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Radiol Artif Intell 2020 Jul 29;2(4):e200150. Epub 2020 Jul 29.

Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, MC 5105, Stanford, CA 94305.

Radiol Artif Intell 2020 May 27;2(3):e200068. Epub 2020 May 27.

Office of Student Affairs, Saint Louis University, 1402 S Grand Blvd, Caroline 100, St Louis, MO 63104.

Radiol Artif Intell 2020 Nov 14;2(6):e200230. Epub 2020 Oct 14.

Microsoft Research New England, 1 Memorial Dr, Cambridge, MA 02142-1313 (N.A.T.); and Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.J.W.).

Radiol Artif Intell 2020 Nov 11;2(6):e200207. Epub 2020 Nov 11.

Department of Radiology and Cancer Research UK Cambridge Centre, University of Cambridge, Box 218, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, England.

Radiol Artif Intell 2021 Mar 24;3(2):e210004. Epub 2021 Mar 24.

Department of Radiology and Imaging Sciences, University of Utah School of Medicine, 30 North 1900 East, Room 1A71, Salt Lake City, UT 84132.

Radiol Artif Intell 2020 Jul 29;2(4):e209002. Epub 2020 Jul 29.

[This corrects the article DOI: 10.1148/ryai.2020190211. Read More

Radiol Artif Intell 2020 Sep 30;2(5):e200075. Epub 2020 Sep 30.

Department of Radiology, University of Pennsylvania Perelman School of Medicine, 606E Goddard Bldg, 3700 Hamilton Walk, Philadelphia, PA 19104.

Radiol Artif Intell 2021 Mar 3;3(2):e210005. Epub 2021 Mar 3.

Department of Medical Imaging, CISSS Lanaudière, affiliated with Laval University, 1000 Blvd St-Anne, Saint-Charles-Borromée, QC, Canada J6E 6J2.

Radiol Artif Intell 2021 Mar 10;3(2):e210009. Epub 2021 Feb 10.

Departments of Radiology (S.D.O., M.B.) and Surgery (S.D.O.), Medical College of Wisconsin, 9200 W Wisconsin Ave, Milwaukee, WI 53226.

Radiol Artif Intell 2021 Mar 17;3(2):e200300. Epub 2021 Mar 17.

Institute of Radiology, Cantonal Hospital Münsterlingen, Spital Thurgau, Münsterlingen, Spitalcampus 1, 8596 Munsterlingen, Switzerland; and University of Zurich, Zürich, Switzerland.

Radiol Artif Intell 2021 Mar 20;3(2):e200254. Epub 2021 Jan 20.

Department of Medical Imaging, Unity Health Toronto, University of Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (E.C.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.C.K.); Diagnósticos da América SA (Dasa) (F.C.K.); Department of Radiology, University of Kentucky, Lexington, Ky (S.B.H.); Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (C.C.W.); Department of Radiology, Stanford University, Stanford, Calif (M.P.L., S.S.H.); Department of Radiology, The Ohio State University, Columbus, Ohio (L.M.P.); Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Mass (J.K.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); MD.ai, New York, NY (A.S.); Department of Radiology, Koc University School of Medicine, Istanbul, Turkey (E.A.); Department of Radiology and Nuclear Medicine, Alfred Health, Monash University, Melbourne, Australia (M.L.); Department of Radiodiagnosis, Fortis Escorts Heart Institute, New Delhi, India (P.K.); Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, University of Jordan, Amman, Jordan (K.A.M.); Department of Departamento de Imagenología, Hospital Regional de Alta Especialidad de la Península de Yucatán, Mérida, Mexico (D.C.N.R.); Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (J.W.S.); Department of Radiology, Cooper University Hospital, Camden, NJ (P. Germaine); A Coruña University Hospital, A Coruña, Spain (E.C.L.); Swiss Medical Group, Buenos Aires, Argentina (T.A.); Inland Imaging, Spokane, Wash (P. Gupta); AMRI Hospitals, Kolkata, India (M.J.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (F.U.K.); Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Md (C.T.L.); Department of Radiology and Imaging Sciences, Tata Medical Center, Kolkata, India (S.S.); Department of Radiology, University of New Mexico, Albuquerque, NM (J.W.R.); Department of Radiology, Universitair Ziekenhuis Brussel, Jette, Belgium (C.C.B.); Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, Calif (J.M).

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Radiol Artif Intell 2021 Mar 23;3(2):e200198. Epub 2020 Dec 23.

Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).

Purpose: To develop and validate a deep learning-based method for automatic quantitative analysis of lower-extremity alignment.

