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Smith C, Nance S, Chamberlin JH, Maisuria D, O'Doherty J, Baruah D, Schoepf UJ, Szemes AV, Elojeimy S, Kabakus IM. Application of an artificial intelligence ensemble for detection of important secondary findings on lung ventilation and perfusion SPECT-CT. Clin Imaging 2023; 100:24-29. [PMID: 37167806 DOI: 10.1016/j.clinimag.2023.04.015] [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: 11/14/2022] [Revised: 04/22/2023] [Accepted: 04/27/2023] [Indexed: 05/13/2023]
Abstract
RATIONALE Single-photon-emission-computerized-tomography/computed-tomography(SPECT/CT) is commonly used for pulmonary disease. Scant work has been done to determine ability of AI for secondary findings using low-dose-CT(LDCT) attenuation correction series of SPECT/CT. METHODS 120 patients with ventilation-perfusion-SPECT/CT from 9/1/21-5/1/22 were included in this retrospective study. AI-RAD companion(VA10A,Siemens-Healthineers, Erlangen, Germany), an ensemble of deep-convolutional-neural-networks was evaluated for the detection of pulmonary nodules, coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss. Accuracy, sensitivity, specificity was measured for the outcomes. Inter-rater reliability were measured. Inter-rater reliability was measured using the intraclass correlation coefficient (ICC) by comparing the number of nodules identified by the AI to radiologist. RESULTS Overall per-nodule accuracy, sensitivity, and specificity for detection of lung nodules were 0.678(95%CI 0.615-0.732), 0.956(95%CI 0.900-0.985), and 0.456(95%CI 0.376-0.543), respectively, with an intraclass correlation coefficient (ICC) between AI and radiologist of 0.78(95%CI 0.71-0.83). Overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.939(95%CI 0.878-0.975), 0.974(95%CI 0.925-0.995), and 0.857(95%CI 0.781-0.915), respectively. Sensitivity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.898(95%CI 0.778-0.966), 1 (95%CI 0.958-1), and 1 (95%CI 0.961-1), respectively. Specificity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.969(95% CI 0.893-0.996), 0.897 (95% CI 0.726-0.978), and 0.346 (95% CI 0.172-0.557), respectively. CONCLUSION AI ensemble was accurate for coronary artery calcium and aortic ectasia/aneurysm, while sensitive for aortic ectasia/aneurysm, lung nodules and vertebral height loss on LDCT attenuation correction series of SPECT/CT.
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Affiliation(s)
- Carter Smith
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Sophia Nance
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Jordan H Chamberlin
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Dhruw Maisuria
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Jim O'Doherty
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America; Siemens Healthineers, 40 Liberty Boulevard, Malvern, PA 19355, United States of America.
| | - Dhiraj Baruah
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Uwe Joseph Schoepf
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Akos-Varga Szemes
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Saeed Elojeimy
- Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
| | - Ismail M Kabakus
- Division of CardioThoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC 29425, United States of America.
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Xue H, Hu G, Hong N, Dunnick NR, Jin Z. How to keep artificial intelligence evolving in the medical imaging world? Challenges and opportunities. Sci Bull (Beijing) 2023; 68:648-652. [PMID: 36964087 DOI: 10.1016/j.scib.2023.03.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Affiliation(s)
- Huadan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Ge Hu
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing 100044, China.
| | - N Reed Dunnick
- Department of Radiology, University of Michigan, Ann Arbor 48109, USA.
