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AI Framework Accelerates Aortic Aneurysm Risk Prediction from Imaging
Researchers developed BioPINN-LM, combining physics-informed neural networks and multimodal large language models to deliver fast, interpretable risk assessments for ascending thoracic aortic aneurysms.
AI-Driven Whole-Body MRI Biomarkers Aid Myeloma Risk Stratification
AI-powered MRI analysis offers automated risk stratification via body composition in multiple myeloma patients.
AI Targets Radiology Residents’ Training Gaps for Personalized Learning
Artificial intelligence can identify gaps in radiology residents' clinical exposure, supporting tailored educational interventions.
From the Research Hub
CT-based cross-center risk stratification of pure ground-glass nodules incorporating domain style-aware correction.
BackgroundPure ground-glass nodules (pGGNs) are increasingly detected on chest CT. Although most exhibit indolent behavior, a subset may harbor invasive features before obvious radiological progression, making early risk stratification clinically important.PurposeTo develop and externally validate a CT-based deep learning model with domain adaptation for cross-center risk stratification of pGGNs.Material and MethodsThis retrospective multicenter study included 1235 surgically confirmed pGGNs from two institutions. A deep learning model incorporating domain style-aware correction was developed to differentiate AAH/AIS-like lesions from MIA/IAC-like lesions using preoperative CT images. Data from one center were used for training and internal validation, and an independent external cohort was used for validation. Model performance was evaluated using AUC, accuracy, calibration, and decision curve analysis.ResultsThe model achieved an AUC of 0.93 and an accuracy of 90.4% in the internal validation set. In the external validation set, the AUC was 0.88 with an accuracy of 81.7%, demonstrating good cross-center generalization. Domain adaptation improved external AUC from 0.85 to 0.88. Calibration analysis showed good agreement between predicted and observed probabilities. Decision curve analysis indicated higher net benefit compared with treat-all and treat-none strategies across a range of thresholds.ConclusionThe proposed model demonstrates robust performance across centers and may serve as a noninvasive tool to support individualized surveillance strategies for patients with pGGNs.
From Volumetrics to 3D Tensors: A Multi-Cohort Evaluation of Machine Learning and Deep Learning for Alzheimer's Classification.
Automated Alzheimer's disease (AD) classification from structural MRI typically employs either feature-engineered machine learning (ML) or end-to-end 3D deep learning (DL). Addressing a lack of rigorous statistical comparison and multi-dataset validation in current literature, this study evaluates classical ML algorithms utilizing volumetric and voxel-based morphometry (VBM) features against 3D CNNs processing raw MRI tensors. Using the ADNI and OASIS datasets, models underwent internal 4-fold cross-validation and zero-shot cross-cohort testing to assess predictive performance, domain shift resilience, and clinical sensitivity. Welch's t-tests confirmed that 16 of 19 macro-regional brain aspects differed significantly between AD and cognitively normal subjects ( <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><msub><mi>p</mi> <mtext>FDR</mtext></msub> <mo><</mo> <mn>0.05</mn></mrow> </math> ), led by the medial-temporal lobe. While DL architectures achieved marginal numerical superiority in peak accuracy on ADNI (DenseNet121: accuracy <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0.92</mn> <mo>±</mo> <mn>0.02</mn></mrow> </math> , F1 <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0.85</mn> <mo>±</mo> <mn>0.04</mn></mrow> </math> , ROC-AUC <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0.96</mn> <mo>±</mo> <mn>0.02</mn></mrow> </math> ), they exhibited higher variance and lacked statistical significance compared to optimized ML baselines (XGBoost-VOL: F1 <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0.82</mn> <mo>±</mo> <mn>0.03</mn></mrow> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi> <mo>≥</mo> <mn>0.28</mn></mrow> </math> for all 3D CNNs). On the highly imbalanced OASIS dataset, VBM-enhanced Logistic Regression matched top DL models in overall F1-score ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0.68</mn> <mo>±</mo> <mn>0.05</mn></mrow> </math> ) while delivering statistically superior AD recall ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mn>0.67</mn> <mo>±</mo> <mn>0.04</mn></mrow> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi> <mo>=</mo> <mn>0.0026</mn></mrow> </math> versus baseline), exceeding every 3D CNN. Although both paradigms generalized robustly across cohorts (only a 2-4% zero-shot F1 reduction; ROC-AUC up to 0.9506), the findings highlight a crucial clinical trade-off: 3D CNNs autonomously extract complex spatial features, but simpler, interpretable ML models provide superior inferential stability and diagnostic sensitivity, making them highly viable for real-world deployment.
Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial
Training deep learning-based medical image segmentation models is challenging with limited curated datasets. For AGITG TOPGEAR, a gastric cancer trial, the Clinical Target Volume (CTV) is complex and defined by multiple anatomical landmarks, making upfront training data preparation difficult for an automated contour QA segmentation model. We investigate anatomical priors, derived from surrounding organ segmentations, to provide spatial context and improve TOPGEAR CTV segmentation accuracy. We also evaluate active learning, iteratively expanding the training dataset by selecting cases expected to improve performance. One hundred TOPGEAR CT scans were retrospectively analyzed. An initial set of 10 expert-contoured cases was used to train an nnU-Net model. TotalSegmentator generated a voxel-wise anatomical prior map from surrounding structures as an additional input channel. Active learning was simulated over four iterations, selecting cases by model uncertainty and segmentation performance. All models used five-fold cross-validation for an ensemble uncertainty measure. Evaluation used a hold-out testing set of 50 cases. The anatomical prior improved CTV segmentation accuracy, increasing mean Dice Similarity Coefficient (DSC) from 0.84 to 0.86. Active learning similarly improved performance to 0.86, with greatest benefit in the final round. Combining the anatomical prior with active learning achieved the highest accuracy, with a DSC of 0.87. Model uncertainty correlated with DSC, supporting its use in identifying suboptimal predictions and guiding active learning. Anatomical priors and active learning each improved CTV segmentation accuracy and generalizability, with their combination achieving the best performance, supporting integration into segmentation model development for automated contour QA in radiotherapy clinical trials.
From the RadAI FDA Approvals Database
Radformation, Inc.
AutoContour (RADAC V6)
AutoContour (RADAC V6) is a software tool used to assist radiation therapy planning by automatically processing radiological images to define anatomical structures, helping clinicians to more efficiently and accurately plan treatments for cancer patients.
Screenpoint Medical B.V.
Transpara (2.1.0)
Transpara is an AI-based software that helps radiologists detect and diagnose possible cancerous lesions in medical images, improving the accuracy and speed of cancer screening and diagnosis.
ADVAHEALTH SOLUTIONS, LLC
AdvaView
AdvaView is a radiological image processing system designed to assist in the viewing and analysis of medical images, helping clinicians interpret radiology scans more efficiently.
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