Objective: To compare the accuracy of computer monitor and smartphone screen for radiographic diagnosis of marginal gap.
Materials And Methods: Forty teeth with mesial-occlusal-distal inlays (each tooth with a perfect fit and a 0.4-mm marginal gap restoration) were imaged with a phosphor plate system. Original digital radiographs were exported and analyzed with two different methods: computer monitor and smartphone screen; for the last method, images were shared with WhatsApp. Three examiners assessed all radiographs (n = 160) for the presence of marginal gap by using a dichotomous scale (yes/no). Diagnostic performance of each examiner and viewing method was evaluated by means of sensitivity (Se), specificity (Sp), and overall accuracy (Ac). Difference between the frequencies of gap detection of each method was analyzed using the McNemar test. Intra- and inter-examiner agreements were calculated using kappa statistics.
Results: Intra- and inter-examiner agreements were ≥ 0.80 for both methods. Similar diagnostic performance was found for computer monitor (Se = 0.87-1; Sp = 0.8-0.97; Ac = 0.84-0.99) and smartphone (Se = 0.77-1; Sp = 0.87-1; Ac = 0.88-0.95) viewing methods. No statistically significant differences in the frequency of gap detection were observed between the methods (P > 0.05).
Conclusion: Diagnostic accuracy of smartphone screens was similar to that of computer monitor for marginal gap detection.
Clinical Relevance: Smartphones are becoming a common daily tool. In this sense, it might be an important new aid in Dentistry, including radiographic evaluation, which could benefit patients and dentists.
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http://dx.doi.org/10.1007/s00784-019-02848-6 | DOI Listing |
Int J Med Inform
January 2025
Department of Computer Science and Artificial Intelligence, University of Udine, 33100, Italy.
Background: Segmentation models for clinical data experience severe performance degradation when trained on a single client from one domain and distributed to other clients from different domain. Federated Learning (FL) provides a solution by enabling multi-party collaborative learning without compromising the confidentiality of clients' private data.
Methods: In this paper, we propose a cross-domain FL method for Weakly Supervised Semantic Segmentation (FL-W3S) of white blood cells in microscopic images.
J Neurosurg
January 2025
1Department of Bioengineering, George Mason University, Fairfax, Virginia.
Objective: The complex mix of factors, including hemodynamic forces and wall remodeling mechanisms, that drive intracranial aneurysm growth is unclear. This study focuses on the specific regions within aneurysm walls where growth occurs and their relationship to the prevalent hemodynamic conditions to reveal critical mechanisms leading to enlargement.
Methods: The authors examined hemodynamic models of 67 longitudinally followed aneurysms, identifying 88 growth regions.
PLoS One
January 2025
Cardiovascular Center, Division of Cardiology, Korea University Guro Hospital, Seoul, Republic of Korea.
Background: The phase angle (PhA) in bioelectrical impedance analysis (BIA) reflects the cell membrane integrity or body fluid equilibrium. We examined how the PhA aligns with previously known markers of acute heart failure (HF) and assessed its value as a screening tool.
Methods: PhA was measured in 50 patients with HF and 20 non-HF controls along with the edema index (EI), another BIA parameter suggestive of edema.
PLoS One
January 2025
UK Centre for Ecology and Hydrology, Crowmarsh Gifford, Wallingford, United Kingdom.
Surface water plays a vital role in the spread of infectious diseases. Information on the spatial and temporal dynamics of surface water availability is thus critical to understanding, monitoring and forecasting disease outbreaks. Before the launch of Sentinel-1 Synthetic Aperture Radar (SAR) missions, surface water availability has been captured at various spatial scales through approaches based on optical remote sensing data.
View Article and Find Full Text PDFPLoS One
January 2025
School of Information Science and Engineering, Xinjiang University, Urumqi, China.
Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis.
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