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Vellala A, Mogler C, Haag F, Tollens F, Rudolf H, Pietsch F, Wängler C, Wängler B, Schoenberg SO, Froelich MF, Hertel A. Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis. Front Med (Lausanne) 2024; 11:1407235. [PMID: 38903806 PMCID: PMC11188677 DOI: 10.3389/fmed.2024.1407235] [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: 03/26/2024] [Accepted: 05/27/2024] [Indexed: 06/22/2024] Open
Abstract
Purpose This study compares phantom-based variability of extracted radiomics features from scans on a photon counting CT (PCCT) and an experimental animal PET/CT-scanner (Albira II) to investigate the potential of radiomics for translation from animal models to human scans. While oncological basic research in animal PET/CT has allowed an intrinsic comparison between PET and CT, but no 1:1 translation to a human CT scanner due to resolution and noise limitations, Radiomics as a statistical and thus scale-independent method can potentially close the critical gap. Methods Two phantoms were scanned on a PCCT and animal PET/CT-scanner with different scan parameters and then the radiomics parameters were extracted. A Principal Component Analysis (PCA) was conducted. To overcome the limitation of a small dataset, a data augmentation technique was applied. A Ridge Classifier was trained and a Feature Importance- and Cluster analysis was performed. Results PCA and Cluster Analysis shows a clear differentiation between phantom types while emphasizing the comparability of both scanners. The Ridge Classifier exhibited a strong training performance with 93% accuracy, but faced challenges in generalization with a test accuracy of 62%. Conclusion These results show that radiomics has great potential as a translational tool between animal models and human routine diagnostics, especially using the novel photon counting technique. This is another crucial step towards integration of radiomics analysis into clinical practice.
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Affiliation(s)
- Abhinay Vellala
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Carolin Mogler
- Department of Pathology, Technical University of Munich, Munich, Germany
| | - Florian Haag
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Fabian Tollens
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Henning Rudolf
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Friedrich Pietsch
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Carmen Wängler
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Björn Wängler
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stefan O. Schoenberg
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Matthias F. Froelich
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
| | - Alexander Hertel
- Department of Radiology and Nuclear medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
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Murcia VM, Aggarwal V, Pesaladinne N, Thammineni R, Do N, Alterovitz G, Fricks RB. Automating Clinical Trial Matches Via Natural Language Processing of Synthetic Electronic Health Records and Clinical Trial Eligibility Criteria. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:125-134. [PMID: 38827083 PMCID: PMC11141802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Clinical trials are critical to many medical advances; however, recruiting patients remains a persistent obstacle. Automated clinical trial matching could expedite recruitment across all trial phases. We detail our initial efforts towards automating the matching process by linking realistic synthetic electronic health records to clinical trial eligibility criteria using natural language processing methods. We also demonstrate how the Sørensen-Dice Index can be adapted to quantify match quality between a patient and a clinical trial.
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Affiliation(s)
- Victor M Murcia
- VA Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA
- VA National Artificial Intelligence Institute, Washington, D.C
| | - Vinod Aggarwal
- VHA Office of Healthcare Innovation and Learning, VA Central Office, Washington DC
- MDClone, Be'er Sheva, Israel
| | | | - Ram Thammineni
- CTS Group, Girls Computing League, Nonprofit Organization, Herndon, VA
| | - Nhan Do
- VA Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA
| | - Gil Alterovitz
- VA National Artificial Intelligence Institute, Washington, D.C
| | - Rafael B Fricks
- VA Massachusetts Veterans Epidemiology Research and Information Center, Boston, MA
- VA National Artificial Intelligence Institute, Washington, D.C
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3
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Gozzi F, Bertolini M, Gentile P, Verzellesi L, Trojani V, De Simone L, Bolletta E, Mastrofilippo V, Farnetti E, Nicoli D, Croci S, Belloni L, Zerbini A, Adani C, De Maria M, Kosmarikou A, Vecchi M, Invernizzi A, Ilariucci F, Zanelli M, Iori M, Cimino L. Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma. Diagnostics (Basel) 2023; 13:2451. [PMID: 37510195 PMCID: PMC10378347 DOI: 10.3390/diagnostics13142451] [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: 05/24/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.
