863 results match your criteria disclosed deep


Automated detection of artefacts in neonatal EEG with residual neural networks.

Comput Methods Programs Biomed 2021 May 24;208:106194. Epub 2021 May 24.

Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; BaBA center, Department of Children's Clinical Neurophysiology, Children's hospital, HUS Medical Imaging center, Helsinki University Central Hospital and University of Helsinki, Finland. Electronic address:

Background And Objective: To develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals.

Methods: As part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Read More

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Discordance Between Respiratory Drive and Sedation Depth in Critically Ill Patients Receiving Mechanical Ventilation.

Crit Care Med 2021 Jun 9. Epub 2021 Jun 9.

Center for Acute Respiratory Failure, Columbia University College of Physicians and Surgeons and New York-Presbyterian Hospital, New York, NY. Department of Pharmacy, NewYork-Presbyterian Hospital, New York, NY. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Taibah University, Medina, Saudi Arabia. Department of Pharmacy, UC San Diego Health, San Diego, CA. Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY.

Objectives: In mechanically ventilated patients, deep sedation is often assumed to induce "respirolysis," that is, lyse spontaneous respiratory effort, whereas light sedation is often assumed to preserve spontaneous effort. This study was conducted to determine validity of these common assumptions, evaluating the association of respiratory drive with sedation depth and ventilator-free days in acute respiratory failure.

Design: Prospective cohort study. Read More

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Reasons for the Overuse of Sedatives and Deep Sedation for Mechanically Ventilated Coronavirus Disease 2019 Patients.

Crit Care Med 2021 Jun 2. Epub 2021 Jun 2.

Both authors: Clinical Pharmaceutics Department, Affiliated Yueqing Hospital, Wenzhou Medical University, Yueqing, People's Republic of China.

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Using Nonheparin Anticoagulant to Treat a Near-Fatal Case With Multiple Venous Thrombotic Lesions During ChAdOx1 nCoV-19 Vaccination-Related Vaccine-Induced Immune Thrombotic Thrombocytopenia.

Crit Care Med 2021 Jun 1. Epub 2021 Jun 1.

Department of Anesthesia and Critical Care, University of Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France. Department of Biology, Hemostasis Unit, University of Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France. Department of Vascular Medicine, University of Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France.

Objectives: To describe the successful recovery from multiple and life-threatening venous thrombosis after ChAdOx1 nCoV-19 vaccination.

Design: Case report.

Setting: University Hospital. Read More

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Is Microthrombosis the Main Pathology in Coronavirus Disease 2019 Severity?-A Systematic Review of the Postmortem Pathologic Findings.

Crit Care Explor 2021 May 20;3(5):e0427. Epub 2021 May 20.

Departments of Anesthesiology and Perioperative Medicine, University of Louisville School of Medicine, Louisville, KY.

This systematic review attempts to retrieve and report the findings of postmortem studies including the histopathologic data of deceased coronavirus disease 2019 patients and to review the manifestations of coronavirus disease 2019-associated thrombotic pathologies reported in the recent literature.

Data Sources: PubMed, Excerpta Medica Database, and Cochrane library between December 1, 2019, and August 26, 2020.

Study Selection: Investigators screened 360 unique references, retrieved published autopsy series, and report on the postmortem histopathologic information on patients who had died of coronavirus disease 2019. Read More

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[Motoda Nagazane's standards of Confucianism - between Confucianism and Emperor Centralism].

Authors:
Seogin Eom

F1000Res 2021 6;10:272. Epub 2021 Apr 6.

Faculty of Humanities and Social Sciences, University of Tsukuba, Tsukuba, Ibakaki, Japan.

This paper discusses the ideological significance of the activities of Motoda Nagazane who, in the latter half of his life, became an attendant of Emperor Meiji as a member of the Kumamoto school of practical science. Whilst there were trends towards modernisation and Westernisation, Motoda Nagazane led a conservative reaction attempting to restore Confucianist politics/policies. I scrutinise the theories of revolution and lineage considering the history of East Asian Confucianism and comparing Motoda's assertions to the views expressed by Kumazawa Banzan. Read More

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Machine learning for detection of interictal epileptiform discharges.

