Purpose: To prospectively determine the interpretation time associated with computer-aided detection (CAD) and to analyze how CAD affected radiologists' decisions and their level of confidence in their interpretations of digital screening mammograms.
Materials And Methods: An Institutional Review Board exemption was obtained, and patient consent was waived in this HIPAA compliant study. The participating radiologists gave informed consent. Five radiologists were prospectively studied as they interpreted 267 clinical digital screening mammograms. Interpretation times, recall decisions, and confidence levels were recorded without CAD and then with CAD. Software was used for linear regression fitting of interpretation times. P values less than .05 were considered to indicate statistically significant differences.
Results: Mean interpretation time without CAD was 118 seconds ± 4.2 (standard error of the mean). Mean time for reviewing CAD images was 23 seconds ± 1.5. CAD identified additional findings in five cases, increased confidence in 38 cases, and decreased confidence in 21 cases. Interpretation time without CAD increased with the number of mammographic views (P < .0001). Mean times for interpretation without CAD and review of the CAD images both increased with the number of CAD marks (P < .0001). The interpreting radiologist was a significant variable for all interpretation times (P < .0001). Interpretation time with CAD increased by 3.2 seconds (95% confidence interval: 1.8, 4.6) for each calcification cluster marked and by 7.3 seconds (95% confidence interval: 4.7, 9.9) for each mass marked.
Conclusion: The additional time required to review CAD images represented a 19% increase in the mean interpretation time without CAD. CAD requires a considerable time investment for digital screening mammography but may provide less measureable benefits in terms of confidence of the radiologists.
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http://dx.doi.org/10.1148/radiol.10092170 | DOI Listing |
Eur Heart J Imaging Methods Pract
October 2024
Cardiologia 1-Emodinamica, Dipartimento Cardiotoracovascolare 'A. De Gasperis', ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy.
Artificial intelligence (AI) is transforming cardiovascular imaging by offering advancements across multiple modalities, including echocardiography, cardiac computed tomography (CCT), cardiovascular magnetic resonance (CMR), interventional cardiology, nuclear medicine, and electrophysiology. This review explores the clinical applications of AI within each of these areas, highlighting its ability to improve patient selection, reduce image acquisition time, enhance image optimization, facilitate the integration of data from different imaging modality and clinical sources, improve diagnosis and risk stratification. Moreover, we illustrate both the advantages and the limitations of AI across these modalities, acknowledging that while AI can significantly aid in diagnosis, risk stratification, and workflow efficiency, it cannot replace the expertise of cardiologists.
View Article and Find Full Text PDFArab J Urol
September 2024
Department of Surgery, Sabah Al-Ahmad Urology Center, Kuwait City, Kuwait.
Purpose: To compare the outcomes of using prophylactic direct oral anti-coagulants (DOAC) and low-molecular-weight heparin (LMWH) after major urologic surgery.
Materials And Methods: Systematic literature searches of MEDLINE, Embase, Web of Science, and Cochrane CENTRAL were performed up to 9 November 2023, and protocols were registered on PROSPERO (CRD42024494424). The primary outcomes were post-operative incidence of VTE and bleeding.
Ann Clin Transl Neurol
January 2025
Department of Neurology, Johns Hopkins Encephalitis Center, Johns Hopkin School of Medicine, Baltimore, Maryland, USA.
Objective: Encephalitis is a serious and potentially life-threatening condition of infectious or autoimmune cause. We aim to characterize the frequency and clinical spectrum of presenting psychiatric symptoms in encephalitis in order to inform earlier recognition and initiation of treatment.
Methods: This was a retrospective study of adult patients who met the 2013 International Encephalitis Consortium (IEC) and/or 2016 Graus criteria between February 2005 and February 2023.
J Robot Surg
January 2025
Multimedia University, Cyberjaya, Malaysia.
Artificial intelligence and robotics are revolutionizing surgical practices by enhancing precision, efficiency, and patient outcomes. With global healthcare systems increasingly adopting AI-driven technologies, the integration of robotics in surgery addresses critical challenges such as surgical accuracy, minimally invasive techniques, and healthcare accessibility. However, disparities in access and ethical concerns regarding automation persist globally, necessitating a balanced discourse on these advancements.
View Article and Find Full Text PDFBiometrics
January 2025
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Investigating the relationship, particularly the lead-lag effect, between time series is a common question across various disciplines, especially when uncovering biological processes. However, analyzing time series presents several challenges. Firstly, due to technical reasons, the time points at which observations are made are not at uniform intervals.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!