Materials And Methods: In this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data ( = 109), a U-Net convolutional neural network was trained to segment the femur and tibia versus manual segmentation. Read More

Radiol Artif Intell 2021 Mar 2;3(2):e200137. Epub 2020 Dec 2.

Department of Radiology and Biomedical Imaging (J.Y., J.H.S., S.C.B., G.TG., Y.S.) and Physics Research Laboratory (J.Y., G.T.G., Y.S.), University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94143-0946.

Purpose: To demonstrate the feasibility of CT-less attenuation and scatter correction (ASC) in the image space using deep learning for whole-body PET, with a focus on the potential benefits and pitfalls.

Materials And Methods: In this retrospective study, 110 whole-body fluorodeoxyglucose (FDG) PET/CT studies acquired in 107 patients (mean age ± standard deviation, 58 years ± 18; age range, 11-92 years; 72 females) from February 2016 through January 2018 were randomly collected. A total of 37. Read More

Radiol Artif Intell 2021 Mar 6;3(2):e200130. Epub 2021 Jan 6.

Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5031, Cincinnati, OH 45229-3026 (J.C., E.S., L.A.G., A.T.T., S.B.); and Departments of Radiology (E.S., A.T.T., S.B.) and Pediatrics (A.T.T.), University of Cincinnati College of Medicine, Cincinnati, Ohio.

Purpose: To automate skeletal muscle segmentation in a pediatric population using convolutional neural networks that identify and segment the L3 level at CT.

Materials And Methods: In this retrospective study, two sets of U-Net-based models were developed to identify the L3 level in the sagittal plane and segment the skeletal muscle from the corresponding axial image. For model development, 370 patients (sampled uniformly across age group from 0 to 18 years and including both sexes) were selected between January 2009 and January 2019, and ground truth L3 location and skeletal muscle segmentation were manually defined. Read More

Radiol Artif Intell 2021 Mar 18;3(2):e200024. Epub 2020 Nov 18.

Departments of Radiology (T.J.O., Y.X., E.S., T.B., Y.S.N., R.M.P.) and Health Systems Information Resources (C.B.), University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 5323 Harry Hines Blvd, Dallas TX 75235.

Purpose: To determine how to optimize the delivery of machine learning techniques in a clinical setting to detect intracranial hemorrhage (ICH) on non-contrast-enhanced CT images to radiologists to improve workflow.

Materials And Methods: In this study, a commercially available machine learning algorithm that flags abnormal noncontrast CT examinations for ICH was implemented in a busy academic neuroradiology practice between September 2017 and March 2019. The algorithm was introduced in three phases: as a "pop-up" widget on ancillary monitors, as a marked examination in reading worklists, and as a marked examination for reprioritization based on the presence of the flag. Read More

Radiol Artif Intell 2021 Mar 6;3(2):e190228. Epub 2021 Jan 6.

Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215 (S.H., S.J.B.); Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Mass (R.L., P.G.); and Clinical Informatics Solutions and Services, Philips Research, Cambridge, Mass (X.W., S.D.).

Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs.

Materials And Methods: In this retrospective study, 369 071 chest radiographs and associated radiology reports from 64 581 patients (mean age, 51.71 years; 54. Read More

Radiol Artif Intell 2021 Mar 23;3(2):e190181. Epub 2020 Dec 23.

Kheiron Medical Technologies Ltd, 116 Old Street, London EC1V 9BG, England (D.K., H.H., A.H., E.K., G.W., T.R., P.K.); and Department of Computing, Imperial College London, London, England (B.G.).

Purpose: To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts.

Materials And Methods: In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 × 1024 pixels by using images from 90 000 patients (average age, 56 years ± 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra-high-dimensional pixel distributions was used, which was based on moment plots. Read More

Radiol Artif Intell 2021 Jan 2;3(1):e200125. Epub 2020 Dec 2.

Department of Radiology, Division of Musculoskeletal Radiology, NYU Langone Health, 301 E 17th St, 6th Floor, New York, NY, 10003 (B.W., C.B., R.S.A.); and Department of Musculoskeletal Imaging, Hôpital Lariboisière, Paris, France (L.P.).

Purpose: To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses.

Materials And Methods: In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years ± 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision ( = 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation ( = 192) were included. Images in patients with a histologic diagnosis ( = 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Read More

Radiol Artif Intell 2021 Jan 20;3(1):e200105. Epub 2021 Jan 20.

Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (B.J.E.); and Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.K.).

