lethal arrhythmia events in patients with HCM by analyzing multimodal medical data. MAARS' transformer-based neural networks learn from electronic health records, echocardiogram and radiology reports, and contrast-enhanced cardiac magnetic resonance images, the latter being a unique feature of this model. MAARS achieves an area under the curve of 0.89 (95% confidence interval (CI) 0.79-0.94) and 0.81 (95% CI 0.69-0.93) in internal and external cohorts and outperforms current clinical guidelines by 0.27-0.35 (internal) and 0.22-0.30 (external). In contrast to clinical guidelines, it demonstrates fairness across demographic subgroups. We interpret MAARS' predictions on multiple levels to promote artificial intelligence transparency and derive risk factors warranting further investigation.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI) and to demonstrate its potential clinical value as a noninvasive tool for guiding timely intervention and improving patient outcomes. This study included 238 patients with severe TBI (training cohort: n = 166; testing cohort: n = 72). Postoperative ultrasound images of the optic nerve sheath (ONS) and Spectral doppler imaging of middle cerebral artery (MCASDI) were obtained at 6 and 18 h after DC. Patients were grouped according to threshold values of 15 mmHg and 20 mmHg based on invasive intracranial pressure (ICPi) measurements. Clinical-semantic features were collected, and radiomics features were extracted from ONS images, and Additionally, deep transfer learning (DTL) features were generated using RseNet101. Predictive models were developed using the Light Gradient Boosting Machine (light GBM) machine learning algorithm. Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. A combined nomogram was developed by integrating DLR (deep learning radiomics) features with clinical-ultrasound variables, and its diagnostic performance over different thresholds was evaluated using Receiver Operating Characteristic (ROC) curve analysis and decision curve analysis (DCA). The nomogram model demonstrated superior performance over the clinical model at both 15 mmHg and 20 mmHg thresholds. For 15 mmHg, the AUC was 0.974 (95% confidence interval [CI]: 0.953-0.995) in the training cohort and 0.919 (95% CI: 0.845-0.993) in the testing cohort. For 20 mmHg, the AUC was 0.968 (95% CI: 0.944-0.993) in the training cohort and 0.889 (95% CI: 0.806-0.972) in the testing cohort. DCA curves showed net clinical benefit across all models. Among DLR models based on ONS, MCASDI, or their pre-fusion, the ONS-based model performed best in the testing cohorts. The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting early IH in post-DC patients. It shows promise for enhancing non-invasive ICP monitoring and supporting individualized therapeutic strategies.

Delineation of multiple organs in murine µCT images is crucial for preclinical studies but requires manual volumetric segmentation, a tedious and time-consuming process prone to inter-observer variability. Automatic deep learning-based segmentation can improve speed and reproducibility. While 2D and 3D deep learning models have been developed for anatomical segmentation, their generalization to external datasets has not been extensively investigated. Furthermore, ensemble learning, combining predictions of multiple 2D models, and partially-supervised learning (PSL), enabling training on partially-labeled datasets, have not been explored for preclinical purposes. This study demonstrates the first use of PSL frameworks and the superiority of 3D models in accuracy and generalizability to external datasets. Ensemble methods performed on par or better than the best individual 2D network, but only 3D models consistently generalized to external datasets (Dice Similarity Coefficient (DSC) > 0.8). PSL frameworks showed promising results across various datasets and organs, but its generalization to external data can be improved for some organs. This work highlights the superiority of 3D models over 2D and ensemble counterparts in accuracy and generalizability for murine µCT image segmentation. Additionally, a promising PSL framework is presented for leveraging multiple datasets without complete annotations. Our model can increase time-efficiency and improve reproducibility in preclinical radiotherapy workflows by circumventing manual contouring bottlenecks. Moreover, high segmentation accuracy of 3D models allows monitoring multiple organs over time using repeated µCT imaging, potentially reducing the number of mice sacrificed in studies, adhering to the 3R principle, specifically Reduction and Refinement.

The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. We introduce the anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework for detecting brain tumors in a privacy-preserving, decentralized system used by many healthcare institutions. ResNet-50 and capsule networks are incorporated to achieve better feature extraction and maintain the structure of images' spatial data. To get the best results, the hybrid Gorilla Badger optimization algorithm (HGBOA) is applied for selecting the key features. Preprocessing techniques include anisotropic diffusion filtering, morphological operations, and mutual information-based image registration. Updates to the model are made secure and tamper-evident on the Ethereum network with its private blockchain and SHA-256 hashing scheme. The project is built using Python, TensorFlow and PyTorch. The model displays 99.07% accuracy, 98.54% precision and 99.82% sensitivity on assessments from benchmark CT imaging of brain tumors. This approach also helps to reduce the number of cases where no disease is found when there is one and vice versa. The framework ensures that patients' data is protected and does not decrease the accuracy of brain tumor detection.

