Aim: the adenoma detection rate is the quality indicator of colonoscopy that is most closely related to the development of interval colorectal cancer or post-colonoscopy colorectal cancer. However, the recording of this indicator in different units of gastrointestinal endoscopy is obstructed due to the large consumption of resources required for its calculation. Several alternatives have been proposed, such as the polyp detection rate. The objective of this study was to evaluate the relationship between the polyp detection rate and its influence on post-colonoscopy colorectal cancer rate.
Patients And Methods: in this study, 12,482 colonoscopies conducted by 14 endoscopists were analyzed. The polyp detection rate was calculated for each endoscopist. Endoscopists were grouped into quartiles (Q1, Q2, Q3, and Q4), from lowest to highest polyp detection rate, in order to evaluate whether there were any differences in the development of post-colonoscopy colorectal cancer.
Results: the lowest polyp detection rate was 20.66% and the highest was 52.16%, with a median of 32.78 and a standard deviation of ± 8.54. A strong and positive association between polyp endoscopy diagnosis and adenoma histopathology result was observed and a linear regression was performed. A significantly higher post-colonoscopy colorectal cancer rate was observed in the group of endoscopists with a lower polyp detection rate (p < 0.02).
Conclusion: polyp detection rate is a valuable quality indicator of colonoscopy and its calculation is much simpler than that of the adenoma detection rate. In our study, the prevalence of post-colonoscopy colorectal cancer was inversely and significantly related to the endoscopists' polyp detection rate.
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http://dx.doi.org/10.17235/reed.2019.5889/2018 | DOI Listing |
J 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
Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Analysis of the symmetry of the brain hemispheres at the level of individual structures and dominant tissue features has been the subject of research for many years in the context of improving the effectiveness of imaging methods for the diagnosis of brain tumor, stroke, and Alzheimer's disease, among others. One useful approach is to reliably determine the midline of the brain, which allows comparative analysis of the hemispheres and uncovers information on symmetry/asymmetry in the relevant planes of, for example, CT scans. Therefore, an effective method that is robust to various geometric deformations, artifacts, varying noise characteristics, and natural anatomical variability is sought.
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.
View Article and Find Full Text PDFClin Exp Nephrol
January 2025
Reach-J Steering Committee, Tsukuba, Ibaraki, Japan.
Background: Although several studies have examined the Kidney Disease Quality of Life (KDQOL) in patients with chronic kidney disease (CKD), the factors associated with kidney-related symptoms have not been fully explored.
Methods: This nationwide multicenter cohort study enrolled 2248 patients. To identify the factors associated with each item or the three KDQOL domains, such as burden of kidney disease, symptoms/problems of kidney disease, and impact of kidney disease on daily life, multiple regression analysis was performed using baseline data.
Clin Oral Investig
January 2025
Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, D-52074, Aachen, Germany.
Objectives: In advanced stages of osteoradionecrosis, medication-related osteonecrosis of the jaw, and osteomyelitis, a resection of sections of the mandible may be unavoidable. The determination of adequate bony resection margins is a fundamental problem because bony resection margins cannot be secured intraoperatively. Single-photon emission computed tomography (SPECT-CT) is more accurate than conventional imaging techniques in detecting inflammatory jaw pathologies.
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