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Lew CO, Calabrese E, Chen JV, Tang F, Chaudhari G, Lee A, Faro J, Juul S, Mathur A, McKinstry RC, Wisnowski JL, Rauschecker A, Wu YW, Li Y. Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE). Radiol Artif Intell 2024; 6:e240076. [PMID: 38984984 PMCID: PMC11427921 DOI: 10.1148/ryai.240076] [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: 02/05/2024] [Revised: 05/21/2024] [Accepted: 06/18/2024] [Indexed: 07/11/2024]
Abstract
Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25, 2017, and October 9, 2019, were retrospectively analyzed. The harmonized MRI protocol included T1-weighted, T2-weighted, and diffusion tensor imaging. Deep learning classifiers were trained to predict the primary outcome of the HEAL trial (death or any neurodevelopmental impairment at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 10% of cases from two institutions (out-of-distribution test set, n = 41). Model performance in predicting additional secondary outcomes, including death alone, was also assessed. Results For the 414 neonates (mean gestational age, 39 weeks ± 1.4 [SD]; 232 male, 182 female), in the study cohort, 198 (48%) died or had any neurodevelopmental impairment at 2 years. The deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.60, 0.86) and 63% accuracy in the in-distribution test set and an AUC of 0.77 (95% CI: 0.63, 0.90) and 78% accuracy in the out-of-distribution test set. Performance was similar or better for predicting secondary outcomes. Conclusion Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes. Keywords: Convolutional Neural Network (CNN), Prognosis, Pediatrics, Brain, Brain Stem Clinical trial registration no. NCT02811263 Supplemental material is available for this article. © RSNA, 2024 See also commentary by Rafful and Reis Teixeira in this issue.
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Affiliation(s)
| | | | - Joshua V. Chen
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Felicia Tang
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Gunvant Chaudhari
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Amanda Lee
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - John Faro
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Sandra Juul
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Amit Mathur
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Robert C. McKinstry
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Jessica L. Wisnowski
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Andreas Rauschecker
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Yvonne W. Wu
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
| | - Yi Li
- From the Department of Radiology, Duke University Medical Center,
2301 Erwin Rd, Box 3808, Durham, NC 27710 (C.O.L., E.C, A.L., J.F.); Department
of Radiology (J.V.C., F.T., G.C., A.R., Y.L.) and Weill Institute for
Neurosciences (Y.W.W.), University of California San Francisco, San Francisco,
Calif; Department of Pediatrics, University of Washington, Seattle, Wash (S.J.);
Department of Pediatrics, Saint Louis University, St Louis, Mo (A.M.);
Mallinckrodt Institute of Radiology, Washington University School of Medicine,
St Louis, Mo (R.C.M.); and Children’s Hospital Los Angeles, University of
Southern California, Los Angeles, Calif (J.L.W.)
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Hung SC, Tu YF, Hunter SE, Guimaraes C. MRI predictors of long-term outcomes of neonatal hypoxic ischaemic encephalopathy: a primer for radiologists. Br J Radiol 2024; 97:1067-1077. [PMID: 38407350 DOI: 10.1093/bjr/tqae048] [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: 08/09/2023] [Revised: 01/12/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024] Open
Abstract
This review aims to serve as a foundational resource for general radiologists, enhancing their understanding of the role of Magnetic Resonance Imaging (MRI) in early prognostication for newborns diagnosed with hypoxic ischaemic encephalopathy (HIE). The article explores the application of MRI as a predictive instrument for determining long-term outcomes in newborns affected by HIE. With HIE constituting a leading cause of neonatal mortality and severe long-term neurodevelopmental impairments, early identification of prognostic indicators is crucial for timely intervention and optimal clinical management. We examine current literature and recent advancements to provide an in-depth overview of MRI predictors, encompassing brain injury patterns, injury scoring systems, spectroscopy, and diffusion imaging. The potential of these MRI biomarkers in predicting long-term neurodevelopmental outcomes and the probability of epilepsy is also discussed.
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Affiliation(s)
- Sheng-Che Hung
- Department of Radiology, School of Medicine, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, United States
| | - Yi-Fang Tu
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70403, Taiwan
| | - Senyene E Hunter
- Department of Neurology, School of Medicine, University of North Carolina Chapel Hill, Chapel Hill, NC 27599-7025, United States
| | - Carolina Guimaraes
- Department of Radiology, School of Medicine, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, United States
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Sullivan BA, Beam K, Vesoulis ZA, Aziz KB, Husain AN, Knake LA, Moreira AG, Hooven TA, Weiss EM, Carr NR, El-Ferzli GT, Patel RM, Simek KA, Hernandez AJ, Barry JS, McAdams RM. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. J Perinatol 2024; 44:1-11. [PMID: 38097685 PMCID: PMC10872325 DOI: 10.1038/s41372-023-01848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.
