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A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data

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Abstract

Background

Deep learning has been employed using brain functional connectome data for evaluating the risk of cognitive deficits in very preterm infants. Although promising, training these deep learning models typically requires a large amount of labeled data, and labeled medical data are often very difficult and expensive to obtain.

Objective

This study aimed to develop a self-training deep neural network (DNN) model for early prediction of cognitive deficits at 2 years of corrected age in very preterm infants (gestational age ≤32 weeks) using both labeled and unlabeled brain functional connectome data.

Materials and methods

We collected brain functional connectome data from 343 very preterm infants at a mean (standard deviation) postmenstrual age of 42.7 (2.5) weeks, among whom 103 children had a cognitive assessment at 2 years (i.e. labeled data), and the remaining 240 children had not received 2-year assessments at the time this study was conducted (i.e. unlabeled data). To develop a self-training DNN model, we built an initial student model using labeled brain functional connectome data. Then, we applied the trained model as a teacher model to generate pseudo-labels for unlabeled brain functional connectome data. Next, we combined labeled and pseudo-labeled data to train a new student model. We iterated this procedure to obtain the best student model for the early prediction task in very preterm infants.

Results

In our cross-validation experiments, the proposed self-training DNN model achieved an accuracy of 71.0%, a specificity of 71.5%, a sensitivity of 70.4% and an area under the curve of 0.75, significantly outperforming transfer learning models through pre-training approaches.

Conclusion

We report the first self-training prognostic study in very preterm infants, efficiently utilizing a small amount of labeled data with a larger share of unlabeled data to aid the model training. The proposed technique is expected to facilitate deep learning with insufficient training data.

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Acknowledgments

We sincerely thank our collaborators from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS) Group: principal investigator Nehal A. Parikh, DO, MS; and collaborators (in alphabetical order) Mekibib Altaye, PhD; Anita Arnsperger, RRT; Traci Beiersdorfer, RN BSN; Kaley Bridgewater, RT(MR) CNMT; Tanya Cahill, MD; Kim Cecil, PhD; Kent Dietrich, RT; Christen Distler, BSN RNC-NIC; Juanita Dudley, RN BSN; Brianne Georg, BS; Cathy Grisby, RN BSN CCRC; Lacey Haas, RT(MR) CNMT; Karen Harpster, PhD, OT/RL; Scott K. Holland, PhD; V.S. Priyanka Illapani, MS; Kristin Kirker, CRC; Julia E. Kline, PhD; Beth M. Kline-Fath, MD; Matt Lanier, RT(MR) RT(R); Stephanie L. Merhar, MD MS; Greg Muthig, BS; Brenda B. Poindexter, MD MS; David Russell, JD; Kari Tepe, BSN RNC-NIC; Leanne Tamm, PhD; Julia Thompson, RN BSN; Jean A. Tkach, PhD; Jinghua Wang, PhD; Brynne Williams, RT(MR) CNMT; Kelsey Wineland, RT(MR) CNMT; Sandra Wuertz, RN BSN CCRP; Donna Wuest, AS; and Weihong Yuan, PhD. We are most grateful to the families that made this study possible.

Funding

This work was supported by the National Institutes of Health (R01-EB029944, R01-EB030582, R01-NS094200 and R01-NS096037); and the Academic and Research Committee (ARC) Awards of Cincinnati Children's Hospital Medical Center. The funders played no role in the design, analysis or presentation of the findings.

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Authors and Affiliations

  1. Imaging Research Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Redha Ali, Hailong Li, Jonathan R. Dillman, Hui Wang & Lili He

  2. Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., MLC 5033, Cincinnati, OH, 45229, USA

    Redha Ali, Hailong Li, Jonathan R. Dillman, Hui Wang & Lili He

  3. Center for Artificial Intelligence in Imaging Research, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Hailong Li, Jonathan R. Dillman & Lili He

  4. Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Hailong Li, Mekibib Altaye, Nehal A. Parikh & Lili He

  5. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Jonathan R. Dillman & Lili He

  6. Biostatistics & Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Mekibib Altaye

  7. MR Clinical Science, Philips, Cincinnati, OH, USA

    Hui Wang

  8. The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Nehal A. Parikh

  9. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Nehal A. Parikh

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  1. Redha Ali
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Ali, R., Li, H., Dillman, J.R. et al. A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatr Radiol 52, 2227–2240 (2022). https://doi.org/10.1007/s00247-022-05510-8

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