Objective: We wanted to investigate the usefulness of a computer-aided diagnosis (CAD) system in assisting radiologists to diagnosis malignant solitary pulmonary nodules (SPNs), as compared with diagnosing SPNs with using direct personal drawing.
Materials And Methods: Forty patients with SPNs were analyzed. After the pre-contrast scan was performed, an additional ten series of post-contrast images were obtained at 20-second intervals. Two investigators measured the attenuation values of the SPNs: a radiologist who drew the regions of interest (ROIs), and a technician who used a CAD system. The Bland and Altman plots were used to compare the net enhancement between a CAD system and direct personal drawing. The diagnostic characteristics of the malignant SPNs were calculated by considering the CAD and direct personal drawing and with using Fisher's exact test.
Results: On the Bland and Altman plot, the net enhancement difference between the CAD system and direct personal drawing was not significant (within +/- 2 standard deriation). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of diagnosing malignant SPNs using CAD was 92%, 85%, 75%, 96% and 88%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of diagnosing malignant SPNs using direct drawing was 92%, 89%, 79%, 92% and 88%, respectively.
Conclusion: The CAD system was a useful tool for diagnosing malignant SPNs.
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http://dx.doi.org/10.3348/kjr.2008.9.5.401 | DOI Listing |
J Clin Med
January 2025
Department of Reconstructive Dentistry, UZB University Center for Dental Medicine Basel, University of Basel, 4058 Basel, Switzerland.
The technical development of implant-supported fixed dental prostheses (iFDP) initially concentrated on the computer-aided manufacturing of prosthetic restorations (CAM). Advances in information technologies have shifted the focus for optimizing digital workflows to AI-based processes for design (CAD). This pre-clinical pilot trial investigated the feasibility of the automatic design of three-unit iFDPs using CAD software (Dental Manger 2021, 3Shape; DentalCAD 3.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Faculty of Pharmacy, University of Montreal, 2940 Chem. de Polytechnique, Montreal, QC H3T 1J4, Canada.
Background/objectives: Through phase III clinical trials, PARP inhibitors have demonstrated outcome improvements in mCRPC patients with alterations in BRCA1/2 genes who have progressed on a second-generation androgen receptor pathway inhibitor (ARPI). While improving outcomes, PARP inhibitors contribute to the ever-growing economic burden of PCa. The objective of this project is to evaluate the cost-effectiveness of PARP inhibitors (olaparib, rucaparib, or talazoparib) versus the SOC (docetaxel or androgen receptor pathway inhibitors (ARPI)) for previously progressed mCRPC patients with BRCA1/2 mutations from the Canadian healthcare system perspective.
View Article and Find Full Text PDFBMC Public Health
January 2025
School of Population and Public Health (SPPH), University of British Columbia (UBC), 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada.
Background: Widespread digital transformation necessitates developing digital competencies for public health practice. Given work in 2024 to update Canada's public health core competencies, there are opportunities to consider digital competencies. In our previous research, we identified digital competency and training recommendations within the literature.
View Article and Find Full Text PDFJpn J Radiol
January 2025
Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.
Artificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with two distinct applications: computer-aided cancer detection (CAD) and risk prediction. While AI CAD systems are slowly finding its way into clinical practice to assist radiologists or make independent reads, this review focuses on AI risk models, which aim to predict a patient's likelihood of being diagnosed with breast cancer within a few years after negative screening. Unlike AI CAD systems, AI risk models are mainly explored in research settings without widespread clinical adoption.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
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