Authors show how to use Digital Imaging and Communications in Medicine (DICOM) Query and Retrieve functions to pull a study from a cloud or public picture archiving and communication system (PACS), run an artificial intelligence (AI) algorithm on those images, and store the results back to another (cloud) PACS. Read More

Radiol Artif Intell 2021 Jan 25;3(1):e200103. Epub 2020 Nov 25.

Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George's Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.).

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Radiol Artif Intell 2021 Jan 18;3(1):e200026. Epub 2020 Nov 18.

Department of Radiology, Thomas Jefferson University Hospital, Sidney Kimmel Jefferson Medical College, 132 S 10th St, Philadelphia, PA 19107.

Purpose: To determine the efficacy of deep learning in assessing endotracheal tube (ETT) position on radiographs.

Materials And Methods: In this retrospective study, 22 960 de-identified frontal chest radiographs from 11 153 patients (average age, 60.2 years ± 19. Read More

Radiol Artif Intell 2021 Jan 25;3(1):e200021. Epub 2020 Nov 25.

Departments of Radiology (A.B., A.J.) and Cardiology (G.H.), Hôpital de la Timone Adultes, AP-HM, 264, rue Saint-Pierre 13385 Marseille Cedex 05, France; CRMBM-UMR CNRS 7339, Medical Faculty, Aix-Marseille University, Marseille, France (A.B., J.F., Z.B., M.B., A.J.); I2M-UMR CNRS 7373, Aix-Marseille University, Centrale Marseille, Marseille, France (J.F., B.G.); ImVia Laboratory and University Hospital of Dijon, Bourgogne-Franche Comté University, Dijon, France (A.L.); Department of Radiology, Hôpital de la Croix-Rousse, Hospices Civils de Lyon, Lyon, France (L.B.); Department of Cardiovascular Imaging, Lille University Hospital, Lille, France (F.P.); and Department of Diagnostic Imaging, Rouen University Hospital, Rouen, France (J.N.D.).

Purpose: To develop and evaluate a complete deep learning pipeline that allows fully automated end-diastolic left ventricle (LV) cardiac MRI segmentation, including trabeculations and automatic quality control of the predicted segmentation.

Materials And Methods: This multicenter retrospective study includes training, validation, and testing datasets of 272, 27, and 150 cardiac MR images, respectively, collected between 2012 and 2018. The reference standard was the manual segmentation of four LV anatomic structures performed on end-diastolic short-axis cine cardiac MRI: LV trabeculations, LV myocardium, LV papillary muscles, and the LV blood cavity. Read More

Radiol Artif Intell 2021 Jan 4;3(1):e200015. Epub 2020 Nov 4.

Whiterabbit AI, Inc, 3930 Freedom Circle, Suite 101, Santa Clara, CA 95054 (T.P.M., S.S., B.M., J.S., M.P.S., S.P., A.L., R.M.H., N.G., D.S.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.M., J.G., S.M.M., R.L.W.); and Peninsula Diagnostic Imaging, San Mateo, Calif (S.C.M.).

Purpose: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multisite setting for synthetic two-dimensional mammographic (SM) images derived from digital breast tomosynthesis examinations by using full-field digital mammographic (FFDM) images and limited SM data.

Materials And Methods: A DL model was trained to predict BI-RADS breast density by using FFDM images acquired from 2008 to 2017 (site 1: 57 492 patients, 187 627 examinations, 750 752 images) for this retrospective study. The FFDM model was evaluated by using SM datasets from two institutions (site 1: 3842 patients, 3866 examinations, 14 472 images, acquired from 2016 to 2017; site 2: 7557 patients, 16 283 examinations, 63 973 images, 2015 to 2019). Read More

Radiol Artif Intell 2020 Nov 25;2(6):e200228. Epub 2020 Nov 25.

Department of Medical Imaging Research, Royal Adelaide Hospital, Adelaide, Australia.

Radiol Artif Intell 2020 Nov 4;2(6):e200057. Epub 2020 Nov 4.

Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (W.F.W., M.T.C., K.M., S.A.G., E.G., M.H.R., G.C.G., K.P.A.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (W.F.W., M.T.C., K.M., K.P.A.).

Artificial intelligence and machine learning (AI-ML) have taken center stage in medical imaging. To develop as leaders in AI-ML, radiology residents may seek a formative data science experience. The authors piloted an elective Data Science Pathway (DSP) for 4th-year residents at the authors' institution in collaboration with the MGH & BWH Center for Clinical Data Science (CCDS). Read More

Radiol Artif Intell 2020 Nov 28;2(6):e200010. Epub 2020 Oct 28.