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Affiliation(s)
- Fabrizio Gozzi
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Marco Bertolini
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Pietro Gentile
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
- Clinical and Experimental Medicine Ph.D. Program, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Laura Verzellesi
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Valeria Trojani
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Luca De Simone
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Elena Bolletta
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | | | - Enrico Farnetti
- Molecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Davide Nicoli
- Molecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Stefania Croci
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Lucia Belloni
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Alessandro Zerbini
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Chantal Adani
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Michele De Maria
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Areti Kosmarikou
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Marco Vecchi
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Alessandro Invernizzi
- Eye Clinic, Luigi Sacco Hospital, Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, Italy
- Faculty of Health and Medicine, Save Sight Institute, University of Sydney, Sydney, NSW 2000, Australia
| | | | - Magda Zanelli
- Surgical Oncology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Luca Cimino
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, with Interest in Transplants, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, 41124 Modena, Italy
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Zhong J, Pan Z, Chen Y, Wang L, Xia Y, Wang L, Li J, Lu W, Shi X, Feng J, Yan F, Zhang H, Yao W. Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability. Insights Imaging 2023; 14:79. [PMID: 37166511 PMCID: PMC10175529 DOI: 10.1186/s13244-023-01426-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/05/2023] [Indexed: 05/12/2023] Open
Abstract
OBJECTIVES To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms. METHODS A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated. RESULTS 92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI70keV (r2 0.3870-0.6178, all p < 0.001). CONCLUSIONS The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, London, SW7 2AZ, UK
| | - Jianxing Feng
- Haohua Technology Co., Ltd., Shanghai, 201100, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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Shang F, Tan Z, Gong T, Tang X, Sun H, Liu S. Evaluation of parametrial infiltration in patients with IB-IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18 F-fluorodeoxy glucose positron emission tomography/MR images. NMR IN BIOMEDICINE 2023:e4945. [PMID: 37012600 DOI: 10.1002/nbm.4945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/03/2023] [Accepted: 03/26/2023] [Indexed: 06/19/2023]
Abstract
Parametrial infiltration (PMI) is an essential factor in staging and planning treatment of cervical cancer. The purpose of this study was to develop a radiomics model for accessing PMI in patients with IB-IIB cervical cancer using features from 18 F-fluorodeoxy glucose (18 F-FDG) positron emission tomography (PET)/MR images. In this retrospective study, 66 patients with International Federation of Gynecology and Obstetrics stage IB-IIB cervical cancer (22 with PMI and 44 without PMI) who underwent 18 F-FDG PET/MRI were divided into a training dataset (n = 46) and a testing dataset (n = 20). Features were extracted from both the tumoral and peritumoral regions in 18 F-FDG PET/MR images. Single-modality and multimodality radiomics models were developed with random forest to predict PMI. The performance of the models was evaluated with F1 score, accuracy, and area under the curve (AUC). The Kappa test was used to observe the differences between PMI evaluated by radiomics-based models and pathological results. The intraclass correlation coefficient for features extracted from each region of interest (ROI) was measured. Three-fold crossvalidation was conducted to confirm the diagnostic ability of the features. The radiomics models developed by features from the tumoral region in T2 -weighted images (F1 score = 0.400, accuracy = 0.700, AUC = 0.708, Kappa = 0.211, p = 0.329) and the peritumoral region in PET images (F1 score = 0.533, accuracy = 0.650, AUC = 0.714, Kappa = 0.271, p = 0.202) achieved the best performances in the testing dataset among the four single-ROI radiomics models. The combined model using features from the tumoral region in T2 -weighted images and the peritumoral region in PET images achieved the best performance (F1 score = 0.727, accuracy = 0.850, AUC = 0.774, Kappa = 0.625, p < 0.05). The results suggest that 18 F-FDG PET/MRI can provide complementary information regarding cervical cancer. The radiomics-based method integrating features from the tumoral and peritumoral regions in 18 F-FDG PET/MR images gave a superior performance for evaluating PMI.
