PUUMA (Placental Patch and Whole-Uterus Dual-Branch U-Mamba-Based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk
Fajardo-Rojas D, Baljer L, Aviles Verdera J, Hall M, Cromb D, Rutherford MA, Story L, Robinson EC, Hutter J (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 16118 LNCS
Pages Range: 153-163
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Daejeon, KOR
ISBN: 9783032059963
DOI: 10.1007/978-3-032-05997-0_14
Abstract
Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and impede optimal care. In this study, a dual-branch deep learning architecture (PUUMA) was developed to predict gestational age (GA) at birth using T2* fetal MRI data from 295 pregnancies, encompassing a heterogeneous and imbalanced population. The model integrates both global whole-uterus and local placental features. Its performance was benchmarked against linear regression using cervical length measurements obtained by experienced clinicians from anatomical MRI and other Deep Learning architectures. The GA at birth predictions were assessed using mean absolute error. Accuracy, sensitivity, and specificity were used to assess preterm classification. Both the fully automated MRI-based pipeline and the cervical length regression achieved comparable mean absolute errors (3 weeks) and good sensitivity (0.67) for detecting preterm birth, despite pronounced class imbalance in the data set. These results provide a proof of concept for automated prediction of GA at birth from functional MRI, and underscore the value of whole-uterus functional imaging in identifying at-risk pregnancies. Additionally, we demonstrate that manual, high-definition cervical length measurements derived from MRI —not currently routine in clinical practice— offer valuable predictive information. Future work will focus on expanding the cohort size and incorporating additional organ-specific imaging to improve generalisability and predictive performance.
Authors with CRIS profile
Involved external institutions
United Kingdom (GB)How to cite
APA:
Fajardo-Rojas, D., Baljer, L., Aviles Verdera, J., Hall, M., Cromb, D., Rutherford, M.A.,... Hutter, J. (2026). PUUMA (Placental Patch and Whole-Uterus Dual-Branch U-Mamba-Based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk. In Daphna Link-Sourani, Esra Abaci Turk, Wietske Bastiaansen, Jana Hutter, Andrew Melbourne, Roxane Licandro (Eds.), Lecture Notes in Computer Science (pp. 153-163). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
MLA:
Fajardo-Rojas, Diego, et al. "PUUMA (Placental Patch and Whole-Uterus Dual-Branch U-Mamba-Based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk." Proceedings of the 10th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2025, Held in Conjunction with 28th International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2025, Daejeon, KOR Ed. Daphna Link-Sourani, Esra Abaci Turk, Wietske Bastiaansen, Jana Hutter, Andrew Melbourne, Roxane Licandro, Springer Science and Business Media Deutschland GmbH, 2026. 153-163.
BibTeX: Download