1
|
Cadavid L, Karout L, Kalra MK, Morgado F, Londoño MA, Pérez L, Galeano M, Montaño M, Wesley L, Almanza J, Pacheco W, Gómez L, Moscatelli A, Muglia V, Kiipper F, Lucena R, Bernardo M, Ugas C. Setting up regional diagnostic reference levels for pediatric computed tomography in Latin America: preliminary results, challenges and the work ahead. Pediatr Radiol 2024; 54:457-467. [PMID: 37227466 DOI: 10.1007/s00247-023-05676-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/26/2023]
Abstract
We established a framework for collecting radiation doses for head, chest and abdomen-pelvis computed tomography (CT) in children scanned at multiple imaging sites across Latin America with an aim towards establishing diagnostic reference levels (DRLs) and achievable doses (ADs) in pediatric CT in Latin America. Our study included 12 Latin American sites (in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras and Panama) contributing data on the four most common pediatric CT examinations (non-contrast head, non-contrast chest, post-contrast chest and post-contrast abdomen-pelvis). Sites contributed data on patients' age, sex and weight, scan factors (tube current and potential), volume CT dose index (CTDIvol) and dose length product (DLP). Data were verified, leading to the exclusion of two sites with missing or incorrect data entries. We estimated overall and site-specific 50th (AD) and 75th (diagnostic reference level [DRL]) percentile CTDIvol and DLP for each CT protocol. Non-normal data were compared using the Kruskal-Wallis test. Sites contributed data from 3,934 children (1,834 females) for different CT exams (head CT 1,568/3,934, 40%; non-contrast chest CT 945/3,934, 24%; post-contrast chest CT 581/3,934, 15%; abdomen-pelvis CT 840/3,934, 21%). There were significant statistical differences in 50th and 75th percentile CTDIvol and DLP values across the participating sites (P<0.001). The 50th and 75th percentile doses for most CT protocols were substantially higher than the corresponding doses reported from the United States of America. Our study demonstrates substantial disparities and variations in pediatric CT examinations performed in multiple sites in Latin America. We will use the collected data to improve scan protocols and perform a follow-up CT study to establish DRLs and ADs based on clinical indications.
Collapse
Affiliation(s)
- Lina Cadavid
- Department of Radiology, Hospital Pablo Tobón Uribe, Calle 78B#69-240, Medellín, Antioquia, 050034, Colombia.
| | - Lina Karout
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Flavio Morgado
- Pontificie Universidade Católica de São Paulo, São Paulo, Brazil
| | - María Antonieta Londoño
- Universidad Nacional de Colombia/Fundación Hospital Pediátrico La Misericordia, Bogotá, Colombia
| | - Lizbeth Pérez
- Department of Radiology, Clinica alemana de Santiago - Universidad del Desarrollo, Santiago de Chile, Chile
| | - Monica Galeano
- Department of Radiology, Hospital del Niño Prof Dr. Ramón Exeni, La Matanza provincia de Buenos Aires, Argentina
| | - Mario Montaño
- Department of Radiology, Hospital Santa Cruz C.P.S, Santa Cruz de la Sierra, Bolivia
| | - Lavinia Wesley
- Department of Radiology, Hospital del Niño Dr. José Renán Esquivel, Ciudad de Panamá, Panamá
| | - Judith Almanza
- Department of Radiology, Salud Digna., Ciudad de México, México
| | - Walter Pacheco
- Department of Radiology, Maria Hospital, Pediatric Specialties, Tegucigalpa, Honduras
| | - Lucia Gómez
- Department of Radiology, Hospital Metropolitano, Quito, Ecuador
| | | | - Valdair Muglia
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo, Ribeirão Preto, Brazil
| | | | | | - Monica Bernardo
- Pontifícia Universidade Católica de São Paulo, Hospital Miguel Soeiro, Sorocaba, Brazil
| | - Carlos Ugas
- Department of Diagnostic Imaging, Instituto Nacional de Salud del Niño San Borja, Lima, Perú
| |
Collapse
|
2
|
Shah KA, Ng CKC. Workplace violence in medical radiation science: A systematic review. Radiography (Lond) 2024; 30:440-447. [PMID: 38199160 DOI: 10.1016/j.radi.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/07/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024]
Abstract
INTRODUCTION According to World Health Organization (WHO), workplace violence (WPV) is a significant issue in healthcare. However, no systematic review on WPV in medical radiation science (MRS) has been published yet. The purpose of this paper is to systematically review prevalence of WPV in MRS and its risk factors. METHODS Electronic scholarly publication databases, namely EBSCOhost/Cumulative Index of Nursing and Allied Health Literature Ultimate, PubMed/Medline, ScienceDirect, Scopus, and Wiley Online Library were used for literature search to identify articles about WPV in MRS published over last 10 years as per preferred reporting items for systematic reviews and meta-analyses guidelines. To facilitate comparisons of the WPV prevalence and relative importance of individual risk factors across the included studies, their reported absolute figures of findings were used to synthesize respective percentages (if not stated). RESULTS Twelve papers met the selection criteria and were included. This review shows that the WPV prevalence were 69.2-100 % (whole career) and 46.1-83.0 % (last 