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Affiliation(s)
- Brynne A Sullivan
- Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Khyzer B Aziz
- Division of Neonatology, Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
| | - Ameena N Husain
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Lindsey A Knake
- Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Alvaro G Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Thomas A Hooven
- Division of Newborn Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elliott M Weiss
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Treuman Katz Center for Pediatric Bioethics and Palliative Care, Seattle Children's Research Institute, Seattle, WA, USA
| | - Nicholas R Carr
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - George T El-Ferzli
- Division of Neonatology, Department of Pediatrics, Ohio State University, Nationwide Children's Hospital, Columbus, OH, USA
| | - Ravi M Patel
- Division of Neonatology, Department of Pediatrics, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Kelsey A Simek
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Antonio J Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - James S Barry
- Division of Neonatology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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Bao R, Song Y, Bates SV, Weiss RJ, Foster AN, Cobos CJ, Sotardi S, Zhang Y, Gollub RL, Grant PE, Ou Y. BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.30.546841. [PMID: 37461570 PMCID: PMC10350009 DOI: 10.1101/2023.06.30.546841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term neonates. Accurate identification and segmentation of HIE-related lesions in neonatal brain magnetic resonance images (MRIs) is the first step toward predicting prognosis, identifying high-risk patients, and evaluating treatment effects. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy. We release the first public dataset containing neonatal brain diffusion MRI and expert annotation of lesions from 133 patients diagnosed with HIE. HIE-related lesions in brain MRI are often diffuse (i.e., multi-focal), and small (over half the patients in our data having lesions occupying <1% of brain volume). Segmentation for HIE MRI data is remarkably different from, and arguably more challenging than, other segmentation tasks such as brain tumors with focal and relatively large lesions. We hope that this dataset can help fuel the development of MRI lesion segmentation methods for HIE and small diffuse lesions in general.
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Affiliation(s)
- Rina Bao
- Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | | | | | - Anna N. Foster
- Boston Children’s Hospital, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Yue Zhang
- Boston Children’s Hospital, Boston, MA, USA
| | - Randy L. Gollub
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - P. Ellen Grant
- Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yangming Ou
- Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Xiao T, Dong X, Lu Y, Zhou W. High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:204-215. [PMID: 37197647 PMCID: PMC10110825 DOI: 10.1007/s43657-022-00071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 05/19/2023]
Abstract
Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.
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Affiliation(s)
- Tiantian Xiao
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Department of Neonatology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610000 China
| | - Xinran Dong
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Yulan Lu
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Wenhao Zhou
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
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Zhuang X, Lin H, Li J, Yin Y, Dong X, Jin K. Radiomics based of deep medullary veins on susceptibility-weighted imaging in infants: predicting the severity of brain injury of neonates with perinatal asphyxia. Eur J Med Res 2023; 28:9. [PMID: 36609425 PMCID: PMC9817267 DOI: 10.1186/s40001-022-00954-y] [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: 09/15/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE This study aimed to apply radiomics analysis of the change of deep medullary veins (DMV) on susceptibility-weighted imaging (SWI), and to distinguish mild hypoxic-ischemic encephalopathy (HIE) from moderate-to-severe HIE in neonates. METHODS A total of 190 neonates with HIE (24 mild HIE and 166 moderate-to-severe HIE) were included in this study. All of them were born at 37 gestational weeks or later. The DMVs were manually included in the regions of interest (ROI). For the purpose of identifying optimal radiomics features and to construct Rad-scores, 1316 features were extracted. LASSO regression was used to identify the optimal radiomics features. Using the Red-score and the clinical independent factor, a nomogram was constructed. In order to evaluate the performance of the different models, receiver operating characteristic (ROC) curve analysis was applied. Decision curve analysis (DCA) was implemented to evaluate the clinical utility. RESULTS A total of 15 potential predictors were selected and contributed to Red-score construction. Compared with the radiomics model, the nomogram combined model incorporating Red-score and urea nitrogen did not better distinguish between the mild HIE and moderate-to-severe HIE group. For the training cohort, the AUC of the radiomics model and the combined nomogram model was 0.84 and 0.84. For the validation cohort, the AUC of the radiomics model and the combined nomogram model was 0.80 and 0.79, respectively. The addition of clinical characteristics to the nomogram failed to distinguish mild HIE from moderate-to-severe HIE group. CONCLUSION We developed a radiomics model and combined nomogram model as an indicator to distinguish mild HIE from moderate-to-severe HIE group.
