Aim: The aim of the study is to compare and evaluate the validity and reliability of tooth widths and Bolton ratios measured from digital models obtained from intraoral scanners and plaster models derived from alginate and polyvinyl siloxane impression materials.
Materials And Methods: Alginate and polyvinylsiloxane impression was taken for 40 subjects, orthokal stone was poured and grouped as Group I and Group II, respectively. Intraoral scanning was done using Trios Pod 3shape for the same patients, digital models were obtained and grouped as Group III. OrthoAnalyzer software was used for obtaining measurements in digital models and Aerospace Vernier calipers in plaster models. The validity and reliability of the three groups were quantified and compared.
Results: Validity measurements showed significant differences between tooth widths and Bolton ratios obtained from digital models and plaster models indicating higher accuracy for plaster models whereas reliability coefficients were excellent for digital models indicating better reproducibility of values.
Conclusion: The study shows significant differences in accuracy on measuring with vernier calipers and Orthoanalyzer software showing plaster models are still better than digital models for measuring tooth widths and bolton ratios.
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http://dx.doi.org/10.4103/jpbs.jpbs_735_21 | DOI Listing |
Brain Inform
January 2025
Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland.
A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology, Duke University Hospital, 2301 Erwin Rd, Box 3808, Durham, NC 27701 (B.W.T., K.R.K., B.C.A., S.P.T., D.E.K., B.H., M.R.B., D.M., E.S., E.A.); Department of Biostatistics and Bioinformatics (N.F., S.M., A.E.) and Department of Medical Physics (W.P.S., E.S., E.A.), Duke University, Durham, NC.
Background Detection of hepatic metastases at CT is a daily task in radiology departments that influences medical and surgical treatment strategies for oncology patients. Purpose To compare simulated photon-counting CT (PCCT) with energy-integrating detector (EID) CT for the detection of small liver lesions. Materials and Methods In this reader study (July to December 2023), a virtual imaging framework was used with 50 anthropomorphic phantoms and 183 generated liver lesions (one to six lesions per phantom, 0.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China (Q.S., P.L., J.Z.); and Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029 (Q.S., P.L., R.Y., D.F.Y., C.I.H.).
Background Angiolymphatic invasion (ALI) is an important prognostic indicator in non-small cell lung cancer (NSCLC). However, few studies focus on radiologic features for predicting ALI in patients with early-stage NSCLCs 30 mm or smaller. Purpose To identify radiologic features for predicting ALI in NSCLCs 30 mm or smaller in maximum diameter.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDFChronobiol Int
January 2025
Google LLC, San Francisco, California, USA.
Circadian rhythms are governed by a biological clock, and are known to occur in a variety of physiological processes. We report results on the circadian rhythm of heart rate observed using a wrist-worn wearable device (Fitbit), consisting of over 17,000 individuals over the course of 30 days. We obtain an underlying heart rate circadian rhythm from the time series heart rate by modeling the circadian rhythm as a sum over the first two Fourier harmonics.
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