Clin Neurophysiol 2021 Jul 21;132(7):1433-1443. Epub 2021 Apr 21.

Department of Clinical Neurophysiology, Institute for Technical Medicine, University of Twente, Technical Medical Centre, Enschede, the Netherlands; Neurocentrum, Medisch Spectrum Twente MST, Enschede, the Netherlands. Electronic address:

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches. Read More

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Temporary Diverting End-Colostomy in Critically Ill Children with Severe Perianal Wound Infection.

Adv Skin Wound Care 2021 Jun;34(6):322-326

At the Ankara University School of Medicine, Turkey, Emrah Gün, MD, is Fellow, Department of Pediatric Critical Care Medicine; Tanil Kendirli, MD, is Professor, Department of Pediatric Critical Care Medicine; Edin Botan, MD, is Fellow, Department of Pediatric Critical Care Medicine; Halil Özdemir, MD, is Associate Professor, Department of Pediatric Infectious Disease; Ergin Çiftçi, MD, is Professor, Department of Pediatric Infectious Disease; Kübra Konca, MD, is Fellow, Department of Pediatric Infectious Disease; Meltem Koloğlu, MD, is Professor, Department of Pediatric Surgery; Gülnur Göllü, MD, is Associate Professor, Department of Pediatric Surgery; Özlem Selvi Can, MD, is Associate Professor, Department of Pediatric Anesthesia; Ercan Tutar, MD, is Professor, Department of Pediatric Cardiology; Ahmet Rüçhan Akar, MD, is Professor, Department of Cardiovascular Surgery, Heart Center, Cebeci Hospitals; and Erdal İnce, MD, is Professor, Department of Pediatric Infectious Disease. Acknowledgments: The authors wish to thank all the pediatric ICU nursing staff for all their efforts and support for our critically ill pediatric patients. The authors have disclosed no financial relationships related to this article. Submitted June 24, 2020; accepted in revised form September 25, 2020.

Abstract: Broad and deep perianal wounds are challenging in both adult and pediatric ICUs. These wounds, if contaminated with gastrointestinal flora, can cause invasive sepsis and death, and recovery can be prolonged. Controlling the source of infection without diverting stool from the perianal region is complicated. Read More

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Quantitative Chest CT in COPD: Can Deep Learning Enable the Transition?

Radiol Cardiothorac Imaging 2021 Apr 8;3(2):e210044. Epub 2021 Apr 8.

Division of Thoracic Imaging, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Boston, MA 02114.

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Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.

Radiol Cardiothorac Imaging 2021 Apr 8;3(2):e200477. Epub 2021 Apr 8.

Department of Radiology (K.A.H., N.Y., T.R., S.K., A.H.) and Department of Medicine (D.J.C.), University of California San Diego, 9452 Medical Center Dr, La Jolla, CA 92037; Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.); and Department of Radiology, National Jewish Health, Denver, Colo (D.A.L.).

Purpose: To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality.

Materials And Methods: In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). Read More

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Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT.

Radiol Cardiothorac Imaging 2021 Apr 15;3(2):e190219. Epub 2021 Apr 15.

Department of Biomedical Engineering and Physics (B.D.d.V., I.I.), Cardiovascular Institute (B.D.d.V., I.I.), and Department of Radiology and Nuclear Medicine (I.I.), Amsterdam University Medical Center, Meibergdreef 9, Amsterdam, the Netherlands; and Image Sciences Institute (B.D.d.V., N.L., I.I.) and Department of Radiology (P.A.d.J., I.I.), University Medical Center Utrecht, Utrecht, the Netherlands.

Purpose: To examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality.