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Affiliation(s)
- Xiamei Zhuang
- grid.440223.30000 0004 1772 5147Department of Radiology, Hunan Children’s Hospital, 86 Ziyuan Road, Yuhua District, Changsha, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, 410005 China
| | - Junwei Li
- grid.440223.30000 0004 1772 5147Department of Radiology, Hunan Children’s Hospital, 86 Ziyuan Road, Yuhua District, Changsha, China
| | - Yan Yin
- grid.440223.30000 0004 1772 5147Department of Radiology, Hunan Children’s Hospital, 86 Ziyuan Road, Yuhua District, Changsha, China
| | - Xiao Dong
- grid.440223.30000 0004 1772 5147Department of Radiology, Hunan Children’s Hospital, 86 Ziyuan Road, Yuhua District, Changsha, China
| | - Ke Jin
- grid.440223.30000 0004 1772 5147Department of Radiology, Hunan Children’s Hospital, 86 Ziyuan Road, Yuhua District, Changsha, China
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Sarioglu FC, Sarioglu O, Guleryuz H, Deliloglu B, Tuzun F, Duman N, Ozkan H. The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia. Br J Radiol 2022; 95:20210128. [PMID: 34919441 PMCID: PMC9153720 DOI: 10.1259/bjr.20210128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE To evaluate the efficacy of the MRI-based texture analysis (TA) of the basal ganglia and thalami to distinguish moderate-to-severe hypoxic-ischemic encephalopathy (HIE) from mild HIE in neonates. METHODS This study included 68 neonates (15 with mild, 20 with moderate-to-severe HIE, and 33 control) were born at 37 gestational weeks or later and underwent MRI in first 10 days after birth. The basal ganglia and thalami were delineated for TA on the apparent diffusion coefficient (ADC) maps, T1-, and T2 weighted images. The basal ganglia, thalami, and the posterior limb of the internal capsule (PLIC) were also evaluated visually on diffusion-weighted imaging and T1 weighted sequence. Receiver operating characteristic curve and logistic regression analyses were used. RESULTS Totally, 56 texture features for the basal ganglia and 46 features for the thalami were significantly different between the HIE groups on the ADC maps, T2-, and T2 weighted sequences. Using a Histogram_entropy log-10 value as >1.8 from the basal ganglia on the ADC maps (p < 0.001; OR, 266) and the absence of hyperintensity of the PLIC on T1 weighted images (p = 0.012; OR, 17.11) were found as independent predictors for moderate-to-severe HIE. Using only a Histogram_entropy log-10 value had an equal diagnostic yield when compared to its combination with other texture features and imaging findings. CONCLUSION The Histogram_entropy log-10 value can be used as an indicator to differentiate from moderate-to-severe to mild HIE. ADVANCES IN KNOWLEDGE MRI-based TA may provide quantitative findings to indicate different stages in neonates with perinatal asphyxia.