12 months) in diagnostic radiography, 63.0-84.0 % (whole career) in radiation therapy, 57.6 % in medical sonography (last 12 months), and 46.8 % (last 6 months) in nuclear medicine. The identified WPV risk factors included intoxicated patients, staff stress, feeling of inadequacy resulting in self-protection, more vulnerable practitioners (female, <40 years old and <5-year experience), working in radiation therapy treatment room, emergency department, examination room, general radiography, public hospital, and non-examination and waiting areas, long patient waiting time, night shift, overcrowding environment, unable to meet patients'/family members' expectations, miscommunication, patient handling, inadequate staff and security measures, interaction with colleagues, and lone working. CONCLUSION The WPV risk in diagnostic radiography and radiation therapy appears extremely high as a result of the aforementioned risk factors. Nevertheless, these study findings should be used with caution due to potential non-response bias. IMPLICATIONS FOR PRACTICE A WPV policy should be developed in every clinical workplace. Even if such policy is available, its enforcement including policy awareness boosting, and encouraging incident reporting and support seeking will be essential for reducing WPV. More survey studies based on WHO WPV questionnaire should be conducted for strengthening evidence base.
Collapse
Affiliation(s)
- K A Shah
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, Western Australia 6845, Australia
| | - C K C Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, Western Australia 6845, Australia; Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, Western Australia 6845, Australia.
| |
Collapse
|
3
|
Lepri G, Oddi F, Gulino RA, Giansanti D. Reimagining Radiology: A Comprehensive Overview of Reviews at the Intersection of Mobile and Domiciliary Radiology over the Last Five Years. Bioengineering (Basel) 2024; 11:216. [PMID: 38534491 DOI: 10.3390/bioengineering11030216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
(Background) Domiciliary radiology, which originated in pioneering studies in 1958, has transformed healthcare, particularly during the COVID-19 pandemic, through advancements such as miniaturization and digitization. This evolution, driven by the synergy of advanced technologies and robust data networks, reshapes the intersection of domiciliary radiology and mobile technology in healthcare delivery. (Objective) The objective of this study is to overview the reviews in this field with reference to the last five years to face the state of development and integration of this practice in the health domain. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome detected 21 studies. (Key Content and Findings) The exploration of mobile and domiciliary radiology unveils a compelling and optimistic perspective. Notable strides in this dynamic field include the integration of Artificial Intelligence (AI), revolutionary applications in telemedicine, and the educational potential of mobile devices. Post-COVID-19, telemedicine advances and the influential role of AI in pediatric radiology signify significant progress. Mobile mammography units emerge as a solution for underserved women, highlighting the crucial importance of early breast cancer detection. The investigation into domiciliary radiology, especially with mobile X-ray equipment, points toward a promising frontier, prompting in-depth research for comprehensive insights into its potential benefits for diverse populations. The study also identifies limitations and suggests future exploration in various domains of mobile and domiciliary radiology. A key recommendation stresses the strategic prioritization of multi-domain technology assessment initiatives, with scientific societies' endorsement, emphasizing regulatory considerations for responsible and ethical technology integration in healthcare practices. The broader landscape of technology assessment should aim to be innovative, ethical, and aligned with societal needs and regulatory standards. (Conclusions) The dynamic state of the field is evident, with active exploration of new frontiers. This overview also provides a roadmap, urging scholars, industry players, and regulators to collectively contribute to the further integration of this technology in the health domain.
Collapse
Affiliation(s)
- Graziano Lepri
- Azienda Unità Sanitaria Locale Umbria 1, Via Guerriero Guerra 21, 06127 Perugia, Italy
| | - Francesco Oddi
- Facoltà di Ingegneria, Università di Tor Vergata, Via del Politecnico, 1, 00133 Roma, Italy
| | - Rosario Alfio Gulino
- Facoltà di Ingegneria, Università di Tor Vergata, Via del Politecnico, 1, 00133 Roma, Italy
| | - Daniele Giansanti
- Centro Nazionale TISP, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy
| |
Collapse
|
4
|
Wong JYK, Leung VWS, Hung RHM, Ng CKC. Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality. Diagnostics (Basel) 2024; 14:465. [PMID: 38472938 DOI: 10.3390/diagnostics14050465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/01/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: -0.89% (PTV76D98%) and -2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and -1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation.
