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ChatGPT vs Gemini: Comparative Accuracy and Efficiency in CAD-RADS Score Assignment from Radiology Reports.
J Imaging Inform Med
November 2024
Division of Cardiothoracic Imaging, Department of Radiology and Radiological Science, Clinical Science Building, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA.
Article Synopsis
- The study evaluated the ability of four language models (ChatGPT-3.5, ChatGPT-4o, Google Gemini, and Google Gemini Advanced) to accurately generate CAD-RADS scores from coronary CT angiography reports without any fine-tuning.
- ChatGPT-4o had the highest accuracy at 87%, while ChatGPT-3.5 was the fastest but only achieved 50.5% accuracy, and Gemini had a higher failure rate at 12%.
- Overall, the findings indicate that while model performance showed promise, further refinement of these AI tools is needed before they can be reliably used in clinical decision-making for CAD-RADS scoring.
Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease.
Eur Radiol Exp
October 2024
Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France.
Article Synopsis
- The study aimed to create and evaluate a deep learning algorithm for automatically segmenting kidneys and renal cysts in patients with autosomal dominant polycystic kidney disease (ADPKD) using MRI scans.
- Researchers used a combination of historical MRI data and added normal renal scans to train the algorithm, comparing its results to those from human raters to measure accuracy.
- While the algorithm performed fairly well within the variability of human ratings, it tended to underestimate the total kidney and cyst volume, particularly in cases with very large cyst volumes.
Dr. Carolyn Meltzer: Pioneer, innovator, mentor, and 2023 ACR Gold Medal winner.
Clin Imaging
October 2024
Keck School of Medicine of USC, 1975 Zonal Ave, Los Angeles, CA 90033, USA.
Dr. Carolyn Meltzer is an extraordinary radiologist, researcher, mentor, and distinguished leader who deserves recognition for her immense impact on the discipline of radiology. This article serves to acknowledge and celebrate Dr.
View Article and Find Full Text PDFA preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.
Br J Radiol
August 2024
Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea.
Objectives: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement compared with other image reconstructions.
Methods: This single-centre retrospective study included 50 patients (mean age, 59 years; range, 44-81 years; 13 men) who were treated with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement between January and July 2023. The images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR), and SR-DLR.
A Prospective Study of the Diagnostic Performance of Photon-Counting CT Compared With MRI in the Characterization of Renal Masses.
Invest Radiol
November 2024
From the Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD (F.H., F.D.F., P.Y., M.N., M.D., R.G., E.C.J., E.B.T., A.A.M.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD (N.G., M.W.B., W.M.L.); Siemens Healthineers, Malvern, PA (P.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, AL (M.N.); Laboratory of Imaging Physics, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (M.W., H.W.); and Center for Interventional Oncology, Radiology, and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, (W.F.P.).
Objectives: The aim of this study was to assess the interreader reliability and per-RCC sensitivity of high-resolution photon-counting computed tomography (PCCT) in the detection and characterization of renal masses in comparison to MRI.
Materials And Methods: This prospective study included 24 adult patients (mean age, 52 ± 14 years; 14 females) who underwent PCCT (using an investigational whole-body CT scanner) and abdominal MRI within a 3-month time interval and underwent surgical resection (partial or radical nephrectomy) with histopathology (n = 70 lesions). Of the 24 patients, 17 had a germline mutation and the remainder were sporadic cases.
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