The radiographic interpretation of pathological lesions which are endodontic in origin is relatively imprecise as so many variables are involved. Even the presence or absence of a lesion cannot be determined with accuracy and there is little agreement on the criteria which should be applied. Strict attention to the technique of exposing, processing and viewing radiographs is necessary if the information to be gained is to be optimal. The major problem of visual interpretation and the psychological factors involved have been subjected to a certain amount of study and recent work on the mental processes of clinical decision making provide further insight. There is considerable promise being shown by methods of computer analysis and image enhancement and it may be that further development in these fields will provide the degree of objectivity that is essential if improved accuracy in diagnosis is to be achieved.
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BMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
View Article and Find Full Text PDFJ Surg Res
January 2025
Department of Surgery, Stanford University School of Medicine, Stanford, California; S-SPIRE Center, Department of Surgery, Stanford University School of Medicine, Stanford, California.
Introduction: Previous research has demonstrated that after neoadjuvant therapy for rectal cancer, the sensitivity of magnetic resonance complete response (mrCR) for detecting pathologic complete response (pCR) in the surgical specimen ranges from 74 to 94%. Patient and provider interest in nonoperative management of rectal cancer that responds favorably to neoadjuvant therapy has grown, necessitating stronger evidence for how well radiographic complete response truly predicts pCR. We sought to determine the current association between mrCR and pCR in locally advanced rectal cancer.
View Article and Find Full Text PDFAnn 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.
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