Objective: To evaluate the reliability of WhatsApp in comparison to the images viewed on a workstation monitor (gold standard) for the identification and interpretation of radiographic images of jaw pathologies.
Methods: 150 panoramic radiographs were screened for the assessment of jaw pathologies in the workstation monitor. The radiographs were sent to two observers (Observer A and B) via WhatsApp® Messenger which were viewed independently on smartphones. A structured proforma was prepared to evaluate the radiographs for the presence or absence of various radiographic pathological characteristics.
Results: The reliability of WhatsApp for observers A and B concerning various characteristics like vital structures, pathological fractures, periodontal ligament widening, and root resorption indicated almost perfect agreement (0.8-0.97). The Kappa coefficients for WhatsApp for observers A and B for pre-categorized radiographic impressions were 0.95 and 0.97 which indicated almost perfect agreement.
Conclusion: WhatsApp based expert teleradiology consultation can be a suitable and effective alternative for radiographic interpretations.
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http://dx.doi.org/10.1016/j.jobcr.2021.04.003 | DOI Listing |
Ann Rheum Dis
January 2025
Department of Surgery, University of Cambridge, Cambridge, UK.
Objectives: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.
Methods: We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary.
Vet Rec
January 2025
Richard A. Gillespie College of Veterinary Medicine, Lincoln Memorial University, Harrogate, Tennessee, USA.
Background: Accurate radiographic interpretation is an important day one skill. A case-based radiology course (CBC) demonstrated better learning outcomes than a lecture-based course (LBC) immediately and after a one-semester period. The aim of this study was to compare long-term learning outcomes of both groups.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
View Article and Find Full Text PDFSci Rep
January 2025
Shandong Provincial Public Health Clinical Center, Shandong University, Jinan, 250013, Shandong, China.
Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis.
View Article and Find Full Text PDFJ Dent
January 2025
Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China. Electronic address:
Objective: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.
Methods: Lateral cephalometric radiographs of adult patients were obtained before (T1) and after (T2) orthodontic treatment. The expanded dataset was divided into training, validation, and test sets by random sampling in a ratio of 8:1:1.
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