Collapse
Affiliation(s)
- John Y K Wong
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
- Department of Clinical Oncology, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR, China
| | - Vincent W S Leung
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Rico H M Hung
- Department of Clinical Oncology, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR, China
| | - Curtise K C Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
| |
Collapse
|
5
|
Yan L, Wang Z, Li D, Wang Y, Yang G, Zhao Y, Kong Y, Wang R, Wu R, Wang Z. Low 18F-fluorodeoxyglucose dose positron emission tomography assisted by a deep-learning image-denoising technique in patients with lymphoma. Quant Imaging Med Surg 2024; 14:111-122. [PMID: 38223079 PMCID: PMC10784027 DOI: 10.21037/qims-23-817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 10/20/2023] [Indexed: 01/16/2024]
Abstract
Background Patients with lymphoma receive multiple positron emission tomography/computed tomography (PET/CT) exams for monitoring of the therapeutic response. With PET imaging, a reduced level of injected fluorine-18 fluorodeoxyglucose ([18F]FDG) activity can be administered while maintaining the image quality. In this study, we investigated the efficacy of applying a deep learning (DL) denoising-technique on image quality and the quantification of metabolic parameters and Deauville score (DS) of a low [18F]FDG dose PET in patients with lymphoma. Methods This study retrospectively enrolled 62 patients who underwent [18F]FDG PET scans. The low-dose (LD) data were simulated by taking a 50% duration of routine-dose (RD) PET list-mode data in the reconstruction, and a U-Net-based denoising neural network was applied to improve the images of LD PET. The visual image quality score (1 = undiagnostic, 5 = excellent) and DS were assessed in all patients by nuclear radiologists. The maximum, mean, and standard deviation (SD) of the standardized uptake value (SUV) in the liver and mediastinum were measured. In addition, lesions in some patients were segmented using a fixed threshold of 2.5, and their SUV, metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG) were measured. The correlation coefficient and limits of agreement between the RD and LD group were analyzed. Results The visual image quality of the LD group was improved compared with the RD group. The DS was similar between the RD and LD group, and the negative (DS 1-3) and positive (DS 4-5) results remained unchanged. The correlation coefficients of SUV in the liver, mediastinum, and lesions were all >0.85. The mean differences of SUVmax and SUVmean between the RD and LD groups, respectively, were 0.22 [95% confidence interval (CI): -0.19 to 0.64] and 0.02 (95% CI: -0.17 to 0.20) in the liver, 0.13 (95% CI: -0.17 to 0.42) and 0.02 (95% CI: -0.12 to 0.16) in the mediastinum, and -0.75 (95% CI: -3.42 to 1.91), and -0.13 (95% CI: -0.57 to 0.31) in lesions. The mean differences in MTV and TLG were 0.85 (95% CI: -2.27 to 3.98) and 4.06 (95% CI: -20.53 to 28.64) between the RD and LD groups. Conclusions The DL denoising technique enables accurate tumor assessment and quantification with LD [18F]FDG PET imaging in patients with lymphoma.
Collapse
Affiliation(s)
- Lei Yan
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhao Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Dacheng Li
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yangyang Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Guangjie Yang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yujun Zhao
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yan Kong
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Rui Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Runze Wu
- Central Research Institute, Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Zhenguang Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| |
Collapse
|
6
|
Leung VWS, Ng CKC, Lam SK, Wong PT, Ng KY, Tam CH, Lee TC, Chow KC, Chow YK, Tam VCW, Lee SWY, Lim FMY, Wu JQ, Cai J. Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy. J Pers Med 2023; 13:1643. [PMID: 38138870 PMCID: PMC10744672 DOI: 10.3390/jpm13121643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.
Collapse
Affiliation(s)
- Vincent W. S. Leung
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia;
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
| | - Sai-Kit Lam
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China;
| | - Po-Tsz Wong
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Ka-Yan Ng
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Cheuk-Hong Tam
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Tsz-Ching Lee
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Kin-Chun Chow
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Yan-Kate Chow
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Victor C. W. Tam
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Shara W. Y. Lee
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| | - Fiona M. Y. Lim
- Department of Oncology, Princess Margaret Hospital, Hong Kong SAR, China;
| | - Jackie Q. Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27708, USA;
| | - Jing Cai
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; (P.-T.W.); (V.C.W.T.); (S.W.Y.L.); (J.C.)
