Background: The purpose of this clinical study was to evaluate the complications associated with the different attachments used in implant-supported overdentures, including prosthetic problems and implant failures. A comparison of ball, bar and Locator (Zest Anchors, Inc, homepage, Escondido, CA, USA) attachments, in completely edentulous patients with two, three or four implants, was conducted.
Material And Methods: A total of 36 edentulous patients (20 female, 16 male) with a mean age of 66.3 years, were enrolled in the study. The patients were treated with 95 implants, for the prosthetic restoration of the maxilla or the mandible. The mean follow-up time was 41.17 months. Prosthetic complications including, fractured overdentures, replacements of O-ring attachment and retention clips, implant failures, hygiene problems, mucosal enlargements, attachment fractures, retention loss and dislodgement of the attachments were recorded and evaluated. The recall visits at 3, 6, 12 months and, annually thereafter.
Results: Fourteen complications in the ballattachment group and 7 complications in the bar group were observed. No complications were observed in the locator group. The difference was found to be as statistically significant (p=0,009). Six of the 95 implants had failed. Totally 39 implant overdentures were applied. Three prostheses were renewed because of fractures.
Conclusion: Within the limits of the present study, it was concluded that the locator system showed superior clinical results than the ball and the bar attachments, with regard to the rate of prosthodontic complications and the maintenance of the oral function.
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RSNA 2023 Abdominal Trauma AI Challenge Review and Outcomes Analysis.
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From the Department of Medical Imaging, St. Michael's Hospital, Unity Health Toronto, 30 Bond St, Toronto, ON, Canada, M5B 1W8 (S.H., Z.H., H.M.L., I.Y., E.C.); Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada (Z.H., E.S.); The Jackson Laboratory, Bar Harbor, Me (R.L.B.); Department of Radiology, The Ohio State University, Columbus, Ohio (L.M.P.); Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada (F.H.B.); Department of Radiology, Scripps Clinic Medical Group and University of California San Diego, San Diego, Calif (J.D.R.); Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.); Department of Radiology, Weill Cornell Medicine, New York, NY (G.S.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.M.), Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (B.S.M.); Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (M.A.D.); Duke University School of Medicine, Durham, NC (K.M.); North York General Hospital, Toronto, Ontario, Canada (E.S.); and Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada (E.C.).
Purpose To evaluate the performance of the winning machine learning (ML) models from the 2023 RSNA Abdominal Trauma Detection Artificial Intelligence Challenge. Materials and Methods The competition was hosted on Kaggle and took place between July 26, 2023, to October 15, 2023. The multicenter competition dataset consisted of 4,274 abdominal trauma CT scans in which solid organs (liver, spleen and kidneys) were annotated as healthy, low-grade or high-grade injury.
View Article and Find Full Text PDFThe RSNA Abdominal Traumatic Injury CT (RATIC) Dataset.
Radiol Artif Intell
November 2024
From the Department of Radiology, Scripps Clinic Medical Group and University of California, San Diego, 200 W Arbor Dr, San Diego, CA 92103-8749 (J.D.R.); Department of Medical Imaging, St Michael's Hospital, Unity Health Toronto, Toronto, Canada (H.M.L., S.H., S.M., E.C.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.J., S.N.); Department of Radiology, The Ohio State University, Columbus, Ohio (L.M.P.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (B.S.M.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.); Department of Radiology, Duke University School of Medicine, Durham, NC (K.M.); Department of Radiology, Weill Cornell Medicine, New York, NY (G.S.); Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (M.A.D.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (J.M.); Departments of Radiological Sciences and Computer Science, University of California, Irvine, Irvine, Calif (P.D.C.); Department of Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada (F.H.B.); Department of Radiology, Alfred Health, Monash University, Melbourne, Australia (M.L.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Diagnostic and Interventional Radiology, University Hospital of Würzburg, Würzburg, Germany (J.P.G., A.S.K.); Department of Medical Imaging, Mater Dei Hospital, Msida, Malta (S.G.S.); Department of Diagnostic Radiology, Kingston Health Sciences Center, Queen's University, Kingston, Canada (A.D.C.); Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tübingen, Tübingen, Germany (S.A.); Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan (C.C.K.); Big Data Center, China Medical University Hospital, Taichung, Taiwan (C.C.K.); AKI-CARE (Clinical Advancement, Research and Education) Center, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan (C.C.K.); Department of Medical Imaging, Liverpool Hospital, Sydney, Australia (L.A.); Department of Radiology, Hospital Universitario Ramón y Cajal, Madrid, Spain (A.V.C.); Department of Radiology, Gold Coast University Hospital, Griffith University, Gold Coast, Australia (A.S.); Department of Medical Imaging, Clínica Santa María, Santiago, Chile (F.A.S.T.); Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand (A.J.); Department of Radiology, University Hospitals Cleveland Medical Center and Case Western Reserve University School of Medicine, Cleveland, Ohio (L.K.B.); Department of Radiology, Tallaght University Hospital, Dublin, Ireland (M. Brassil); Department of Radiology, Arrazi Hospital, CHU Mohamed VI Cadi Ayyad University, Marrakech, Morocco (A.E.H.); Department of Radiology, Koç University School of Medicine, Istanbul, Turkey (H.D.); Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina (M. Becircic); Department of Diagnostic Radiology, Medical College of Wisconsin, Milwaukee, Wis (A.G.B.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (E.M.J.d.M.F.); and Department of Medical Imaging, University of Toronto, Toronto, Canada (S.M., S.H., E.C.).
Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center.
Radiol Artif Intell
November 2024
From the Edward S. Rogers Department of Electrical and Computer Engineering (Z.H., W.L., E.S.), Department of Medical Imaging, Faculty of Medicine (M.P., S.M., R.M., E.C.), Faculty of Medicine (M.N., J.W., C.W.), and Division of Neurosurgery, Department of Surgery (J.W., C.W.), University of Toronto, 40 St George St, Toronto, ON, Canada M5S 3G4; Department of Medical Imaging (H.M.L., M.N., S.M., R.M., E.C.) and Li Ka Shing Knowledge Institute (S.M., J.W., C.W., E.C.), St Michael's Hospital, Unity Health Toronto, Toronto, Canada; The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Standard School of Medicine, Stanford University, Stanford, Calif (K.W.Y.); H2O.ai, Mountain View, Calif (Q.H., P.S., P.P.); School of Computer Science, University of Birmingham, Birmingham, UK (H.C.); DoubleYard, Edulab Group, Boston, Ireland (D.H.); Mapbox, London, UK (S.S.); NVIDIA, Santa Clara, Calif (C.H.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (I.P.); University of London, Goldsmiths, London, UK (H.S.); Department of Radiology, The Ohio State University, Columbus, Ohio (L.M.P.); Department of Radiology, Division of Neuroradiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.); Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (T.R.); and North York General Hospital, Toronto, Canada (E.S.).
Purpose To evaluate the performance of the top models from the RSNA 2022 Cervical Spine Fracture Detection challenge on a clinical test dataset of both noncontrast and contrast-enhanced CT scans acquired at a level I trauma center. Materials and Methods Seven top-performing models in the RSNA 2022 Cervical Spine Fracture Detection challenge were retrospectively evaluated on a clinical test set of 1828 CT scans (from 1829 series: 130 positive for fracture, 1699 negative for fracture; 1308 noncontrast, 521 contrast enhanced) from 1779 patients (mean age, 55.8 years ± 22.
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