Introduction: We present a novel semi-automated computerized method for the detection and quantification of parafoveal capillary network (PCN) in fluorescein angiography (FA) images.
Material And Methods: An algorithm detecting the superficial parafoveal capillary bed in high-resolution grayscale FA images and creating a one-pixel-wide PCN skeleton was developed using MatLab software. In addition to PCN detection, capillary density and branch point density in two circular areas centered on the center of the foveal avascular zone of 500μm and 750μm radius was calculated by the algorithm. Three consecutive FA images with distinguishable PCN from 56 eyes from 56 subjects were used for analysis. Both manual and semi-automated detection of the PCN and branch points was performed and compared. Three different intensity thresholds were used for the PCN detection to optimize the method defined as mean(I)+0.05*SD(I), mean(I) and mean(I)-0.05*SD(I), where I is the grayscale intensity of each image and SD the standard deviation. Limits of agreement (LoA), intraclass correlation coefficient (ICC) and Pearson's correlation coefficient (r) were calculated.
Results: Using mean(I)-0.05*SD(I) as threshold the average difference in PCN density between semi-automated and manual method was 0.197 (0.316) deg at 500μm radius and 0.409 (0.562) deg at 750μm radius. The LoA were -0.421 to 0.817 and -0.693 to 1.510 deg, respectively. The average difference of branch point density between semi-automated and manual method was zero for both areas; LoA were -0.001 to 0.002 and -0.001 to 0.001 branch points/degrees, respectively. The other two intensity thresholds provided wider LoA for both metrics. The semi-automated algorithm showed great repeatability (ICC>0.91 in the 500μm radius and ICC>0.84 in the 750μm radius) for both metrics.
Conclusion: This semi-automated algorithm seems to provide readings in agreement with those of manual capillary tracing in FA. Larger prospective studies are needed to confirm the utility of the algorithm in clinical practice.
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http://dx.doi.org/10.2147/OPTH.S407695 | DOI Listing |
Sensors (Basel)
December 2024
School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK.
The rapid integration of Internet of Things (IoT) systems in various sectors has escalated security risks due to sophisticated multilayer attacks that compromise multiple security layers and lead to significant data loss, personal information theft, financial losses etc. Existing research on multilayer IoT attacks exhibits gaps in real-world applicability, due to reliance on outdated datasets with a limited focus on adaptive, dynamic approaches to address multilayer vulnerabilities. Additionally, the complete reliance on automated processes without integrating human expertise in feature selection and weighting processes may affect the reliability of detection models.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Ophthalmology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach.
View Article and Find Full Text PDFBMC Oral Health
December 2024
Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China.
Background: This study aims to evaluate the impact of different thresholds and voxel sizes on the accuracy of Cone-beam computed tomography (CBCT) tooth reconstruction and to assess the accuracy of fused CBCT and intraoral scanning (IOS) tooth models using curvature continuity algorithms under varying thresholds and voxel conditions.
Methods: Thirty-two isolated teeth were digitized using IOS and CBCT at two voxel sizes and five threshold settings. Crown-root fusion was performed using a curvature continuity algorithm.
Ther Adv Gastrointest Endosc
December 2024
Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam.
The utilization of artificial intelligence (AI) in gastrointestinal (GI) endoscopy has witnessed significant progress and promising results in recent years worldwide. From 2019 to 2023, the European Society of Gastrointestinal Endoscopy has released multiple guidelines/consensus with recommendations on integrating AI for detecting and classifying lesions in practical endoscopy. In Vietnam, since 2019, several preliminary studies have been conducted to develop AI algorithms for GI endoscopy, focusing on lesion detection.
View Article and Find Full Text PDFSkelet Muscle
December 2024
Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada.
Background: INTER- and INTRAmuscular fat (IMF) is elevated in high metabolic states and can promote inflammation. While magnetic resonance imaging (MRI) excels in depicting IMF, the lack of reproducible tools prevents the ability to measure change and track intervention success.
Methods: We detail an open-source fully-automated iterative threshold-seeking algorithm (ITSA) for segmenting IMF from T1-weighted MRI of the calf and thigh within three cohorts (CaMos Hamilton (N = 54), AMBERS (N = 280), OAI (N = 105)) selecting adults 45-85 years of age.
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