Materials And Methods: This retrospective study included 5564 participants who underwent low-dose CT from the National Lung Screening Trial between August 2002 and April 2004, who were followed until December 2009. A deep learning network was trained to quantify six types of vascular calcification: thoracic aorta calcification (TAC); aortic and mitral valve calcification; and coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. Read More

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Automated Analysis of Alignment in Long-Leg Radiographs by Using a Fully Automated Support System Based on Artificial Intelligence.

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

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CT-less Direct Correction of Attenuation and Scatter in the Image Space Using Deep Learning for Whole-Body FDG PET: Potential Benefits and Pitfalls.

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

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Automated Segmentation of Abdominal Skeletal Muscle on Pediatric CT Scans Using Deep Learning.

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

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Deep Learning to Quantify Pulmonary Edema in Chest Radiographs.

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

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Endotracheal Tube Position Assessment on Chest Radiographs Using Deep Learning.

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

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January 2021

Deep Learning-based Automated Segmentation of Left Ventricular Trabeculations and Myocardium on Cardiac MR Images: A Feasibility Study.

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

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January 2021

A Multisite Study of a Breast Density Deep Learning Model for Full-Field Digital Mammography and Synthetic Mammography.

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

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January 2021

Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies.

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

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November 2020

The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings.

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

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November 2020

Automatic Detection of Inadequate Pediatric Lateral Neck Radiographs of the Airway and Soft Tissues using Deep Learning.

Radiol Artif Intell 2020 Sep 30;2(5):e190226. Epub 2020 Sep 30.

Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229.

Purpose: To develop and validate a deep learning (DL) algorithm to identify poor-quality lateral airway radiographs.

Materials And Methods: A total of 1200 lateral airway radiographs obtained in emergency department patients between January 1, 2000, and July 1, 2019, were retrospectively queried from the picture archiving and communication system. Two radiologists classified each radiograph as adequate or inadequate. Read More

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September 2020

Improved Segmentation and Detection Sensitivity of Diffusion-weighted Stroke Lesions with Synthetically Enhanced Deep Learning.

Radiol Artif Intell 2020 Sep 16;2(5):e190217. Epub 2020 Sep 16.

Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Gloriastrasse 35, 8092 Zürich, Switzerland (C.F., N. Scherrer, S.K.); Stanford Stroke Center, Department of Neurology, Stanford University, Stanford, Calif (S.C., J.M., M.L.); and Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel, Basel, Switzerland (J.O., V.S.Z., N. Schmidt, H.C.B.).

Purpose: To compare the segmentation and detection performance of a deep learning model trained on a database of human-labeled clinical stroke lesions on diffusion-weighted (DW) images to a model trained on the same database enhanced with synthetic stroke lesions.

Materials And Methods: In this institutional review board-approved study, a stroke database of 962 cases (mean patient age ± standard deviation, 65 years ± 17; 255 male patients; 449 scans with DW positive stroke lesions) and a normal database of 2027 patients (mean age, 38 years ± 24; 1088 female patients) were used. Brain volumes with synthetic stroke lesions on DW images were produced by warping the relative signal increase of real strokes to normal brain volumes. Read More

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September 2020

Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning.

Radiol Artif Intell 2020 Sep 30;2(5):e190183. Epub 2020 Sep 30.

Departments of Radiology (J.C.C., K.A.P., S.H., P.R., G.M.C., D.C.V., Q.H., B.J.E.) and Cardiovascular Science (Z.A.), Mayo Clinic Rochester, 200 First St. SW, RO_PB_02_RIL, Rochester, MN 55905; Department of Radiology, Khon Kaen University, Khon Kaen, Thailand (A.B.); Department of Health Sciences Research, Mayo Clinic Florida, Jacksonville, Fla (A.D.W.); and Department of Internal Medicine, Ascension St. John Hospital, Detroit, Mich (A.Z.).

Purpose: To develop a deep learning model that segments intracranial structures on head CT scans.

