Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound
Objective: To develop deep learning (DL) models applied to neonatal cranial ultrasound (CUS) and clinical variables to predict neurodevelopmental impairment (NDI) in very preterm infants (VPIs) at 3 years of corrected age. Patients and Methods: This is a retrospective study of a cohort of VPI (220-3...
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| Format: | Article |
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Elsevier
2024-12-01
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| Series: | Mayo Clinic Proceedings: Digital Health |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949761224001007 |
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| author | Tahani M. Ahmad, MD, ABR Alessandro Guida, PhD Sam Stewart, PhD Noah Barrett, MSc Michael J. Vincer, MD Jehier K. Afifi, MD, MSc |
| author_facet | Tahani M. Ahmad, MD, ABR Alessandro Guida, PhD Sam Stewart, PhD Noah Barrett, MSc Michael J. Vincer, MD Jehier K. Afifi, MD, MSc |
| author_sort | Tahani M. Ahmad, MD, ABR |
| collection | DOAJ |
| description | Objective: To develop deep learning (DL) models applied to neonatal cranial ultrasound (CUS) and clinical variables to predict neurodevelopmental impairment (NDI) in very preterm infants (VPIs) at 3 years of corrected age. Patients and Methods: This is a retrospective study of a cohort of VPI (220-306 weeks’ gestation) born between 2004 and 2016 in Nova Scotia, Canada. Clinical data at hospital discharge and CUS images at 3 time points were used to develop DL models using elastic net (EN) and convolutional neural network (CNN). The models’ performances were compared using precision recall area under the curve (PR-AUC) and area under the receiver operation characteristic curve (ROC-AUC) with their 95% ci. Results: Of 665 eligible VPIs, 619 (93%) infants with 4184 CUS images were included. The CNN model combining CUS and clinical variables reported better performance (PR-AUC, 0.75; 95% CI, 072-0.79; ROC-AUC, 0.71; 95% CI, 0.67-0.74) in the prediction of positive NDI outcome compared with the traditional models based solely on clinical predictors (PR-AUC, 0.60; 95% CI, 0.52-0.68; ROC-AUC, 0.72; 95% CI, 0.68-0.75). When analyzed by the CUS plane and acquisition time point, the model using the anterior coronal plane at 6 weeks of age provided the highest predictive accuracy (PR-AUC, 0.81; 95% CI, 0.77-0.91; ROC-AUC, 0.78; 95% CI, 0.66-0.87). Conclusion: We developed and internally validated a DL prognostic model using CUS and clinical predictors to predict NDI in VPIs at 3 years of age. Early and accurate identification of infants at risk for NDI enables referral to targeted interventions, which improves functional outcomes. |
| format | Article |
| id | doaj-art-ce11ff2789174a4b8d05d7529a6a88be |
| institution | OA Journals |
| issn | 2949-7612 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Mayo Clinic Proceedings: Digital Health |
| spelling | doaj-art-ce11ff2789174a4b8d05d7529a6a88be2025-08-20T01:47:25ZengElsevierMayo Clinic Proceedings: Digital Health2949-76122024-12-012459660510.1016/j.mcpdig.2024.09.003Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral UltrasoundTahani M. Ahmad, MD, ABR0Alessandro Guida, PhD1Sam Stewart, PhD2Noah Barrett, MSc3Michael J. Vincer, MD4Jehier K. Afifi, MD, MSc5Department of Pediatric Radiology, IWK Health, Halifax, Nova Scotia, Canada; Department of Diagnostic Radiology, Dalhousie University, IWK Health, Nova Scotia, Canada; Department of Radiology, University of Jordan, Amman, Jordan; Correspondence: Tahani Ahmad, MD, IWK Health, Department of Diagnostic Imaging, 5850/5980 University Avenue, PO Box 9700, Halifax, Nova Scotia B3K6R8, Canada.Biomedical Translational Imaging Centre (BIOTIC), Department of Diagnostic Radiology, Dalhousie University, Halifax, Nova Scotia, Canada; Department of Diagnostic Imaging, Nova Scotia Health, Nova Scotia, CanadaDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, CanadaFaculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, CanadaDivision of Neonatal-Perinatal Medicine, IWK Health, Halifax, Nova Scotia, Canada; Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia, CanadaDivision of Neonatal-Perinatal Medicine, IWK Health, Halifax, Nova Scotia, Canada; Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia, CanadaObjective: To develop deep learning (DL) models applied to neonatal cranial ultrasound (CUS) and clinical variables to predict neurodevelopmental impairment (NDI) in very preterm infants (VPIs) at 3 years of corrected age. Patients and Methods: This is a retrospective study of a cohort of VPI (220-306 weeks’ gestation) born between 2004 and 2016 in Nova Scotia, Canada. Clinical data at hospital discharge and CUS images at 3 time points were used to develop DL models using elastic net (EN) and convolutional neural network (CNN). The models’ performances were compared using precision recall area under the curve (PR-AUC) and area under the receiver operation characteristic curve (ROC-AUC) with their 95% ci. Results: Of 665 eligible VPIs, 619 (93%) infants with 4184 CUS images were included. The CNN model combining CUS and clinical variables reported better performance (PR-AUC, 0.75; 95% CI, 072-0.79; ROC-AUC, 0.71; 95% CI, 0.67-0.74) in the prediction of positive NDI outcome compared with the traditional models based solely on clinical predictors (PR-AUC, 0.60; 95% CI, 0.52-0.68; ROC-AUC, 0.72; 95% CI, 0.68-0.75). When analyzed by the CUS plane and acquisition time point, the model using the anterior coronal plane at 6 weeks of age provided the highest predictive accuracy (PR-AUC, 0.81; 95% CI, 0.77-0.91; ROC-AUC, 0.78; 95% CI, 0.66-0.87). Conclusion: We developed and internally validated a DL prognostic model using CUS and clinical predictors to predict NDI in VPIs at 3 years of age. Early and accurate identification of infants at risk for NDI enables referral to targeted interventions, which improves functional outcomes.http://www.sciencedirect.com/science/article/pii/S2949761224001007 |
| spellingShingle | Tahani M. Ahmad, MD, ABR Alessandro Guida, PhD Sam Stewart, PhD Noah Barrett, MSc Michael J. Vincer, MD Jehier K. Afifi, MD, MSc Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound Mayo Clinic Proceedings: Digital Health |
| title | Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound |
| title_full | Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound |
| title_fullStr | Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound |
| title_full_unstemmed | Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound |
| title_short | Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound |
| title_sort | deep learning model for predicting neurodevelopmental outcome in very preterm infants using cerebral ultrasound |
| url | http://www.sciencedirect.com/science/article/pii/S2949761224001007 |
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