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RadNet AI Tool Reduces Unnecessary Prostate Biopsies by 19%
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Everlywell Launches Direct-to-Consumer Mammography AI for Breast Cancer Risk
Everlywell now offers the FDA-authorized Clairity Breast AI tool for patients to assess breast cancer risk from their mammograms.
MSKAI Unveils AI Tool for Embryo Cell Analysis and Imaging-Driven Cancer Insights
MSK researchers employ AI and advanced imaging to reveal cancer mechanisms and track embryonic development at the single-cell level.
From the Research Hub
Subtraction iodine maps combined with deep learning reconstruction for improved evaluation of bony invasion in temporal bone lesions.
To assess the capability of subtraction iodine maps (SIM) combined with deep learning reconstruction (DLR) to evaluate the identification of bone involvement in patients with temporal bone lesions, compared with conventional enhanced CT with hybrid iterative reconstruction (HIR). This prospective study continuously recruited seventy-seven patients (mean age: 46.1 ± 18.9 years; 35 males) with temporal bone lesions who underwent enhanced temporal bone CT scans. Pre-contrast and contrast-enhanced phase images were reconstructed using HIR and DLR. The colored SIM fusion images for evaluation were generated by subtracting the pre-contrast image from the contrast-enhanced image to obtain the SIM, which was then overlaid onto the bone window image. Qualitative scores and quantitative parameters were computed and compared among four groups of images: Enhanced-HIR, Enhanced-DLR, SIM-HIR, and SIM-DLR. Quantitative analysis demonstrated that SIM-DLR significantly reduced noise (lower SD values) and increased CNR of the lesion compared to Enhanced-HIR (both p < 0.001). Qualitatively, SIM-DLR exhibited significantly higher scores than conventional Enhanced-HIR in overall image quality, temporal bone lesion detection confidence, lesion contour delineation capability, and temporal bone involvement visualization (all p < 0.001), with high inter-reader agreement (kappa: 0.812-0.904). Enhancement-dependent analysis revealed that SIM showed markedly beneficial scores of lesion detection, contour delineation, and bone involvement visualization in high-enhancement lesions, compared to low-enhancement lesions (all p < 0.05). Compared to conventional Enhanced-HIR, SIM-DLR significantly enhanced temporal bone CT imaging by improving image quality, anatomical delineation, and bone involvement visualization, especially for hypervascular lesions. Question Accurately assessing bony invasion by temporal bone lesions is challenging on conventional enhanced CT due to poor lesion-to-bone contrast. Findings SIM combined with DLR improve image quality, lesion delineation, and visualization of bone involvement compared to conventional CT. Clinical relevance This technique enhances preoperative evaluation of temporal bone lesions, particularly hypervascular pathologies, aiding surgical planning and potentially improving treatment precision and outcomes.
A Bayesian-Optimized Weighted Fuzzy Rank-Based Ensemble for Coronary Stenosis Detection in X-ray Angiography.
X-ray coronary angiography is the gold standard for diagnosing coronary artery disease, a leading cause of global mortality. Existing artificial intelligence methods typically detect stenosis from individual frames or rely on manually selected key frames, despite angiography being inherently a video-based examination in which diagnostically relevant information appears only in a subset of frames. Moreover, conventional ensemble methods generally employ fixed classifier weights, limiting their ability to account for uncertainty and variations in classifier performance. To address these challenges, we propose a framework that combines automatic key-frame selection with a novel weighted fuzzy rank-based ensemble model for right coronary artery (RCA) stenosis detection. The framework employs a semi-supervised key-frame selection strategy using ResNet50 or ViT-B/16 as feature extractors together with a hybrid loss function for vessel segmentation. The selected key frames are then classified using a weighted fuzzy rank-based ensemble, which extends conventional fuzzy rank-based fusion by incorporating learnable classifier weights optimized through Bayesian optimization. Video-level predictions are obtained via majority voting over key-frame classifications, enabling clinically meaningful sequence-level assessment. The proposed method was evaluated on the publicly available AngioCAD dataset and further validated on the independent CADICA dataset. Experimental results demonstrate a video-level precision of 84.63%, F1-score of 80.44%, ROC-AUC of 70.06%, and PR-AUC of 81.83% on AngioCAD, while maintaining consistent performance on the external dataset, indicating good generalizability. The proposed framework may assist cardiologists in the rapid and reliable identification of coronary stenosis and support clinical decision-making.
