Rationale And Objectives: To determine the relationship between heightened levels of reader performance and reader practice in terms of number of cases read and previous experience.
Materials And Methods: A test set of mammograms was developed comprising 50 cases. These cases consisted of 15 abnormals (biopsy proven) and 35 normals (confirmed at subsequent rescreen). Sixty-nine breast image readers reviewed these cases independently and their performance was measured by recording their individual receiver operating characteristic score (area under the curve), sensitivity, and specificity. These measures of performance were then compared to a range of factors relating to the reader such as years of certification and reporting, number of cases read per year, previous experiences, and satisfaction levels. Correlation analyses using Spearman methods were performed along with the Mann-Whitney test to detect differences in performance between specific reader groups.
Results: Improved reader performance was found for years certified (P = .004), years of experience (P = .0001), and hours reading per week (P = .003) shown by positive statistical significant relationships with Az values (area under receiver operating characteristic curve). Statistical comparisons of Az values scored for individuals who read varying number of cases per year showed that those individuals whose annual mammographic case load was 5000 or more (P = .03) or between 2000 and 4999 (P = .05), had statistically significantly higher scores than those who read less than 1000 cases per year.
Conclusion: The results of this study have shown variations in reader performance relating to parameters of reader practice and experience. Levels of variance are shown and potential acceptance levels for diagnostic efficacy are proposed which may inform policy makers, judicial systems and public debate.
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http://dx.doi.org/10.1016/j.acra.2010.06.016 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Eur J Nucl Med Mol Imaging
January 2025
Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Purpose: The positron range effect can impair PET image quality of Gallium-68 (Ga). A positron range correction (PRC) can be applied to reduce this effect. In this study, the effect of a tissue-independent PRC for Ga was investigated on patient data.
View Article and Find Full Text PDFAnn Rheum Dis
January 2025
Rheumatology Department, Cochin Hospital, Paris, France; INSERM (U1153): Clinical Epidemiology and Biostatistics, University of Paris, Paris, France.
Objectives: To assess the ability of a previously trained deep-learning algorithm to identify the presence of inflammation on MRI of sacroiliac joints (SIJ) in a large external validation set of patients with axial spondyloarthritis (axSpA).
Methods: Baseline SIJ MRI scans were collected from two prospective randomised controlled trials in patients with non-radiographic (nr-) and radiographic (r-) axSpA (RAPID-axSpA: NCT01087762 and C-OPTIMISE: NCT02505542) and were centrally evaluated by two expert readers (and adjudicator in case of disagreement) for the presence of inflammation by the 2009 Assessment of SpondyloArthritis International Society (ASAS) definition. Scans were processed by the deep-learning algorithm, blinded to clinical information and central expert readings.
Purpose: We hypothesised that applying radiomics to [F]PSMA-1007 PET/CT images could help distinguish Unspecific Bone Uptakes (UBUs) from bone metastases in prostate cancer (PCa) patients. We compared the performance of radiomic features to human visual interpretation.
Materials And Methods: We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [F]PSMA-1007 PET/CT and exhibited at least one focal bone uptake with known clinical follow-up (reference standard).
Radiology
January 2025
From the Departments of Biomedical Systems Informatics (S.K., Jaewoong Kim, C.H., D.Y.) and Neurology (Joonho Kim, J.Y.), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; Department of Radiology, Central Draft Physical Examination Office of Military Manpower Administration, Daegu, Republic of Korea (D.K.); Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (H.J.S. Y.K., S.J.), and Center for Digital Health (H.J.S., D.Y.), Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea (S.H.L.); Departments of Radiology (M.H.) and Neurology (S.J.L.), Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea; and Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea (D.Y.).
Background The increasing workload of radiologists can lead to burnout and errors in radiology reports. Large language models, such as OpenAI's GPT-4, hold promise as error revision tools for radiology. Purpose To test the feasibility of GPT-4 use by determining its error detection, reasoning, and revision performance on head CT reports with varying error types and to validate its clinical utility by comparison with human readers.
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