Background/aims: Pigmented lesions are often difficult to evaluate clinically. Improvement of diagnostic accuracy by dermatoscopy has attracted much interet. With advanced digital imaging measurement of assymmetry, border irregularity and relative color as well as texture characteristics, lesional depth and changes in lesional area are now possible, the object of this review is to conclude the present status of these techniques and their potential.
Conclusions: Digital imaging of pigmented lesions to this date include acquiring and storing of images, quantification of clinical features including asymmetry, and teledermatology with transfer of images. Predicted uses include malignancy evaluation, delineation of depth of invasion and the development of large collections of pigment lesions observations. The field is rapidly expanding. As of 1994, it is unknown what role digital imaging will ultimately play in clinical dermatology.
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http://dx.doi.org/10.1111/j.1600-0846.1995.tb00007.x | DOI Listing |
Surg Radiol Anat
January 2025
Department of Anatomy, Jagiellonian University Medical College, Mikołaja Kopernika 12, Kraków, 33-332, Poland.
Introduction: The anterior division of the internal iliac artery (ADIIA) is a crucial vascular structure that supplies blood to the pelvic organs, perineum, and gluteal region. The present study demonstrates practical data concerning the anatomy of the ADIIA and its branches. It is hoped that the results of the current study may aid in localizing the pelvic arteries effectively.
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January 2025
From the Department of Radiology, University of Washington, UW Medical Center-Montlake, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC), University of Washington, Seattle, Wash (D.M.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (M.v.A.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.H.); Department of Radiology, Mayo Clinic, Rochester, Minn (T.L., E.E.W.); Departments of Cardiology and Radiology, Royal Brompton Hospital, London, United Kingdom (E.D.N.); School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom (E.D.N.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (B.D.A.); Department of Radiology, University of Cagliari, Cagliari, Italy (L.S.); Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1 Postbus 30 001, 9700 RB Groningen, the Netherlands (R.V.); Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.H.).
Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, and interpretation, extending to prognostication and reporting. Despite the development of many cardiac imaging AI algorithms, AI tools are at various stages of development and face challenges for clinical implementation. This scientific statement, endorsed by several societies in the field, provides an overview of the current landscape and challenges of AI applications in cardiac CT and MRI.
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January 2025
From the Department of Radiology (J.H.L.) and Department of Thoracic and Cardiovascular Surgery (J.L., Y.J.J., S.Y.P., J.H.C., Y.S.C., J.K., Y.M.S., H.K.K.), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea; Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, 115 Irwon-ro, Gangnam-gu, Seoul 06355, Korea (D.K., J.L., S.Y.P., S.K., J.C.); Center for Clinical Epidemiology, Sungkyunkwan University, Samsung Medical Center, Seoul, Korea (D.K., J.C.); Patient-Centered Outcomes Research Institute, Samsung Medical Center, Seoul, Korea (J.L., Y.M.S., S.K., H.K.K., J.C.); and Department of Epidemiology and Medicine, Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (J.C.).
Background A comprehensive assessment of skeletal muscle health is crucial to understanding the association between improved clinical outcomes and obesity as defined by body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) in lung cancer, but limited studies have been conducted on this topic. Purpose To investigate the association between BMI-defined obesity and survival in patients with non-small cell lung cancer who underwent curative resection, with a specific focus on the status of skeletal muscle assessed at CT. Materials and Methods This retrospective study investigated Korean patients with non-small cell lung cancer who underwent curative resection between January 2008 and December 2019.
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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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January 2025
From the Departments of Radiology (V.K., A.R., P.D.) and Pathology (J.N.), University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205.
A 61-year-old male patient without prior history of ophthalmologic problems presented with pain and redness in the left eye associated with slowly progressive proptosis over the previous 6 months. The patient also had diplopia in rightward and downward gaze. There was no vision loss.
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