Materials And Methods: In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27-95 years) acquired between August and December 2018 was used for model development. Eleven intracranial structures were manually annotated on the axial oblique series. Read More

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September 2020

Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study.

Radiol Artif Intell 2020 Sep 23;2(5):e190146. Epub 2020 Sep 23.

Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104 (J.D.R., L.X., A.K., J.M.E., T.C., I.M.N., S.M., J.C.G.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (J.D.R., A.M.R.); Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pa (X.L., J.W.); University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa (M.T.D.); Mecklenburg Radiology Associates, Charlotte, NC (E.J.B.); Department of Radiology, University of Texas, Austin, Tex (R.N.B.); and Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, Pa (I.M.N.).

Purpose: To develop and validate a system that could perform automated diagnosis of common and rare neurologic diseases involving deep gray matter on clinical brain MRI studies.

Materials And Methods: In this retrospective study, multimodal brain MRI scans from 212 patients (mean age, 55 years ± 17 [standard deviation]; 113 women) with 35 neurologic diseases and normal brain MRI scans obtained between January 2008 and January 2018 were included (110 patients in the training set, 102 patients in the test set). MRI scans from 178 patients (mean age, 48 years ± 17; 106 women) were used to supplement training of the neural networks. Read More

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September 2020

Robust Deep Learning-based Segmentation of Glioblastoma on Routine Clinical MRI Scans Using Sparsified Training.

Radiol Artif Intell 2020 Sep 30;2(5):e190103. Epub 2020 Sep 30.

Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands (R.S.E., M.v.H., M.G.W.); Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands (M.V., F.B., H.V., J.C.d.M.); Neurosurgical Center Amsterdam, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands (D.M.J.M., P.C.D.W.H.); Institutes of Neurology & Healthcare Engineering, University College London, London, England (F.B.); Faculty of Biology, Medicine & Health, Division of Cancer Sciences, University of Manchester and Christie NHS Trust, Manchester, England (M.v.H.); Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Humanitas Research Hospital, IRCCS, Milan, Italy (L.B., M.C.N., M.R., T.S.); Department of Neurologic Surgery, University of California-San Francisco, San Francisco, Calif (M.S.B., S.H.J.); Department of Neurosurgery, Medical University Vienna, Vienna, Austria (B.K., G.W.); Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria (J.F.); Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands (P.A.J.T.R.); and Department of Neurologic Surgery, Hôpital Lariboisière, Paris, France (E.M.).

Purpose: To improve the robustness of deep learning-based glioblastoma segmentation in a clinical setting with sparsified datasets.

Materials And Methods: In this retrospective study, preoperative T1-weighted, T2-weighted, T2-weighted fluid-attenuated inversion recovery, and postcontrast T1-weighted MRI from 117 patients (median age, 64 years; interquartile range [IQR], 55-73 years; 76 men) included within the Multimodal Brain Tumor Image Segmentation (BraTS) dataset plus a clinical dataset (2012-2013) with similar imaging modalities of 634 patients (median age, 59 years; IQR, 49-69 years; 382 men) with glioblastoma from six hospitals were used. Expert tumor delineations on the postcontrast images were available, but for various clinical datasets, one or more sequences were missing. Read More

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September 2020

Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs.

Radiol Artif Intell 2020 Jul 29;2(4):e190198. Epub 2020 Jul 29.

Department of Diagnostic Imaging, Rhode Island Hospital/Hasbro Children's Hospital, The Warren Alpert Medical School of Brown University, 593 Eddy St, Providence, RI 02903 (I.P., D.W.S., R.S.A.); Department of Diagnostic Imaging and Lifespan Biostatistics Core, Rhode Island Hospital, Providence, RI (G.L.B.); Department of Radiology, Columbia University Medical Center, New York, NY (S.M., C.R.); and Department of Emergency Medicine, University of Florida Shands Hospital, Gainesville, Fla (D.M.).

