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Multimodal AI Boosts Melanoma Detection Accuracy to 94.5%
A new deep learning model combining dermoscopic images with patient metadata achieves 94.5% accuracy in melanoma detection.
Clairity Raises $43M to Commercialize AI for Mammogram-Based Risk Prediction
Clairity, a breast imaging AI startup, secured $43M to advance its FDA-cleared mammogram risk prediction tool.
Healthcare AI Policy, Funding, and Radiology Advances: Key 2024 Updates
Healthcare AI sees rapid investment, evolving regulation, and expanded clinical applications in 2024.
From the Research Hub
Automated detection and segmentation of chondromalacia patella on axial knee MRI using YOLOv11 and a custom CNN: a deep learning-based diagnostic model.
To evaluate a deep learning pipeline using YOLOv11 for segmentation and a custom CNN for classification to automatically detect and assess chondromalacia patella on axial knee MRI, aligning with expert clinical evaluation. A dataset of 650 axial knee MRIs was analyzed. YOLOv11 segmented the patellofemoral region, and a custom CNN classified chondromalacia. Performance was assessed using segmentation accuracy, classification accuracy, confidence scoring, and Grad-CAM-based visual explainability. The CNN achieved a test accuracy of 82.30% on 113 images, with an AUC of 0.87, indicating promising but preliminary discriminative ability. Grad-CAM maps showed reasonable agreement with expert interpretation. The proposed YOLOv11-CNN pipeline demonstrated promising accuracy and may provide a potentially useful and interpretable solution for the detection and segmentation of chondromalacia patella on MRI, with the possibility of enhancing efficiency and consistency in orthopedic radiology workflows after further validation. The online version contains supplementary material available at 10.1186/s12891-025-09275-7.
Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.
Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, and CT imaging plays a crucial role in its diagnosis and management. However, the clinical use of CT is limited by factors, such as suboptimal image quality, diagnostic complexity and the labour-intensive nature of parameter evaluation. Artificial intelligence (AI) is increasingly transforming many areas of medicine. Its integration into CAD CT imaging can enhance image postprocessing, streamline anatomical and functional analyses, support treatment planning and improve risk prediction. This review summarises recent advances in these AI applications, aiming to promote their practical adoption and further development.
Detecting Scoliosis at Scale Using Automated Cobb Angle Analysis in the UK Biobank
PurposeAdult degenerative scoliosis arises after skeletal maturity in an initially normal spine, primarily driven by age-related degeneration. The Cobb angle, the angle between the most tilted vertebrae typically derived from radiographs, remains the clinical standard for assessing curvature severity, yet large-scale evaluation using MRI has not been feasible. This study developed an automated method for Cobb angle estimation from chemical-shift-based water-fat separation (Dixon) MRI and applied it to the UK Biobank to characterise the prevalence and curvature within the general population. MethodsAbdominal Dixon MRI data from 33,889 UK Biobank participants were analysed. Vertebral bone marrow compartments were segmented using a neural network based model, and spinal curvature was quantified using a centroid-based spline-fitting algorithm. Sex-stratified linear regression analyses were performed to explore associations between spinal curvature and anthropometric, socioeconomic, and health-related traits, including back pain and body composition. ResultsWhile scoliosis was clinically diagnosed or self-reported in only 0.5% of participants, the automated approach detected scoliosis (Cobb angle > 10{degrees}) in 28%, of which 95% were mild (<25{degrees}). Females exhibited higher average Cobb angles and greater curvature across all age groups. Linear regression revealed significant associations between Cobb angle and age, paraspinal muscle fat infiltration, chronic back pain, and visceral adipose tissue in both sexes, and with iliopsoas muscle volume in males only. ConclusionThis fully automated approach enables large-scale, population-based assessment of spinal curvature, revealing adult scoliosis to be substantially under-recognised and closely linked to muscle quality and back pain.
From the RadAI FDA Approvals Database
Canon Medical Systems Corporation
Aplio i900, Aplio i800 and Aplio i700 Software V9.0 Diagnostic Ultrasound System
The Aplio i900, i800, and i700 Software V9.0 are diagnostic ultrasound systems used for medical imaging. These systems help clinicians visualize internal body structures in real-time using pulsed Doppler ultrasound technology, aiding in diagnosis and treatment planning.
ViTAA Medical Solutions Inc.
AiORTA - Plan
AiORTA - Plan is an AI-powered software that processes radiological images automatically, assisting clinicians by enhancing image analysis workflows in radiology. It helps improve accuracy and efficiency in interpreting medical images.
GE Medical Systems Ultrasound and Primary Care Diagnostics
LOGIQ E10
The LOGIQ E10 is an advanced ultrasound imaging system developed by GE Medical Systems. It uses pulsed Doppler ultrasonic technology to produce detailed real-time images, helping clinicians accurately assess and diagnose patients through non-invasive ultrasound scans.
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