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Affiliation(s)
- Fatma Ceren Sarioglu
- Division of Pediatric Radiology, Department of Radiology, Dokuz Eylul University School of Medicine, İzmir, Turkey
| | - Orkun Sarioglu
- Department of Radiology, Izmir Democracy University School of Medicine, Izmir, Turkey
| | - Handan Guleryuz
- Division of Pediatric Radiology, Department of Radiology, Dokuz Eylul University School of Medicine, İzmir, Turkey
| | - Burak Deliloglu
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
| | - Funda Tuzun
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
| | - Nuray Duman
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
| | - Hasan Ozkan
- Division of Neonatology, Department of Pediatrics, Dokuz Eylul University School of Medicine, Izmir, Turkey
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Vedmurthy P, Pinto ALR, Lin DDM, Comi AM, Ou Y. Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber. BMJ Open 2022; 12:e053103. [PMID: 35121603 PMCID: PMC8819809 DOI: 10.1136/bmjopen-2021-053103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 01/13/2022] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Secondary analysis of hospital-hosted clinical data can save time and cost compared with prospective clinical trials for neuroimaging biomarker development. We present such a study for Sturge-Weber syndrome (SWS), a rare neurovascular disorder that affects 1 in 20 000-50 000 newborns. Children with SWS are at risk for developing neurocognitive deficit by school age. A critical period for early intervention is before 2 years of age, but early diagnostic and prognostic biomarkers are lacking. We aim to retrospectively mine clinical data for SWS at two national centres to develop presymptomatic biomarkers. METHODS AND ANALYSIS We will retrospectively collect clinical, MRI and neurocognitive outcome data for patients with SWS who underwent brain MRI before 2 years of age at two national SWS care centres. Expert review of clinical records and MRI quality control will be used to refine the cohort. The merged multisite data will be used to develop algorithms for abnormality detection, lesion-symptom mapping to identify neural substrate and machine learning to predict individual outcomes (presence or absence of seizures) by 2 years of age. Presymptomatic treatment in 0-2 years and before seizure onset may delay or prevent the onset of seizures by 2 years of age, and thereby improve neurocognitive outcomes. The proposed work, if successful, will be one of the largest and most comprehensive multisite databases for the presymptomatic phase of this rare disease. ETHICS AND DISSEMINATION This study involves human participants and was approved by Boston Children's Hospital Institutional Review Board: IRB-P00014482 and IRB-P00025916 Johns Hopkins School of Medicine Institutional Review Board: NA_00043846. Participants gave informed consent to participate in the study before taking part. The Institutional Review Boards at Kennedy Krieger Institute and Boston Children's Hospital approval have been obtained at each site to retrospectively study this data. Results will be disseminated by presentations, publication and sharing of algorithms generated.
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Affiliation(s)
- Pooja Vedmurthy
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Anna L R Pinto
- Department of Neurology, Division of Epilepsy, Harvard Medical School, Boston, Massachusetts, USA
| | - Doris D M Lin
- Neuroradiology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Anne M Comi
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology and Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital; Harvard Medical School, Boston, MA, USA
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9
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Jeong E, Osmundson S, Gao C, Edwards DRV, Malin B, Chen Y. Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106397. [PMID: 34530389 PMCID: PMC8551018 DOI: 10.1016/j.cmpb.2021.106397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE There is a wide range of risk factors predisposing to the onset of neonatal encephalopathy (NE), including maternal antepartum/intrapartum comorbidities or events. However, few studies have investigated the difference in the impact of acute and chronic diseases on forecasting NE, which could assist clinicians in choosing the best course of action to prevent NE or reduce its severity and complications. In this study, we aimed to engineer features based on acute and chronic diseases and assess the differences of the impact of acute and chronic diseases on NE prediction using machine learning models. MATERIALS AND METHODS We used ten years of electronic health records of mothers from a large academic medical center to develop three types of features: chronic disease, recurrence of an acute disease, and temporal relationships between acute diseases. Two types of NE prediction models, based on acute and chronic diseases, respectively, were trained with feature selection. We further compared the prediction performance of the models with two state-of-the-art NE forecasting models. The machine learning models ranked the three types of engineered features based on their contributions to the NE prediction. RESULTS The NE model trained on acute disease features showed significantly higher AUC than the model relying on chronic disease features (AUC difference: 0.161, p-value < 0.001). The NE model trained on both acute and chronic disease features achieved the highest average AUC (0.889), with a significant improvement over the best existing model (0.854) with p = 0.0129. Recurrence of "known or suspected fetal abnormality affecting management of mother (655)" was assigned the highest weights in predicting NE. CONCLUSIONS Machine learning models based on the three types of engineered features significantly improve NE prediction. Our results specifically suggest that acute disease-associated features play a more important role in predicting NE.
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Affiliation(s)
- Eugene Jeong
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sarah Osmundson
- Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Cheng Gao
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Digna R Velez Edwards
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bradley Malin
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biostatistics, School of Medicine, Vanderbilt University, Nashville, TN, United States; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - You Chen
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States.
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10
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Martin D, Tong E, Kelly B, Yeom K, Yedavalli V. Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview. FRONTIERS IN RADIOLOGY 2021; 1:713681. [PMID: 37492174 PMCID: PMC10365125 DOI: 10.3389/fradi.2021.713681] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 07/21/2021] [Indexed: 07/27/2023]
Abstract
Artificial Intelligence, Machine Learning, and myriad related techniques are becoming ever more commonplace throughout industry and society, and radiology is by no means an exception. It is essential for every radiologists of every subspecialty to gain familiarity and confidence with these techniques as they become increasingly incorporated into the routine practice in both academic and private practice settings. In this article, we provide a brief review of several definitions and techniques that are commonly used in AI, and in particular machine vision, and examples of how they are currently being applied to the setting of clinical neuroradiology. We then review the unique challenges that the adoption and application of faces within the subspecialty of pediatric neuroradiology, and how these obstacles may be overcome. We conclude by presenting specific examples of how AI is currently being applied within the field of pediatric neuroradiology and the potential opportunities that are available for future applications.
