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Letourneau-Guillon L, Camirand D, Guilbert F, Forghani R. Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clin N Am 2020; 30:e1-e15. [PMID: 33039002 DOI: 10.1016/j.nic.2020.08.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
There is great potential for artificial intelligence (AI) applications, especially machine learning and natural language processing, in medical imaging. Much attention has been garnered by the image analysis tasks for diagnostic decision support and precision medicine, but there are many other potential applications of AI in radiology and have potential to enhance all levels of the radiology workflow and practice, including workflow optimization and support for interpretation tasks, quality and safety, and operational efficiency. This article reviews the important potential applications of informatics and AI related to process improvement and operations in the radiology department.
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Affiliation(s)
- Laurent Letourneau-Guillon
- Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), 1051, rue Sanguinet, Montréal, Quebec H2X 0C1, Canada; Centre de Recherche du CHUM (CRCHUM), 900 St Denis St, Montréal, Quebec H2X 0A9, Canada.
| | - David Camirand
- Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), 1051, rue Sanguinet, Montréal, Quebec H2X 0C1, Canada
| | - Francois Guilbert
- Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), 1051, rue Sanguinet, Montréal, Quebec H2X 0C1, Canada; Centre de Recherche du CHUM (CRCHUM), 900 St Denis St, Montréal, Quebec H2X 0A9, Canada
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montréal, Quebec H4A 3S5, Canada; Department of Radiology, McGill University, 1650 Cedar Avenue, Montréal, Quebec H3G 1A4, Canada; Segal Cancer Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Cote Ste-Catherine Road, Montréal, Quebec H3T 1E2, Canada; Gerald Bronfman Department of Oncology, McGill University, Suite 720, 5100 Maisonneuve Boulevard West, Montréal, Quebec H4A3T2, Canada; Department of Otolaryngology - Head and Neck Surgery, Royal Victoria Hospital, McGill University Health Centre, 1001 boul. Decarie Boulevard, Montréal, Quebec H3A 3J1, Canada; 4intelligent Inc., Cote St-Luc, Quebec H3X 4A6, Canada
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Jiang C, Cao J, Williamson C, Farzaneh N, Rajajee V, Gryak J, Najarian K, Soroushmehr SMR. Midline Shift vs. Mid-Surface Shift: Correlation with Outcome of Traumatic Brain Injuries. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2019; 2019:1083-1086. [PMID: 33569243 PMCID: PMC7871460 DOI: 10.1109/bibm47256.2019.8983159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Traumatic brain injury (TBI) is a major health and socioeconomic problem globally that is associated with a high level of mortality. Early and accurate diagnosis and prognosis of TBI is important in patient management and preventing any secondary injuries. Computer tomography (CT) imaging assists physicians in diagnosing injury and guiding treatment. One of the clinical parameters extracted from CT images is midline shift, a measure of linear displacement in brain structure, which is correlated with TBI patient outcomes. However, only a tiny fraction of the overall tissue displacement is quantified through this parameter. In this paper, a novel measurement of overall mid-surface shift is proposed that quantifies the total volume of brain tissue shifted across the midline. When compared to traditional midline shift, mid-surface shift has a stronger correlation with TBI patient outcomes.
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Affiliation(s)
- Cheng Jiang
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
| | - Jie Cao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Craig Williamson
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
| | - Negar Farzaneh
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Venkatakrishna Rajajee
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI
| | - S M Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI
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