Planner-Executor Style Multimodal Agentic System to Answer Patient Questions in Lung Cancer Screening CT
BackgroundAccurate patient understanding of lung cancer screening (LCS) results is critical for engagement and follow-up adherence, given the currently low screening uptake. While artificial intelligence (AI) systems show promise in facilitating patient communication, its effective use in clinical settings requires appropriate invocation of imaging tools and self-regulation by deferring certain questions to physicians. To this end, we developed and evaluated a planner-executor style multimodal agentic system to answer simulated patient questions about LCS CT results. MethodsThis retrospective study utilized 116 LCS CT reports and images collected from a tertiary academic hospital. The system employed a multi-agent architecture (planner and executor) to mimic clinical reasoning. We developed a large language model (LLM)-based question generation framework to prepare a comprehensive set of 699 simulated patient questions, categorized as answerable and defer-to-doctor to assess self-regulation. Performance was evaluated on tool-calling, self-regulation accuracy (ability to correctly defer out-of-scope questions to a human provider) assessed by LLM-as-a-judge, and clinical quality assessed by a reader performance study with four physicians evaluating a subset of 100 responses. ResultsThe system demonstrated high overall tool-calling accuracy of 93.1% (646/694; 95% CI: 91.2%, 95.0%) and self-regulation accuracy of 92.6% (462/499; 90.2%, 94.8%). For answerable questions, the percentage of responses receiving perfect 5.0 scores across all readers included 78.9% (180/228) for clinical accuracy (inter-rater agreement: Gwets AC2=0.92), and 73.2% (167/228) for patient understandability (0.86). For defer-to-doctor questions, the percentage of responses included 77.3% (133/172) for clinical accuracy (0.80) and 70.3% (121/172) for patient understandability (0.78). ConclusionsThe planner-executor style multi-modal agentic system reliably answers patient-specific questions about LCS. Its high tool-calling and self-regulation performance demonstrates its potential as a safe and effective digital communication facilitator in LCS programs.
From the RadAI FDA Approvals Database
Aevice Health Pte. , Ltd.
Aevice Wheeze Detection Module
The Aevice Wheeze Detection Module is a software tool that uses advanced algorithms to analyze lung sound recordings from compatible electronic stethoscopes. It helps healthcare professionals identify recordings where wheezing, an abnormal lung sound suggestive of respiratory issues, is suspected. The tool acts as a decision support system and aims to improve the evaluation of patients' lung health, particularly in both children and adults, by supporting clinicians in detecting possible respiratory problems.
CapsoVision, Inc.
CapsoView (CVV); CapsoCloud (CLD)
CapsoView and CapsoCloud are software applications that use artificial intelligence to help doctors review video images from capsule endoscopes. The AI features help highlight potential abnormalities, identify digestive tract locations, and hide frames that cannot be interpreted, all with the goal of making the review process faster and easier for clinicians.
Philips Ultrasound, LLC
EPIQ Inspira Series Diagnostic Ultrasound System; Affiniti Series Diagnostic Ultrasound System
The Philips EPIQ Inspira and Affiniti Series are advanced ultrasound systems used for imaging many parts of the body. A key AI-powered feature, 2D Auto LA, helps clinicians quickly and accurately measure the size and function of the left atrium in the heart during routine echocardiography exams, supporting efficient and consistent cardiac assessments while allowing clinicians to review and adjust results.
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