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Affiliation(s)
- Dann Martin
- Vanderbilt University, Nashville, TN, United States
| | - Elizabeth Tong
- Department of Neuroradiology, Stanford Health Care, Stanford, CA, United States
| | - Brendan Kelly
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Kristen Yeom
- Department of Neuroradiology, Stanford Health Care, Stanford, CA, United States
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11
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He S, Pereira D, David Perez J, Gollub RL, Murphy SN, Prabhu S, Pienaar R, Robertson RL, Ellen Grant P, Ou Y. Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan. Med Image Anal 2021; 72:102091. [PMID: 34038818 PMCID: PMC8316301 DOI: 10.1016/j.media.2021.102091] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 03/10/2021] [Accepted: 04/14/2021] [Indexed: 12/31/2022]
Abstract
Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy, generality, and interpretation of age estimation in healthy brain MRIs. For accuracy, we used one of the largest sample sizes (N = 16,705). For each subject, our proposed algorithm first explicitly splits the T1w image, which has been commonly treated as a single-channel 3D image in other studies, into two 3D image channels representing contrast and morphometry information. We further proposed a "fusion-with-attention" deep learning convolutional neural network (FiA-Net) to learn how to best fuse the contrast and morphometry image channels. FiA-Net recognizes varying contributions across image channels at different brain anatomy and different feature layers. In contrast, multi-channel fusion does not exist for brain age estimation, and is mostly attention-free in other medical image analysis tasks (e.g., image synthesis, or segmentation), where treating channels equally may not be optimal. For generality, we used lifespan data 0-97 years of age for real-world utility; and we thoroughly tested FiA-Net for multi-site and multi-scanner generality by two phases of cross-validations in discovery and replication data, compared to most other studies with only one phase of cross-validation. For interpretation, we directly measured each artificial neuron's correlation with the chronological age, compared to other studies looking at the saliency of features where salient features may or may not predict age. Overall, FiA-Net achieved a mean absolute error (MAE) of 3.00 years and Pearson correlation r=0.9840 with known chronological ages in healthy brain MRIs 0-97 years of age, comparing favorably with state-of-the-art algorithms and studies for accuracy and generality across sites and datasets. We also provided interpretations on how different artificial neurons and real neuroanatomy contribute to the age estimation.
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Affiliation(s)
- Sheng He
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Diana Pereira
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Juan David Perez
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Randy L Gollub
- Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA, USA
| | - Shawn N Murphy
- Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA, USA
| | - Sanjay Prabhu
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Rudolph Pienaar
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Richard L Robertson
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - P Ellen Grant
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Yangming Ou
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA.
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12
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Abstract
Die Radiologie ist von stetem Wandel geprägt und definiert sich über den technologischen Fortschritt. Künstliche Intelligenz (KI) wird die praktische Tätigkeit in der Kinder- und Jugendradiologie künftig in allen Belangen verändern. Bildakquisition, Befunderkennung und -segmentierung sowie die Erkennung von Gewebeeigenschaften und deren Kombination mit Big Data werden die Haupteinsatzgebiete in der Radiologie sein. Höhere Effektivität, Beschleunigung von Untersuchung und Befundung sowie Kosteneinsparung sind mit der Anwendung von KI verbundene Erwartungshaltungen. Ein verbessertes Patientenmanagement, Arbeitserleichterungen für medizinisch-technische Radiologieassistenten und Kinder- und Jugendradiologen sowie schnellere Untersuchungs- und Befundzeiten markieren die Meilensteine der KI-Entwicklung in der Radiologie. Von der Terminkommunikation und Gerätesteuerung bis zu Therapieempfehlung und -monitoring wird der Alltag durch Elemente der KI verändert. Kinder- und Jugendradiologen müssen daher grundlegend über KI informiert sein und mit Datenwissenschaftlern bei der Etablierung und Anwendung von KI-Elementen zusammenarbeiten.
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Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC. Artificial intelligence in paediatric radiology: Future opportunities. Br J Radiol 2021; 94:20200975. [PMID: 32941736 PMCID: PMC7774693 DOI: 10.1259/bjr.20200975] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 09/04/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.
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Affiliation(s)
- Natasha Davendralingam
- Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
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