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ACR Forms Panel to Tackle Economics of Imaging AI Reimbursement
The American College of Radiology has created a committee to address reimbursement challenges for AI in medical imaging.
Radiology Partners Unveils Mosaic: AI-Powered Platform for Radiology Workflow
Radiology Partners launches Mosaic Clinical Technologies, aimed at streamlining radiology workflows using AI.
Deep Learning Model Enables 3D Handheld Photoacoustic-Ultrasound Imaging Without Sensors
Pusan National University develops MoGLo-Net, an AI model that reconstructs 3D images from handheld 2D photoacoustic and ultrasound scans without external sensors.
From the Research Hub
Automated Whole-Liver Fat Quantification with Magnetic Resonance Imaging-Derived Proton Density Fat Fraction Map: A Prospective Study in Taiwan.
Magnetic resonance imaging (MRI) with a proton density fat fraction (PDFF) sequence is the most accurate, noninvasive method for assessing hepatic steatosis. However, manual measurement on the PDFF map is time-consuming. This study aimed to validate automated whole-liver fat quantification for assessing hepatic steatosis with MRI-PDFF. In this prospective study, 80 patients were enrolled from August 2020 to January 2023. Baseline MRI-PDFF and magnetic resonance spectroscopy (MRS) data were collected. The analysis of MRI-PDFF included values from automated whole-liver segmentation (autoPDFF) and the average value from measurements taken from eight segments (avePDFF). Twenty patients with ≥10% autoPDFF values who received 24 weeks of exercise training were also collected for the chronologic evaluation. The correlation and concordance coefficients (r and ρ) among the values and differences were calculated. There were strong correlations between autoPDFF versus avePDFF, autoPDFF versus MRS, and avePDFF versus MRS (r=0.963, r=0.955, and r=0.977, all p<0.001). The autoPDFF values were also highly concordant with the avePDFF and MRS values (ρ=0.941 and ρ=0.942). The autoPDFF, avePDFF, and MRS values consistently decreased after 24 weeks of exercise. The change in autoPDFF was also highly correlated with the changes in avePDFF and MRS (r=0.961 and r=0.870, all p<0.001). Automated whole-liver fat quantification might be feasible for clinical trials and practice, yielding values with high correlations and concordance with the time-consuming manual measurements from the PDFF map and the values from the highly complex processing of MRS (ClinicalTrials.gov identifier: NCT04463667).
Deep Learning Applications in Lymphoma Imaging.
Lymphomas are a diverse group of disorders characterized by the clonal proliferation of lymphocytes. While definitive diagnosis of lymphoma relies on histopathology, immune-phenotyping and additional molecular analyses, imaging modalities such as PET/CT, CT, and MRI play a central role in the diagnostic process and management, from assessing disease extent, to evaluation of response to therapy and detecting recurrence. Artificial intelligence (AI), particularly deep learning models like convolutional neural networks (CNNs), is transforming lymphoma imaging by enabling automated detection, segmentation, and classification. This review elaborates on recent advancements in deep learning for lymphoma imaging and its integration into clinical practice. Challenges include obtaining high-quality, annotated datasets, addressing biases in training data, and ensuring consistent model performance. Ongoing efforts are focused on enhancing model interpretability, incorporating diverse patient populations to improve generalizability, and ensuring safe and effective integration of AI into clinical workflows, with the goal of improving patient outcomes.
Multivariate whole brain neurodegenerative-cognitive-clinical severity mapping in the Alzheimer's disease continuum using explainable AI
Neurodegeneration and cognitive impairment are commonly reported in Alzheimers disease (AD); however, their multivariate links are not well understood. To map the multivariate relationships between whole brain neurodegenerative (WBN) markers, global cognition, and clinical severity in the AD continuum, we developed the explainable artificial intelligence (AI) methods, validated on semi-simulated data, and applied the outperforming method systematically to large-scale experimental data (N=1,756). The outperforming explainable AI method showed robust performance in predicting cognition from regional WBN markers and identified the ground-truth simulated dominant brain regions contributing to cognition. This method also showed excellent performance on experimental data and identified several prominent WBN regions hierarchically and simultaneously associated with cognitive declines across the AD continuum. These multivariate regional features also correlated with clinical severity, suggesting their clinical relevance. Overall, this study innovatively mapped the multivariate regional WBN-cognitive-clinical severity relationships in the AD continuum, thereby significantly advancing AD-relevant neurobiological pathways.
From the RadAI FDA Approvals Database
Philips Medical Systems Nederland B.V.
Achieva; Intera; Ingenia 1.5T; Ingenia 3.0T; Ingenia 1.5T CX; Ingenia 3.0T CX; Ingenia Elition S; Ingenia Elition X; Ingenia Ambition S; Ingenia Ambition X; and MR 5300 MR Systems
This product is a family of Philips MRI systems designed to produce detailed images of the body's internal structures. These MRI machines help doctors and clinicians by providing high-quality images that assist in diagnosing a variety of medical conditions across different body regions without using ionizing radiation.
ACCUTOME, INC. Doing Business As Keeler USA
B-Scan
The B-Scan device by ACCUTOME, INC. is an ultrasound imaging system that uses pulsed echo technology to capture images inside the body. It assists clinicians by providing detailed images useful for diagnosis and treatment planning in radiology.
Olympus Medical Systems Corporation
ULTRASONIC PROBE UM-S20-17S (UM-S20-17S); ULTRASONIC PROBE UM-S20-20R (UM-S20-20R)
This product consists of ultrasonic probes used in diagnostic imaging. These probes emit and receive ultrasound waves to create images of the body's internal structures, helping clinicians visualize and assess patients non-invasively.
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