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Dynamic AI Models Provide Early Disease Warnings from Health Data
AI-driven dynamic models may predict disease tipping points earlier by analyzing changes in health data, including imaging.
LLM Boosts Accuracy and Clarity of Patient Radiology Report Translations
A study found GPT-o1 effectively simplified and accurately translated emergency radiology reports into multiple languages, outperforming Google Translate.
Mount Sinai Develops AI Model to Personalize CPAP's Cardiovascular Impact
Mount Sinai has developed a machine learning model forecasting the cardiovascular risk impact of CPAP in obstructive sleep apnea patients.
From the Research Hub
The study of nomogram model based on CT radiomics and clinical features for histological classification of parotid gland tumors.
Accurate preoperative identification of pathological types of parotid tumors is essential for the formulation of treatment decisions. The study aims to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors, and evaluate the effectiveness of different models in the classification of parotid gland tumors. A total of 427 patients with parotid gland tumors were randomly divided into a training set and a test set at a ratio of 7:3. Radiomic features were selected using the ANOVA and the LASSO regression. Three-step machine learning models were constructed using three common classifiers (LR, SVM and XGBoost) to classify the parotid gland tumors into four subtypes. The radiomics signature was constructed using the optimal radiomics model, and a radiomics score (Rad-score) was calculated. Clinical data and CT features were evaluated to build a clinical factor model. A radiomics nomogram incorporating the independent clinical factors and Rad-score was constructed. The evaluation of those models’ performance was executed by using receiver operator characteristics (ROC) curves (AUC) and calibration curves, and the clinical usefulness of these models was evaluated by decision curve analysis (DCA). In each step of the three-step procedure, twenty-seven, twelve, and thirteen valuable features were selected, respectively. And the radiomics model based on the LR, SVM, and LR classifiers obtained the highest AUC in differentiating BPGTs from MPGTs (AUC = 0.838), PA from WT & BCA (AUC = 0.847), and WT from BCA (AUC = 0.870), respectively. The nomogram, which combined the optimal radiomics model and independent clinical factors, achieved an improved classification performance (BPGTs vs. MPGTs, AUC = 0.849; PA vs. WT & BCA, AUC = 0.873; WT vs. BCA, AUC = 0.925). The calibration curve and the DCA demonstrated that the combined nomogram showed superior predictive performance than radiomics model and clinical factor model. The proposed nomogram of radiomics combined with clinical models has high clinical value for the preoperative classification of parotid gland tumors, which might hold promise in assisting clinicians in the exact preoperative diagnosis and formulation of personalized treatment strategy. The online version contains supplementary material available at 10.1038/s41598-026-46970-4.
Computerized diagnosis of brain tumor using graph based CNN classification.
Magnetic resonance imaging (MRI) is essential in the accurate diagnosis of brain tumors so that sound treatment planning can be done, however, clinical evaluation frequently depends on the subjective interpretation of the expert, which is time consuming. Current computer-aided diagnosis strategies that rely entirely on traditional Convolutional Neural Networks (CNNs) mainly acquire local spatial information and pixel grids and could not specifically represent structural associations and heterogeneity inside the tumor regions. Equally, most graph-based methods fail to directly use segmentation-based tumor structure within a unified learning structure. To overcome such limitations, in this study, a four-stage classification framework is proposed based on MRI images of the publicly accessible Kaggle dataset on the topic of Brain Tumor. The suggested method conducts adaptive bilateral filtering of the noise and edge enhancement and then conducts semantic segregation to isolate the tumorareas. Structural boundary information is coded as the Histogram of Oriented Gradients (HOG) descriptors are obtained in the areas that have been segmented. These region-level representations are then learned as nodes of a tumor-aware graph where the relationship between spatial and feature similarities is represented as edges and are labeled using a hybrid Graph Neural Networkconvolutional Neural Network (GNN-CNN) model, which combines learning similarities between relational graphs with convolutional features generation. The proposed framework includes inter-region dependencies, in addition to local texture cues, which increase the discrimination between benign and malignant tumors. Experimental performance on the Kaggle dataset shows high performance with 98.5% accuracy, 98.5% precision, 97.8% recall, and 97.8% F1-score, which indicates the efficiency and performance of the proposed algorithm in the classification of brain tumors in an automated manner.
Local and Global Patterns Support Medical Imaging as a Biomarker of Ageing
AbstractO_ST_ABSBackgroundC_ST_ABSUnderstanding human ageing across multiple organs is essential for characterising individual health trajectories and identifying abnormal ageing processes. Multiorgan imaging provides an opportunity to quantify biological ageing beyond chronological age. The aim of this study is to assess organ-specific and whole-body ageing patterns and their associations with disease and lifestyle factors. MethodsIn this large-scale study, we evaluate biological ageing patterns using 70,000 MRI scans from the UK Biobank and the German National Cohort. We employ 3D ResNet-18 models to predict chronological age from various body regions (brain, heart, liver, spine, lungs, muscle, and intestine) and the whole body. From these predictions, we derive "age gaps" relative to a strictly healthy reference cohort, which enables the identification of accelerated ageing patterns. We then evaluate associations with chronic diseases and lifestyle factors, and a virtual ageing framework was developed to explore counterfactual scenarios by substituting anatomical regions across subjects, quantifying local impacts on global biological age. ResultsHere we show significant associations between detected accelerated ageing and specific chronic diseases, including multiple sclerosis and chronic obstructive pulmonary disease, as well as lifestyle factors such as smoking and physical activity. Virtual substitution of anatomical regions demonstrates that local substitutions can influence global ageing patterns. ConclusionsThis study demonstrates that multi-organ imaging enables the detection of abnormal ageing patterns at both local and global levels. The presented framework provides a foundation for improved risk stratification and supports the development of personalised approaches to health assessment and disease prevention. Plain Language SummaryAs people age, different organs in the body might not always age at the same pace. Understanding these differences can help to explain a persons health and why they develop diseases earlier than others. In this study, we measure how ageing varies across the body using medical images. We analysed about 70,000 whole-body scans from large population studies in the United Kingdom and Germany. Using AI models, we estimated a persons biological age from images of different organs and compared it with their actual age. We found that faster ageing in specific organs is linked to certain diseases (such as multiple sclerosis) and lifestyle factors (like smoking and physical activity). These findings may help improve early disease detection and support more personalised approaches to health and ageing in the future.
From the RadAI FDA Approvals Database
Automated Imaging Diagnostics, LLC
neuropacs
Neuropacs is an AI software product designed to assist clinicians by analyzing neurological medical images, potentially helping in the diagnosis or assessment of neurological conditions. It aids healthcare professionals in interpreting images faster and more accurately.
Anumana, Inc.
ECG-AI Pulmonary Hypertension (PH) 12-Lead algorithm (1020)
ECG-AI Pulmonary Hypertension 12-Lead algorithm is an AI-based software that analyzes 12-lead ECG data to help detect pulmonary hypertension, supporting clinicians in diagnosis and patient management related to heart and lung vascular conditions.
Butterfly Network, Inc.
Butterfly Gestational Age Tool
The Butterfly Gestational Age Tool is an ultrasound-based AI system designed to help clinicians determine the gestational age of a fetus. By analyzing ultrasound data with AI, it aids in providing accurate and timely information to support prenatal care.
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