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Affiliation(s)
- Fei Shang
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Zheng Tan
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Tan Gong
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Xiaoying Tang
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Shuai Liu
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
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Jacobs MA. Data Partitioning and Statistical Considerations for Association of Radiomic Features to Biological Underpinnings: What Is Needed. Radiology 2023; 307:e223007. [PMID: 36537899 PMCID: PMC10068882 DOI: 10.1148/radiol.223007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 11/26/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Michael A. Jacobs
- From the Department of Diagnostic and Interventional Imaging,
McGovern Medical School at The University of Texas Health Science Center at
Houston (UTHealth Houston), 6431 Fannin St, Room R172, Houston, TX 77030; and
The Russell H. Morgan Department of Radiology and Radiological Science and
Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University,
Baltimore, Md
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7
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Søreide K, Ismail W, Roalsø M, Ghotbi J, Zaharia C. Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery. Cancer Control 2023; 30:10732748231154711. [PMID: 36916724 PMCID: PMC9893084 DOI: 10.1177/10732748231154711] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The overall poor prognosis in pancreatic cancer is related to late clinical detection. Early diagnosis remains a considerable challenge in pancreatic cancer. Unfortunately, the onset of clinical symptoms in patients usually indicate advanced disease or presence of metastasis. ANALYSIS AND RESULTS Currently, there are no designated diagnostic or screening tests for pancreatic cancer in clinical use. Thus, identifying risk groups, preclinical risk factors or surveillance strategies to facilitate early detection is a target for ongoing research. Hereditary genetic syndromes are a obvious, but small group at risk, and warrants close surveillance as suggested by society guidelines. Screening for pancreatic cancer in asymptomatic individuals is currently associated with the risk of false positive tests and, thus, risk of harms that outweigh benefits. The promise of cancer biomarkers and use of 'omics' technology (genomic, transcriptomics, metabolomics etc.) has yet to see a clinical breakthrough. Several proposed biomarker studies for early cancer detection lack external validation or, when externally validated, have shown considerably lower accuracy than in the original data. Biopsies or tissues are often taken at the time of diagnosis in research studies, hence invalidating the value of a time-dependent lag of the biomarker to detect a pre-clinical, asymptomatic yet operable cancer. New technologies will be essential for early diagnosis, with emerging data from image-based radiomics approaches, artificial intelligence and machine learning suggesting avenues for improved detection. CONCLUSIONS Early detection may come from analytics of various body fluids (eg 'liquid biopsies' from blood or urine). In this review we present some the technological platforms that are explored for their ability to detect pancreatic cancer, some of which may eventually change the prospects and outcomes of patients with pancreatic cancer.
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Affiliation(s)
- Kjetil Søreide
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Gastrointestinal Translational Research Group, Laboratory for Molecular Medicine, 60496Stavanger University Hospital, Stavanger, Norway
| | - Warsan Ismail
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway
| | - Marcus Roalsø
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway.,Gastrointestinal Translational Research Group, Laboratory for Molecular Medicine, 60496Stavanger University Hospital, Stavanger, Norway.,Department of Quality and Health Technology, 60496University of Stavanger, Stavanger, Norway
| | - Jacob Ghotbi
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway
| | - Claudia Zaharia
- Gastrointestinal Translational Research Group, Laboratory for Molecular Medicine, 60496Stavanger University Hospital, Stavanger, Norway.,Department of Pathology, 60496Stavanger University Hospital, Stavanger, Norway
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8
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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.
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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
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