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
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Chamberlin JH, Smith C, Schoepf UJ, Nance S, Elojeimy S, O'Doherty J, Baruah D, Burt JR, Varga-Szemes A, Kabakus IM. A deep convolutional neural network ensemble for composite identification of pulmonary nodules and incidental findings on routine PET/CT. Clin Radiol 2023; 78:e368-e376. [PMID: 36863883 DOI: 10.1016/j.crad.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/19/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023]
Abstract
AIM To evaluate primary and secondary pathologies of interest using an artificial intelligence (AI) platform, AI-Rad Companion, on low-dose computed tomography (CT) series from integrated positron-emission tomography (PET)/CT to detect CT findings that might be overlooked. MATERIALS AND METHODS One hundred and eighty-nine sequential patients who had undergone PET/CT were included. Images were evaluated using an ensemble of convolutional neural networks (AI-Rad Companion, Siemens Healthineers, Erlangen, Germany). The primary outcome was detection of pulmonary nodules for which the accuracy, identity, and intra-rater reliability was calculated. For secondary outcomes (binary detection of coronary artery calcium, aortic ectasia, vertebral height loss), accuracy and diagnostic performance were calculated. RESULTS The overall per-nodule accuracy for detection of lung nodules was 0.847. The overall sensitivity and specificity for detection of lung nodules was 0.915 and 0.781. The overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia, and vertebral height loss was 0.979, 0.966, and 0.840, respectively. The sensitivity and specificity for coronary artery calcium was 0.989 and 0.969. The sensitivity and specificity for aortic ectasia was 0.806 and 1. CONCLUSION The neural network ensemble accurately assessed the number of pulmonary nodules and presence of coronary artery calcium and aortic ectasia on low-dose CT series of PET/CT. The neural network was highly specific for the diagnosis of vertebral height loss, but not sensitive. The use of the AI ensemble can help radiologists and nuclear medicine physicians to catch CT findings that might be overlooked.
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Affiliation(s)
- J H Chamberlin
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - C Smith
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - U J Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - S Nance
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - S Elojeimy
- Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - J O'Doherty
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Siemens Medical Solutions, Malvern, PA, USA
| | - D Baruah
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - J R Burt
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - A Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - I M Kabakus
- Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Division of Nuclear Medicine, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
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Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study. AJR Am J Roentgenol 2022; 219:743-751. [PMID: 35703413 DOI: 10.2214/ajr.22.27598] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND. Deep learning-based convolutional neural networks have enabled major advances in development of artificial intelligence (AI) software applications. Modern AI applications offer comprehensive multiorgan evaluation. OBJECTIVE. The purpose of this article was to evaluate the impact of an automated AI platform integrated into clinical workflow for chest CT interpretation on radiologists' interpretation times when evaluated in a real-world clinical setting. METHODS. In this prospective single-center study, a commercial AI software solution was integrated into clinical workflow for chest CT interpretation. The software provided automated analysis of cardiac, pulmonary, and musculoskeletal findings, including labeling, segmenting, and measuring normal structures as well as detecting, labeling, and measuring abnormalities. AI-annotated images and autogenerated summary results were stored in the PACS and available to interpreting radiologists. A total of 390 patients (204 women, 186 men; mean age, 62.8 ± 13.3 [SD] years) who underwent out-patient chest CT between January 19, 2021, and January 28, 2021, were included. Scans were randomized using 1:1 allocation between AI-assisted and non-AI-assisted arms and were clinically interpreted by one of three cardiothoracic radiologists (65 scans per arm per radiologist; total of 195 scans per arm) who recorded interpretation times using a stopwatch. Findings were categorized according to review of report impressions. Interpretation times were compared between arms. RESULTS. Mean interpretation times were significantly shorter in the AI-assisted than in the non-AI-assisted arm for all three readers (289 ± 89 vs 344 ± 129 seconds, p < .001; 449 ± 110 vs 649 ± 82 seconds, p < .001; 281 ± 114 vs 348 ± 93 seconds, p = .01) and for readers combined (328 ± 122 vs 421 ± 175 seconds, p < .001). For readers combined, the mean difference was 93 seconds (95% CI, 63-123 seconds), corresponding with a 22.1% reduction in the AI-assisted arm. Mean interpretation time was also shorter in the AI-assisted arm compared with the non-AI-assisted arm for contrast-enhanced scans (83 seconds), noncontrast scans (104 seconds), negative scans (84 seconds), positive scans without significant new findings (117 seconds), and positive scans with significant new findings (92 seconds). CONCLUSION. Cardiothoracic radiologists exhibited a 22.1% reduction in chest CT interpretations times when they had access to results from an automated AI support platform during real-world clinical practice. CLINICAL IMPACT. Integration of the AI support platform into clinical workflow improved radiologist efficiency.
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Dankelman LHM, Schilstra S, IJpma FFA, Doornberg JN, Colaris JW, Verhofstad MHJ, Wijffels MME, Prijs J. Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations. Eur J Trauma Emerg Surg 2022; 49:681-691. [PMID: 36284017 PMCID: PMC10175338 DOI: 10.1007/s00068-022-02128-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/02/2022] [Indexed: 11/26/2022]
Abstract
Abstract
Purpose
The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice.