| |
Collapse
|
7
|
Busch F, Keller S, Rueger C, Kader A, Ziegeler K, Bressem KK, Adams LC. Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals. Acta Radiol Open 2023; 12:20584601231213740. [PMID: 38034076 PMCID: PMC10685787 DOI: 10.1177/20584601231213740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
Background The growing role of artificial intelligence (AI) in healthcare, particularly radiology, requires its unbiased and fair development and implementation, starting with the constitution of the scientific community. Purpose To examine the gender and country distribution among academic editors in leading computer science and AI journals. Material and Methods This cross-sectional study analyzed the gender and country distribution among editors-in-chief, senior, and associate editors in all 75 Q1 computer science and AI journals in the Clarivate Journal Citations Report and SCImago Journal Ranking 2022. Gender was determined using an open-source algorithm (Gender Guesser™), selecting the gender with the highest calibrated probability. Result Among 4,948 editorial board members, women were underrepresented in all positions (editors-in-chief/senior editors/associate editors: 14%/18%/17%). The proportion of women correlated positively with the SCImago Journal Rank indicator (ρ = 0.329; p = .004). The U.S., the U.K., and China comprised 50% of editors, while Australia, Finland, Estonia, Denmark, the Netherlands, the U.K., Switzerland, and Slovenia had the highest women editor representation per million women population. Conclusion Our results highlight gender and geographic disparities on leading computer science and AI journal editorial boards, with women being underrepresented in all positions and a disproportional relationship between the Global North and South.
Collapse
Affiliation(s)
- Felix Busch
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Division of Operative Intensive Care Medicine, Department of Anesthesiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Sarah Keller
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Christopher Rueger
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Avan Kader
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Department of Radiology, Klinikum rechts der Isar, Technische Universität München (TUM), Munich, Germany
| | - Katharina Ziegeler
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Keno K Bressem
- Department of Radiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa C Adams
- Department of Radiology, Klinikum rechts der Isar, Technische Universität München (TUM), Munich, Germany
| |
Collapse
|
8
|
Bahonar BM, Changizi V, Ebrahiminia A, Baradaran S. Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS). Phys Eng Sci Med 2023; 46:1071-1080. [PMID: 37245194 PMCID: PMC10225119 DOI: 10.1007/s13246-023-01276-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 05/05/2023] [Indexed: 05/29/2023]
Abstract
In chest computed tomography (CT), the breasts located within the scan range receive a substantial radiation dose. Due to the risk of breast-related carcinogenesis, analyzing the breast dose for justification of CT examinations seems necessary. The main goal of this study is to overcome the limitations of conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by introducing the adaptive neuro-fuzzy inference system (ANFIS) approach. In this study, the breast dose of 50 adult female patients who underwent chest CT examinations was measured directly by TLDs. Then, the ANFIS model was developed with four inputs including dose length product (DLP), volumetric CT dose index (CTDIvol), total mAs, and size-specific dose estimate (SSDE), and one output (TLD dose). Additionally, multiple linear regression (MLR) as a traditional prediction model was used for linear modeling and its results were compared with the ANFIS. The TLD reader results showed that the breast dose value was 12.37 ± 2.46 mGy. Performance indices of the ANFIS model, including root mean square error (RMSE) and correlation coefficient (R), were calculated at 0.172 and 0.93 for the testing dataset, respectively. Also, the ANFIS model had superior performance in predicting the breast dose than the MLR model (R = 0.805). This study demonstrates that the proposed ANFIS model is efficient for patient dose prediction in CT scans. Therefore, intelligence models such as ANFIS are suggested to estimate and optimize patient dose in CT examinations.
Collapse
Affiliation(s)
- Bahareh Moradmand Bahonar
- Department of Radiology and Radiotherapy Technology, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahid Changizi
- Department of Radiology and Radiotherapy Technology, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ali Ebrahiminia
- Department of Medical Physics, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | | |
Collapse
|
9
|
Ng CKC. Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1372. [PMID: 37628371 PMCID: PMC10453402 DOI: 10.3390/children10081372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.
