The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.

Journal: Biomedical engineering online
PMID:

Abstract

BACKGROUND: Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) is often used to determine the radiologic severity of CRS and make clinical decisions. This proof-of-concept study aimed to develop an automated algorithm combining a convolutional neural network (CNN) for sinus segmentation with post-processing to compute LMS directly from CT scans.

Authors

  • Daniel J Lee
  • Mohammad Hamghalam
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China; Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran. Electronic address: m.hamghalam@gmail.com.
  • Lily Wang
    Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
  • Hui-Ming Lin
    Li Ka Shing Centre for Healthcare Analytics Research and Training, St. Michael's Hospital, Toronto, Canada.
  • Errol Colak
    Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
  • Muhammad Mamdani
    Unity Health Toronto (Verma, Murray, Straus, Pou-Prom, Mamdani); Li Ka Shing Knowledge Institute of St. Michael's Hospital (Verma, Straus, Pou-Prom, Mamdani); Department of Medicine (Verma, Shojania, Straus, Mamdani) and Institute of Health Policy, Management, and Evaluation (Verma, Mamdani) and Department of Statistics (Murray), University of Toronto, Toronto, Ont.; University of Alberta (Greiner); Alberta Machine Intelligence Institute (Greiner), Edmonton, Alta.; Montreal Institute for Learning Algorithms (Cohen), Montréal, Que.; Centre for Quality Improvement and Patient Safety (Shojania), University of Toronto; Sunnybrook Health Sciences Centre (Shojania); Vector Institute (Ghassemi, Mamdani) and Department of Computer Science (Ghassemi); Leslie Dan Faculty of Pharmacy (Mamdani), University of Toronto, Toronto, Ont.; Department of Radiology, Stanford University (Cohen), Stanford, Calif. muhammad.mamdani@unityhealth.to amol.verma@mail.utoronto.ca.
  • Amber L Simpson
    Queen's University, Kingston, ON, CANADA.
  • John M Lee
    Department of Otolaryngology, Head and Neck Surgery, St. Michael's Hospital, Toronto, Ontario, Canada.