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Latest News & Trends
Radiology Partners Urges FDA for Clarity on Imaging AI Regulations
Radiology Partners and Mosaic Clinical Technologies petition the FDA for clearer regulatory guidelines on imaging AI vision-language models.
AI Finds Hidden Aortic Stenosis in Routine Chest CTs
AI algorithms can identify undiagnosed aortic stenosis on routine chest CT scans, potentially improving early detection and patient care.
FDA Solicits Public Input on Regulating Generative AI in Medical Devices
The FDA is seeking public feedback on how to regulate generative AI in medical devices, recognizing both its promise and risks.
From the Research Hub
A novel medical image segmentation network for colorectal polyp small targets and fuzzy boundaries.
To address the challenges of complex feature variations and unclear boundary definitions between segmented targets and surrounding regions in medical images, a novel segmentation model based on Deformable Large Kernel Convolutional Attention (D-LKA) and Transformer is proposed. The model first uses Vision Transformer as the encoder to enhance the ability to capture global information, overcoming the limitations of convolutional neural networks' receptive field. In the decoder, a D-LKA decoder with deformable large kernel convolution attention is used, allowing the model to adapt to complex target features. Finally, the TRR module is introduced to coordinate information transfer between the convolutional neural network and Transformer, reducing semantic loss. The model is trained, validated, and tested on the Kvasir-SEG colon polyp dataset, with multiple ablation experiments. To validate generalization, experiments are also conducted on the CVC-ClinicDB dataset. Experimental results show that TDU-Net outperforms other methods in both segmentation accuracy and generalization. TDU-Net achieves excellent segmentation results, addressing small target and incomplete feature extraction issues, significantly improving clinical diagnosis efficiency and accuracy.
Deep-Learning Based Contrast Boosting: A Multi-Center Multi-Reader Study on Clinical Performance With Standard Contrast Enhanced Brain MRI.
Gadolinium-based contrast agents are used in brain MRI to improve the visualization of disorders and improve the delineation of lesions. Higher doses of GBCAs can improve lesion sensitivity but may have safety implications, particularly in light of recent findings on gadolinium retention and deposition. To evaluate the clinical performance of an FDA-cleared deep-learning (DL)-based contrast boosting algorithm in routine clinical brain MRI exams. Retrospective. One hundred ten patients (47 ± 22 years; 52 Females, 47 Males, 11 N/A) with clinical contrast-enhanced brain MR studies. T1 weighted pre-contrast and post-contrast brain MRI sequences at 0.3 T, 1.5 T, and 3 T. A multi-center database of contrast-enhanced brain MR images was used to evaluate a DL-based contrast boosting algorithm. Pre-contrast and standard post-contrast (SC) images were processed with the algorithm to obtain contrast boosted (CB) images. CB images were compared to SC images in terms of contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP). Three board-certified radiologists with 9, 15, and 18 years of experience reviewed CB and SC images side-by-side for qualitative evaluation and rated them on a 4-point Likert scale for lesion contrast enhancement, border delineation, internal morphology, overall image quality, presence of artifacts, and changes in vessel conspicuity. The presence, cause, and severity of any false lesions was recorded. Wilcoxon signed rank test. A p value < 0.05 was considered significant. CB images had significantly superior quantitative performance than SC images in terms of CNR (729.17% ± 1576.92%), LBR (87.91% ± 72.12%), and CEP (165.81% ± 42%). In the qualitative assessment, CB images showed significantly better lesion visualization (3.73 vs. 3.16) and had significantly better image quality (3.55 vs. 3.07). In this multi-center, multi-reader study, deep learning-based contrast boosting demonstrates robust improvements in lesion visualization and image quality without increasing contrast dosage. 4. Stage 3.
Clinical Pathways Matter for Multimodal Deep Learning in Early Alzheimers Disease Detection
Identifying individuals at risk of Alzheimer's disease (AD), particularly in the preclinical and early stages, remains challenging. Although deep learning approaches based on structural MRI show promise as a non-invasive biomarker, existing multimodal models require task-specific training and depend on biomarkers that are not routinely available in clinical practice. Here, we propose a zero-shot multimodal framework based on SigLIP that combines structural MRI embeddings with text embeddings of routinely collected clinical variables for early AD risk stratification in individuals at preclinical or mild cognitive impairment (MCI) stages. We evaluated the approach in 416 individuals from the ADNI cohort (age: 72.73 +- 6.7). SigLIP was used without fine-tuning to extract MRI and clinical text embeddings, which were combined into multimodal representations for individual-level AD risk prediction within 4 years. We further compared the model performance in a single-visit and two-visit settings to assess the value of longitudinal information and framework scalability. In the single-visit setting, combining MRI embeddings with MMSE, age, and sex achieved an AUC of 0.91 +- 0.02, outperforming both a CSF A\b{eta}42-based model (AUC 0.73 +- 0.08) and an MMSE-based model (AUC 0.85 +- 0.22). In the two-visit setting, performance was maintained or improved, supporting the scalability of the approach to longitudinal data. These findings suggest that zero-shot multimodal fusion of structural MRI and routinely collected clinical variables may provide a practical and scalable strategy for early AD risk stratification without task-specific retraining.
From the RadAI FDA Approvals Database
Mediaire GmbH
mdprostate
mdprostate is a software tool that uses AI to help analyze radiological images, providing automated assessments that can assist clinicians in diagnosing and managing prostate-related conditions.
Fujifilm Corporation
EW10-EC02 Endoscopy Support Program
The EW10-EC02 Endoscopy Support Program is a software tool designed to assist clinicians during gastrointestinal endoscopy by detecting possible lesions. It helps clinicians identify abnormalities in real-time, potentially improving accuracy and efficiency in endoscopic procedures.
Overjet, Inc.
Overjet Multi-Image Caries and Charting Assist
This device is an AI-powered tool designed to assist dentists by analyzing dental radiographs (X-rays) to detect cavities (caries) and support dental charting. It helps clinicians make more informed decisions and improves the efficiency and accuracy of dental image interpretation.
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