Purpose: To determine the accuracy of identifying referral-warranted retinopathy of prematurity (RW-ROP, defined as any zone I ROP, stage 3 or worse, or plus disease) from retinal image sets using three grading protocols: a single optic disk-centered image, a set of 3 horizontal images, and a 5-image set.
Methods: In this secondary analysis of images from the e-ROP study, a weighted sample of 250 image sets from 250 infants (125 with RW-ROP and 125 without RW-ROP) was randomly selected. The sensitivities and specificities for detecting RW-ROP and its components from a single disk center image, along with nasal and temporal retinal images, were calculated and compared with the e-ROP grading of RW-ROP of all 5 retinal images (disk center and nasal, temporal, superior, and inferior retinal images).
Results: RW-ROP was identified with a sensitivity of 11.2% (95% CI, 6.79%-17.9%) using a single disk center image, with a sensitivity of 70.4% (95% CI, 61.9%-77.9%) using 3 horizontal images, and a statistically higher sensitivity of 82.4% (95% CI, 75.0%-89.0%) using all 5 images (P = 0.002). The specificities were 100%, 86.4%, and 90.4%, respectively. For grading using 3 horizontal images, sensitivity was 14.3% for plus disease, 25% for zone I ROP, and 71.2% for stage 3 or worse compared to 40.8%, 50%, and 79.8% for grading using 5-image sets, respectively.
Conclusions: Both a single, disk-centered, posterior pole image and 3 horizontal images were less effective than a 5-image set in determining the presence of RW-ROP on qualitative grading by trained readers.
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http://dx.doi.org/10.1016/j.jaapos.2017.01.001 | DOI Listing |
Ultrasound J
January 2025
Department of Veterinary Clinical and Diagnostic Sciences, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.
Background: Lung ultrasound (LUS) is increasingly utilized in veterinary medicine to assess pulmonary conditions. However, the characterization of pleural line and subpleural fields using different ultrasound transducers, specifically high-frequency linear ultrasound transducers (HFLUT) and curvilinear transducers (CUT), remains underexplored in canine patients. This study aimed to evaluate inter-rater agreement in the characterization of pleural line and subpleural fields using B- and M-mode ultrasonography in dogs with and without respiratory distress.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Department of Periodontology, Semmelweis University, Budapest, Hungary.
Objectives: To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.
Materials And Methods: The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR.
J Oral Facial Pain Headache
September 2024
Department of Orofacial Pain and Oral Medicine, Yonsei University College of Dentistry, 03722 Seoul, Republic of Korea.
This study investigated the effects of unilateral temporomandibular joint disorders (TMJDs), specifically disc displacement without reduction and osteoarthritis on one side of the temporomandibular joint (TMJ), on facial asymmetry in women, while the contralateral TMJ exhibits normal findings. Participants were retrospectively enrolled and divided into an affected group (n = 42 with unilateral TMJD) and a control group (n = 49 with bilateral healthy TMJs). The affected group was dagnosed with osteoarthritis on cone-bema computed tomograph and anterior disk displacement without reduction on magnetic resonance imaging.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, al. Piastow 17, 70-310 Szczecin, Poland.
The safety of the airspace could be improved by the use of visual methods for the detection and tracking of aircraft. However, in the case of the small angular size of airplanes and the high noise level in the image, sufficient use of such methods might be difficult. By using the ConvNN (Convolutional Neural Network), it is possible to obtain a detector that performs the segmentation task for aircraft images that are very small and lost in the background noise.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland.
Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction.
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