Methods
Literature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detection of fractures on CT scans was described. Quality assessment was done with a modified version of the methodologic index for nonrandomized studies (MINORS), with a seven-item checklist. Performance of AI was defined as accuracy, F1-score and area under the curve (AUC).
Results
Of the 1140 identified studies, 17 were included. Accuracy ranged from 69 to 99%, the F1-score ranged from 0.35 to 0.94 and the AUC, ranging from 0.77 to 0.95. Based on ten studies, CNN showed a similar or improved diagnostic accuracy in addition to clinical evaluation only.
Conclusions
CNNs are applicable for the detection and classification fractures on CT scans. This can improve automated and clinician-aided diagnostics. Further research should focus on the additional value of CNN used for CT scans in daily clinics.
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Affiliation(s)
- Lente H. M. Dankelman
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Sanne Schilstra
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
| | - Frank F. A. IJpma
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
| | - Job N. Doornberg
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Orthopedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, Australia
| | - Joost W. Colaris
- Department of Orthopedics, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Michael H. J. Verhofstad
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Mathieu M. E. Wijffels
- Trauma Research Unit, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Jasper Prijs
- Department of Orthopedic Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Surgery, Groningen University Medical Centre, Groningen, The Netherlands
- Department of Orthopedic & Trauma Surgery, Flinders Medical Centre, Flinders University, Adelaide, Australia
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Kocher MR, Chamberlin J, Waltz J, Snoddy M, Stringer N, Stephenson J, Kahn J, Mercer M, Baruah D, Aquino G, Kabakus I, Hoelzer P, Sahbaee P, Schoepf UJ, Burt JR. Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine. Heliyon 2022; 8:e08962. [PMID: 35243082 PMCID: PMC8873537 DOI: 10.1016/j.heliyon.2022.e08962] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/20/2021] [Accepted: 02/11/2022] [Indexed: 12/05/2022] Open
Abstract
Background Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT. Objective To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival. Methods Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival. Results 752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07–1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001). Conclusion Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality. Clinical impact As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes. Automated detection software can quantify lung metastases on initial staging chest CT in breast cancer patients. AI-detected lung metastases number and max diameter on CT at initial cancer staging were strong predictors of mortality. AI detection and segmentation tool contributes to accurate individualized prognostication in breast cancer patients.
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Affiliation(s)
- Madison R Kocher
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jordan Chamberlin
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jeffrey Waltz
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Madalyn Snoddy
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Natalie Stringer
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Joseph Stephenson
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jacob Kahn
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Megan Mercer
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Dhiraj Baruah
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Gilberto Aquino
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Ismail Kabakus
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | | | | | - U Joseph Schoepf
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jeremy R Burt
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
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Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022; 29 Suppl 2:S226-S235. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVES High-resolution computed tomography (HRCT) is paramount in the assessment of interstitial lung disease (ILD). Yet, HRCT interpretation of ILDs may be hampered by inter- and intra-observer variability. Recently, artificial intelligence (AI) has revolutionized medical image analysis. This technology has the potential to advance patient care in ILD. We aimed to systematically evaluate the application of AI for the analysis of ILD in HRCT. MATERIALS AND METHODS We searched MEDLINE/PubMed databases for original publications of deep learning for ILD analysis on chest CT. The search included studies published up to March 1, 2021. The risk of bias evaluation included tailored Quality Assessment of Diagnostic Accuracy Studies and the modified Joanna Briggs Institute Critical Appraisal checklist. RESULTS Data was extracted from 19 retrospective studies. Deep learning techniques included detection, segmentation, and classification of ILD on HRCT. Most studies focused on the classification of ILD into different morphological patterns. Accuracies of 78%-91% were achieved. Two studies demonstrated near-expert performance for the diagnosis of idiopathic pulmonary fibrosis (IPF). The Quality Assessment of Diagnostic Accuracy Studies tool identified a high risk of bias in 15/19 (78.9%) of the studies. CONCLUSION AI has the potential to contribute to the radiologic diagnosis and classification of ILD. However, the accuracy performance is still not satisfactory, and research is limited by a small number of retrospective studies. Hence, the existing published data may not be sufficiently reliable. Only well-designed prospective controlled studies can accurately assess the value of existing AI tools for ILD evaluation.
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