Department of Diagnostic and Interventional Radiology, Medical Image and Data Analysis, University Hospital Tübingen, Hoppe-Seyler-Str 3, 72076 Tübingen, Germany (T.K., T.H., M.F., K.N., S.G.); Department of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany (T.K., M.F., M.S., B.Y.); School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, England (T.K.); Department of Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen, Germany (T.H.); Department of Diagnostic and Interventional Radiology, Section of Experimental Radiology, University Hospital Tübingen, Tübingen, Germany (M.F., M.S., F.S., J.M.); Department of Internal Medicine IV, Eberhard Karls University, Tübingen, Germany (A.F.); Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Zentrum München, University of Tübingen, Tübingen, Germany (A.F., F.S., J.M.); German Center for Diabetes Research (DZD), Tübingen, Germany (A.F., H.U.H., F.S., J.M.); and Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (F.B.).

Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI.

Materials And Methods: Quantification and localization of different adipose tissue compartments derived from whole-body MR images is of high interest in research concerning metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automated segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. Read More

Radiol Artif Intell 2020 Nov 11;2(6):e200004. Epub 2020 Nov 11.

Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).

Purpose: To provide an overview of important factors to consider when purchasing radiology artificial intelligence (AI) software and current software offerings by type, subspecialty, and modality.

Materials And Methods: Important factors for consideration when purchasing AI software, including key decision makers, data ownership and privacy, cost structures, performance indicators, and potential return on investment are described. For the market overview, a list of radiology AI companies was aggregated from the Radiological Society of North America and the Society for Imaging Informatics in Medicine conferences (November 2016-June 2019), then narrowed to companies using deep learning for imaging analysis and diagnosis. Read More

Radiol Artif Intell 2020 Nov 4;2(6):e190218. Epub 2020 Nov 4.

Department of Cancer Physiology (W.M., Y.T., Y.B., R.J.G.) and Gynecologic Oncology (R.W.), H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612; Cancer Institute and Hospital, Chinese Academy of Medical Sciences, Beijing, China (Y.L., N.W.); Department of Computer Science and Engineering, University of South Florida, Tampa, Fla (L.O.H.); Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China (J.T.); and CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China (J.T.).

Purpose: To determine if quantitative features extracted from pretherapy fluorine 18 fluorodeoxyglucose (F-FDG) PET/CT estimate prognosis in patients with locally advanced cervical cancer treated with chemoradiotherapy.

Materials And Methods: In this retrospective study, PET/CT images and outcomes were curated from 154 patients with locally advanced cervical cancer, who underwent chemoradiotherapy from two institutions between March 2008 and June 2016, separated into independent training ( = 78; mean age, 51 years ± 13 [standard deviation]) and testing ( = 76; mean age, 50 years ± 10) cohorts. Radiomic features were extracted from PET, CT, and habitat (subregions with different metabolic characteristics) images that were derived by fusing PET and CT images. Read More

Radiol Artif Intell 2020 Nov 4;2(6):e190208. Epub 2020 Nov 4.

Therapixel SA, 39 Rue Claude Daunesse, 06560 Valbonne, France (S.P., P.C., T.B., P.F.); Radiology & Imaging Services, Hoag Memorial Hospital Presbyterian, Newport Beach, Calif (J.L.); and Department of Statistics, University of Rouen, Rouen, France (J.M.G.).

Purpose: To evaluate the benefits of an artificial intelligence (AI)-based tool for two-dimensional mammography in the breast cancer detection process.

Materials And Methods: In this multireader, multicase retrospective study, 14 radiologists assessed a dataset of 240 digital mammography images, acquired between 2013 and 2016, using a counterbalance design in which half of the dataset was read without AI and the other half with the help of AI during a first session and vice versa during a second session, which was separated from the first by a washout period. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were assessed as endpoints. Read More

Radiol Artif Intell 2020 Nov 14;2(6):e190137. Epub 2020 Oct 14.

Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.M.S., T.P., J.A., C.E.K., T.S.C.); and Boston University School of Medicine, Boston, Mass (J.M.S.).

Purpose: To evaluate publicly available de-identification tools on a large corpus of narrative-text radiology reports.

Materials And Methods: In this retrospective study, 21 categories of protected health information (PHI) in 2503 radiology reports were annotated from a large multihospital academic health system, collected between January 1, 2012 and January 8, 2019. A subset consisting of 1023 reports served as a test set; the remainder were used as domain-specific training data. Read More