Collapse
Affiliation(s)
- Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia; or ; Tel.: +61-8-9266-7314; Fax: +61-8-9266-2377
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
| |
Collapse
|
10
|
Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
Collapse
Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
| |
Collapse
|
11
|
Ng CKC. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review. CHILDREN (BASEL, SWITZERLAND) 2023; 10:children10030525. [PMID: 36980083 PMCID: PMC10047006 DOI: 10.3390/children10030525] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/13/2023] [Accepted: 03/07/2023] [Indexed: 03/30/2023]
Abstract
Artificial intelligence (AI)-based computer-aided detection and diagnosis (CAD) is an important research area in radiology. However, only two narrative reviews about general uses of AI in pediatric radiology and AI-based CAD in pediatric chest imaging have been published yet. The purpose of this systematic review is to investigate the AI-based CAD applications in pediatric radiology, their diagnostic performances and methods for their performance evaluation. A literature search with the use of electronic databases was conducted on 11 January 2023. Twenty-three articles that met the selection criteria were included. This review shows that the AI-based CAD could be applied in pediatric brain, respiratory, musculoskeletal, urologic and cardiac imaging, and especially for pneumonia detection. Most of the studies (93.3%, 14/15; 77.8%, 14/18; 73.3%, 11/15; 80.0%, 8/10; 66.6%, 2/3; 84.2%, 16/19; 80.0%, 8/10) reported model performances of at least 0.83 (area under receiver operating characteristic curve), 0.84 (sensitivity), 0.80 (specificity), 0.89 (positive predictive value), 0.63 (negative predictive value), 0.87 (accuracy), and 0.82 (F1 score), respectively. However, a range of methodological weaknesses (especially a lack of model external validation) are found in the included studies. In the future, more AI-based CAD studies in pediatric radiology with robust methodology should be conducted for convincing clinical centers to adopt CAD and realizing its benefits in a wider context.
Collapse
Affiliation(s)
- Curtise K C Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
| |
Collapse
|
12
|
Cellina M, Cè M, Irmici G, Ascenti V, Caloro E, Bianchi L, Pellegrino G, D’Amico N, Papa S, Carrafiello G. Artificial Intelligence in Emergency Radiology: Where Are We Going? Diagnostics (Basel) 2022; 12:diagnostics12123223. [PMID: 36553230 PMCID: PMC9777804 DOI: 10.3390/diagnostics12123223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/11/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.
Collapse
Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
- Correspondence:
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Lorenzo Bianchi
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Giuseppe Pellegrino
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natascha D’Amico
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Via Sforza 35, 20122 Milan, Italy
| |
Collapse
|
13
|
Finetuned Super-Resolution Generative Adversarial Network (Artificial Intelligence) Model for Calcium Deblooming in Coronary Computed Tomography Angiography. J Pers Med 2022; 12:jpm12091354. [PMID: 36143139 PMCID: PMC9503533 DOI: 10.3390/jpm12091354] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 12/02/2022] Open
Abstract
The purpose of this study was to finetune a deep learning model, real-enhanced super-resolution generative adversarial network (Real-ESRGAN), and investigate its diagnostic value in calcified coronary plaques with the aim of suppressing blooming artifacts for the further improvement of coronary lumen assessment. We finetuned the Real-ESRGAN model and applied it to 50 patients with 184 calcified plaques detected at three main coronary arteries (left anterior descending [LAD], left circumflex [LCx] and right coronary artery [RCA]). Measurements of coronary stenosis were collected from original coronary computed tomography angiography (CCTA) and Real-ESRGAN-processed images, including Real-ESRGAN-high-resolution, Real-ESRGAN-average and Real-ESRGAN-median (Real-ESRGAN-HR, Real-ESRGAN-A and Real-ESRGAN-M) with invasive coronary angiography as the reference. Our results showed specificity and positive predictive value (PPV) of the Real-ESRGAN-processed images were improved at all of the three coronary arteries, leading to significant reduction in the false positive rates when compared to those of the original CCTA images. The specificity and PPV of the Real-ESRGAN-M images were the highest at the RCA level, with values being 80% (95% CI: 64.4%, 90.9%) and 61.9% (95% CI: 45.6%, 75.9%), although the sensitivity was reduced to 81.3% (95% CI: 54.5%, 95.9%) due to false negative results. The corresponding specificity and PPV of the Real-ESRGAN-M images were 51.9 (95% CI: 40.3%, 63.5%) and 31.5% (95% CI: 25.8%, 37.8%) at LAD, 62.5% (95% CI: 40.6%, 81.2%) and 43.8% (95% CI: 30.3%, 58.1%) at LCx, respectively. The area under the receiver operating characteristic curve was also the highest at the RCA with value of 0.76 (95% CI: 0.64, 0.89), 0.84 (95% CI: 0.73, 0.94), 0.85 (95% CI: 0.75, 0.95) and 0.73 (95% CI: 0.58, 0.89), corresponding to original CCTA, Real-ESRGAN-HR, Real-ESRGAN-A and Real-ESRGAN-M images, respectively. This study proves that the finetuned Real-ESRGAN model significantly improves the diagnostic performance of CCTA in assessing calcified plaques.
Collapse
|