Purpose: To develop a deep learning approach to bone age assessment based on a training set of developmentally normal pediatric hand radiographs and to compare this approach with automated and manual bone age assessment methods based on Greulich and Pyle (GP).

Methods: In this retrospective study, a convolutional neural network (trauma hand radiograph-trained deep learning bone age assessment method [TDL-BAAM]) was trained on 15 129 frontal view pediatric trauma hand radiographs obtained between December 14, 2009, and May 31, 2017, from Children's Hospital of New York, to predict chronological age. A total of 214 trauma hand radiographs from Hasbro Children's Hospital were used as an independent test set. Read More

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MRI Manufacturer Shift and Adaptation: Increasing the Generalizability of Deep Learning Segmentation for MR Images Acquired with Different Scanners.

Radiol Artif Intell 2020 Jul 1;2(4):e190195. Epub 2020 Jul 1.

Biomedical Engineering Center, Fudan University, Shanghai, China (W.Y., Y.W.); Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (L.H., L.X.); Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University, Shanghai, China (S.G., F.Y.); and Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands (Q.T.).

Purpose: To quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy.

Materials And Methods: This retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 ( = 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. Read More

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Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks.

Radiol Artif Intell 2020 Jul 29;2(4):e190178. Epub 2020 Jul 29.

Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein-Zuid 10, Nijmegen 6500 HB, the Netherlands.

Purpose: To implement and test a deep learning approach for the segmentation of the arterial and venous cerebral vasculature with four-dimensional (4D) CT angiography.

Materials And Methods: Patients who had undergone 4D CT angiography for the suspicion of acute ischemic stroke were retrospectively identified. A total of 390 patients evaluated in 2014 ( = 113) or 2018 ( = 277) were included in this study, with each patient having undergone one 4D CT angiographic scan. Read More

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Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning.

Radiol Artif Intell 2020 Jul 22;2(4):e190102. Epub 2020 Jul 22.

Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10 (Route 767), 6525 GA, Nijmegen, the Netherlands (G.E.H.M., J.B., E.T.S., M.P., B.v.G., C.J.); and Fraunhofer MEVIS, Bremen, Germany (B.v.G.).

Purpose: To develop a fully automated algorithm for spleen segmentation and to assess the performance of this algorithm in a large dataset.

Materials And Methods: In this retrospective study, a three-dimensional deep learning network was developed to segment the spleen on thorax-abdomen CT scans. Scans were extracted from patients undergoing oncologic treatment from 2014 to 2017. Read More

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Deep Learning-based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images.

Radiol Artif Intell 2020 Jul 15;2(4):e190006. Epub 2020 Jul 15.

Departments of Radiology (G.C., N.J., M.P.R.) and Physiology (T.H.H., A.T.D.X.), Hôpital Cochin, and Reference Center for Rare Systemic Autoimmune Diseases of Ile de France, Hôpital Cochin (A.R., N. Benmostefa, L.M.), Assistance Publique-Hôpitaux de Paris, Université de Paris, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France; Center for Visual Computing, Ecole CentraleSupelec, Gif-sur-Yvette, France (G.C., M.V., E.I.Z., C.M., N.P.); Department of Radiology, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel (G.A.); TheraPanacea, Paris, France (R.M., N. Bus, N.P.); and Departments of Internal Medicine and Inflammatory Disorders (A.M.) and Radiology (L.M.C.), Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris, Sorbonne Université, Paris, France.

Purpose: To develop a deep learning algorithm for the automatic assessment of the extent of systemic sclerosis (SSc)-related interstitial lung disease (ILD) on chest CT images.

Materials And Methods: This retrospective study included 208 patients with SSc (median age, 57 years; 167 women) evaluated between January 2009 and October 2017. A multicomponent deep neural network (AtlasNet) was trained on 6888 fully annotated CT images (80% for training and 20% for validation) from 17 patients with no, mild, or severe lung disease. Read More

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