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SIIM 2026 AI Landscape
Analysis of SIIM 2026 sessions, themes, speaker mix, and abstract activity through the lens of imaging AI.
ECR 2026 AI Product Landscape
A launch-level view of ECR 2026 product announcements: AI share, product mix, and modality focus.
ECR 2026 AI Industry Landscape
A data-driven map of ECR 2026 exhibitors: AI share, vendor mix, and regional patterns.
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FDA Approvals Database
Latest News & Trends
FDA Approves SimBioSys AI Tool for Breast Cancer Risk via MRI
SimBioSys received FDA clearance for TumorSight Risk, an AI tool assessing breast cancer recurrence risk using MRI.
Penn Medicine's Percival AI Detects Hidden Health Risks in CT Scans
Penn Medicine's Percival AI leverages CT scans to uncover unseen health risks and disease patterns, expanding the utility of radiological imaging.
LLMs Rarely Recommend Unnecessary Imaging, Except for Thyroid Ultrasound
Large language models seldom over-recommend imaging, but thyroid ultrasound guidance shows substantial discrepancies.
From the Research Hub
Artificial Intelligence-Assisted Ultrasound for Sarcopenia Assessment: A Scoping Review.
Muscle ultrasound offers a portable, non-invasive, and cost-effective option for sarcopenia assessment, but operator dependency limits its standardization. Artificial intelligence (AI) may help automate and improve ultrasound-based muscle evaluation. This scoping review aims to map the current landscape of AI-assisted ultrasound for sarcopenia assessment, including research trends, methods, clinical uses and performance, and to suggest future work. Following Joanna Briggs Institute guidelines and the PRISMA-ScR checklist, we searched PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, ProQuest, CNKI, WanFang Data, Yiigle, VIP, and Sinomed up to July 25, 2026. Two researchers independently selected studies and extracted data. Thirty studies were included. This field is developing rapidly, with 76.7% of studies published in the last 3 years, primarily from Spain and China. Model development and validation studies (43.3%) were most common. U-Net and its variants were the predominant AI architectures, mainly for image segmentation. B-mode ultrasound with high-frequency linear probes was frequently used. The rectus femoris was the most assessed muscle for sarcopenia (73.3%). AI-integrated models for sarcopenia diagnosis achieved AUCs of 0.640-0.950. Reported Dice coefficients for muscle segmentation ranged from 0.73 to 0.99. In-house datasets were used in 66.7% of the studies, and global classification labels were the most frequent annotation strategy (46.7%). AI-assisted ultrasound shows potential for objective and scalable sarcopenia assessment but remains early-stage. Operator dependency in image acquisition has not been fully resolved. Further investigation should determine the added value of integrating multimodal imaging, combining diverse AI techniques, and evaluating multiple muscle groups. Future efforts should deepen image-processing strategies to extract clinically relevant features and facilitate standardized, scalable implementation in clinical practice.
Accuracy of transfer learning in predicting peri-implantitis onset from intraoral radiograph: A multi-center longitudinal retrospective study.
The growing application of artificial intelligence in dentistry has opened new opportunities for predicting peri-implantitis (PI) using convolutional neural networks (CNNs). Transfer learning, where knowledge from a model pre-trained on a large dataset is adapted to a new, domain-specific task, may enhance performance, particularly when medical datasets are limited. This study evaluated the ability of several pre-trained CNN architectures applied to baseline 2D intraoral radiographs to predict peri-implantitis onset. A retrospective multi-center study was conducted at the University of Michigan and King's College London. A total of 195 implants (146 from Michigan and 49 from London) placed in 108 maintained patients were included. For each implant, a baseline periapical radiograph taken up to one year after prosthetic restoration was analyzed. Five pre-trained CNN architectures (ResNet50, DenseNet, VGG16, InceptionV3, EfficientNet) were combined with four transfer learning strategies: feature extraction (hybrid and non-hybrid), partial fine-tuning (50% of layers), and full fine-tuning. Models were trained using patient-wise splits to avoid leakage, and performance was evaluated on an independent test set using accuracy, area under the ROC curve (AUC), sensitivity, and specificity. All CNN architectures showed variable performance across transfer learning strategies. The non-hybrid feature extraction approach provided the most consistent discrimination, with the best models achieving AUC values between 0.74 and 0.83. InceptionV3 reached the highest AUC (0.83). Fine-tuning strategies yielded modest or low performance, with AUCs generally ranging from 0.42 to 0.69 across architectures. Non-hybrid feature extraction applied to pre-trained CNNs showed the most promising, though moderate, performance for predicting PI onset from baseline intraoral radiographs. These findings indicate that transfer learning may assist early risk stratification in implant dentistry, but larger prospective studies are needed before clinical adoption. Deep learning models may serve as adjunctive tools to identify implants at higher risk for future PI based on routine baseline radiographs, supporting more individualized maintenance protocols and earlier preventive interventions.
Registration is a Representation Problem: Solving Whole-Body PET/CT Registration
Raw image intensities are a poor surrogate for similarity across images in correspondence problems. Further, while auxiliary segmentation labels can provide semantic identification and improve matching, they can assign only coarse identities to fine-grained anatomy and often cover only a subset of structures, particularly in whole-body settings. We present an approach that instead establishes correspondence using complementary representations of anatomy and demonstrate its utility for longitudinal whole-body PET/CT registration in the Learn2Reg 2026 PSMAReg Challenge. We use our 3D foundation model, anatomix, to derive fine-grained, per-voxel semantic representations that are consistent across volumes and further employ off-the-shelf segmentation models to obtain coarse anatomical identities. Together, these representations drive a GPU-accelerated iterative registration solver (FireANTs) in a hybrid multistage framework: the first stage estimates alignment from dense multi-channel features, after which a second refines the deformation using both anatomix- and segmentation-based losses. Lastly, because registration should not introduce non-biological tumor volume changes across time, we develop lesion-specific, component-wise volume preservation schemes. Without any task-specific training, our system placed first on the PSMAReg Challenge leaderboard. Code and interactive visualizations are available at https://www.neeldey.com/reps-for-reg.
From the RadAI FDA Approvals Database
GE Medical Systems Ultrasound & Primary Care Diagnostics, LLC
Lung Guidance
Lung Guidance uses artificial intelligence to help clinicians optimize the process of capturing ultrasound images of the lungs, making it easier and more accurate to acquire diagnostic images for patient care.
Airs Medical, Inc.
SwiftSight-BodyComposition
SwiftSight-BodyComposition is a medical imaging software that analyzes MRI scans to assess body composition. This can help clinicians evaluate fat, muscle, and other tissue distributions, supporting diagnosis and treatment planning.
Brainlab SE
Spine Planning (3.0); Spine & Trauma Navigation; Alignment System Spine
The Brainlab Spine Planning (3.0), Spine & Trauma Navigation, and Alignment System Spine help clinicians plan and navigate spine surgeries using advanced image-guided systems. The software uses AI algorithms to automatically label vertebrae and assist in planning implant placement based on CT, MR, and X-ray images, making complex procedures more precise and efficient.
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