Aims: A novel method for computation of fractional flow reserve (FFR) from optical coherence tomography (OCT) was developed recently. This study aimed to evaluate the diagnostic accuracy of a new OCT-based FFR (OFR) computational approach, using wire-based FFR as the reference standard.
Methods And Results: Patients who underwent both OCT and FFR prior to intervention were analysed. The lumen of the interrogated vessel and the ostia of the side branches were automatically delineated and used to compute OFR. Bifurcation fractal laws were applied to correct the change in reference lumen size due to the step-down phenomenon. OFR was compared with FFR, both using a cut-off value of 0.80 to define ischaemia. Computational analysis was performed in 125 vessels from 118 patients. Average FFR was 0.80±0.09. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for OFR to identify FFR ≤0.80 was 90% (95% CI: 84-95), 87% (95% CI: 77-94), 92% (95% CI: 82-97), 92% (95% CI: 82-97), and 88% (95% CI: 77-95), respectively. The AUC was higher for OFR than minimal lumen area (0.93 [95% CI: 0.87-0.97] versus 0.80 [95% CI: 0.72-0.86], p=0.002). Average OFR analysis time was 55±23 seconds for each OCT pullback. Intra- and inter-observer variability in OFR analysis was 0.00±0.02 and 0.00±0.03, respectively.
Conclusions: OFR is a novel and fast method allowing assessment of flow-limiting coronary stenosis without pressure wire and induced hyperaemia. The good diagnostic accuracy and low observer variability bear the potential of improved integration of intracoronary imaging and physiological assessment.
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http://dx.doi.org/10.4244/EIJ-D-19-00182 | DOI Listing |
Ann Neurol
January 2025
Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
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View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Orthopedic Surgery, Arrowhead Regional Medical Center, Colton, CA, USA.
Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
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