This study aimed at investigating a novel technique for designing and manufacturing a sealing socket abutment (SSA) using artificial intelligence (AI)-driven tooth segmentation and 3D printing technologies. A validated AI-powered module was used to segment the tooth to be replaced on the presurgical Cone Beam Computed Tomography (CBCT) scan. Following virtual surgical planning, the CBCT and intraoral scan (IOS) were imported into Mimics software. The AI-segmented tooth was aligned with the IOS, sliced horizontally at the temporary abutment's neck, and further trimmed 2 mm above the gingival margin to capture the emergence profile. A conical cut, 2 mm wider than the temporary abutment with a 5° taper, was applied for a passive fit. This process produced a custom sealing socket abutment, which was then 3D-printed. After atraumatic tooth extraction and immediate implant placement, the temporary abutment was positioned, followed by the SealPrint atop. A flowable composite was used to fill the gap between the temporary abutment and the SealPrint; the whole structure sealing the extraction socket, providing by design support for the interdental papilla and protecting the implant and (bio)materials used. True to planning, the SealPrint passively fits on the temporary abutment. It provides an optimal seal over the entire surface of the extraction socket, preserving the emergence profile of the extracted tooth, protecting the dental implant and stabilizing the graft material and blood clot. The SealPrint technique provides a reliable and fast solution for protection and preservation of the soft-, hard-tissues and emergence profile following immediate implant placement.

Accurate segmentation of white matter hyperintensities (WMH) is crucial for clinical decision-making, particularly in the context of multiple sclerosis. However, domain shifts, such as variations in MRI machine types or acquisition parameters, pose significant challenges to model calibration and uncertainty estimation. This comparative study investigates the impact of domain shift on WMH segmentation, proposing maximum-entropy regularization techniques to enhance model calibration and uncertainty estimation. The purpose is to identify errors appearing after model deployment in clinical scenarios using predictive uncertainty as a proxy measure, since it does not require ground-truth labels to be computed. We conducted experiments using a classic U-Net architecture and evaluated maximum entropy regularization schemes to improve model calibration under domain shift on two publicly available datasets: the WMH Segmentation Challenge and the 3D-MR-MS dataset. Performance is assessed with Dice coefficient, Hausdorff distance, expected calibration error, and entropy-based uncertainty estimates. Entropy-based uncertainty estimates can anticipate segmentation errors, both in-distribution and out-of-distribution, with maximum-entropy regularization further strengthening the correlation between uncertainty and segmentation performance, while also improving model calibration under domain shift. Maximum-entropy regularization improves uncertainty estimation for WMH segmentation under domain shift. By strengthening the relationship between predictive uncertainty and segmentation errors, these methods allow models to better flag unreliable predictions without requiring ground-truth annotations. Additionally, maximum-entropy regularization contributes to better model calibration, supporting more reliable and safer deployment of deep learning models in multi-center and heterogeneous clinical environments.

We present the first comparative study of two fundamentally distinct feature extraction techniques: Histogram of Oriented Gradients (HOG) and Topological Data Analysis (TDA), for medical image classification using retinal fundus images. HOG captures local texture and edge patterns through gradient orientation histograms, while TDA, using cubical persistent homology, extracts high-level topological signatures that reflect the global structure of pixel intensities. We evaluate both methods on the large APTOS dataset for two classification tasks: binary detection (normal versus diabetic retinopathy) and five-class diabetic retinopathy severity grading. From each image, we extract 26244 HOG features and 800 TDA features, using them independently to train seven classical machine learning models with 10-fold cross-validation. XGBoost achieved the best performance in both cases: 94.29 percent accuracy (HOG) and 94.18 percent (TDA) on the binary task; 74.41 percent (HOG) and 74.69 percent (TDA) on the multi-class task. Our results show that both methods offer competitive performance but encode different structural aspects of the images. This is the first work to benchmark gradient-based and topological features on retinal imagery. The techniques are interpretable, applicable to other medical imaging domains, and suitable for integration into deep learning pipelines.