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Koul A, Bawa RK, Kumar Y. Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 30:831-864. [PMID: 36189431 PMCID: PMC9516534 DOI: 10.1007/s11831-022-09818-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.
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Affiliation(s)
- Apeksha Koul
- Department of Computer Science and Engineering, Punjabi University, Patiala, Punjab India
| | - Rajesh K. Bawa
- Department of Computer Science, Punjabi University, Patiala, Punjab India
| | - Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
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Raynor WY, Park PSU, Borja AJ, Sun Y, Werner TJ, Ng SJ, Lau HC, Høilund-Carlsen PF, Alavi A, Revheim ME. PET-Based Imaging with 18F-FDG and 18F-NaF to Assess Inflammation and Microcalcification in Atherosclerosis and Other Vascular and Thrombotic Disorders. Diagnostics (Basel) 2021; 11:diagnostics11122234. [PMID: 34943473 PMCID: PMC8700072 DOI: 10.3390/diagnostics11122234] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 01/13/2023] Open
Abstract
Positron emission tomography (PET) imaging with 18F-fluorodeoxyglucose (FDG) represents a method of detecting and characterizing arterial wall inflammation, with potential applications in the early assessment of vascular disorders such as atherosclerosis. By portraying early-stage molecular changes, FDG-PET findings have previously been shown to correlate with atherosclerosis progression. In addition, recent studies have suggested that microcalcification revealed by 18F-sodium fluoride (NaF) may be more sensitive at detecting atherogenic changes compared to FDG-PET. In this review, we summarize the roles of FDG and NaF in the assessment of atherosclerosis and discuss the role of global assessment in quantification of the vascular disease burden. Furthermore, we will review the emerging applications of FDG-PET in various vascular disorders, including pulmonary embolism, as well as inflammatory and infectious vascular diseases.
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Affiliation(s)
- William Y. Raynor
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; (W.Y.R.); (P.S.U.P.); (A.J.B.); (T.J.W.); (A.A.)
| | - Peter Sang Uk Park
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; (W.Y.R.); (P.S.U.P.); (A.J.B.); (T.J.W.); (A.A.)
- Perelman School of Medicine at the University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA;
| | - Austin J. Borja
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; (W.Y.R.); (P.S.U.P.); (A.J.B.); (T.J.W.); (A.A.)
- Perelman School of Medicine at the University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA;
| | - Yusha Sun
- Perelman School of Medicine at the University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA;
| | - Thomas J. Werner
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; (W.Y.R.); (P.S.U.P.); (A.J.B.); (T.J.W.); (A.A.)
| | - Sze Jia Ng
- Department of Medicine, Crozer-Chester Medical Center, Upland, PA 19013, USA; (S.J.N.); (H.C.L.)
| | - Hui Chong Lau
- Department of Medicine, Crozer-Chester Medical Center, Upland, PA 19013, USA; (S.J.N.); (H.C.L.)
| | - Poul Flemming Høilund-Carlsen
- Department of Nuclear Medicine, Odense University Hospital, 5000 Odense C, Denmark;
- Department of Clinical Research, University of Southern Denmark, 5000 Odense C, Denmark
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; (W.Y.R.); (P.S.U.P.); (A.J.B.); (T.J.W.); (A.A.)
| | - Mona-Elisabeth Revheim
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; (W.Y.R.); (P.S.U.P.); (A.J.B.); (T.J.W.); (A.A.)
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Sognsvannsveien 20, 0372 Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Problemveien 7, 0315 Oslo, Norway
- Correspondence: or
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Alavi A, Werner TJ, Hess S, Høilund-Carlsen PF. Regarding “ 18F-GP1, a Novel PET Tracer Designed for High-Sensitivity, Low-Background Detection of Thrombi”. J Nucl Med 2018; 59:350-351. [DOI: 10.2967/jnumed.117.200378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Abstract
PURPOSE Thrombosis in cancer may manifest itself as venous thromboembolic disease or tumor thrombosis (TT). We present our experience with incidentally detected TT on FDG PET/CT in 21 oncologic patients. PATIENTS AND METHODS We retrospectively reviewed all FDG PET/CT examinations during a 5-year period at the Army Hospital Research and Referral in New Delhi, India, and included all oncology cases with FDG-avid thrombosis in the report. The diagnosis of TT was based on FDG-avid solid masses inside the vessels in patients with known malignancy. The SUVmax was calculated. RESULTS Twenty-one patients were included; the most common malignancies were renal cell carcinoma (n=6), hepatocellular carcinoma (n=3), and lung cancer (n=3). Indication for the scan was initial staging (n=15) and suspected recurrence (n=6). Several vessels were affected, the most common was the inferior vena cava (n=14), but most other major branches of the venous vasculature was represented, and some patients had thrombi in several vessels. FDG uptake was linear in 7 patients, linear with a dilated vessel in 6 patients, and focal in 7 patients. The mean SUVmax of the primary tumors was 10.3 (range, 2.6-31.2; median, 6.9), and the mean SUVmax of the thrombi was 7.85 (range, 1.7-23.2; median, 6.1). All but 2 patients had additional FDG-avid foci besides the thrombus. CONCLUSIONS This study supports results from other smaller studies regarding the usefulness of FDG PET/CT in TT and corroborates the hypothesis that the SUVmax and the patterns of FDG uptake can be helpful for differentiating BT from TT in oncological patients.
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