Objective: The urinary tract dilation (UTD) classification system provides objective assessment relevant to hydronephrosis management for children. However, the lack of uniform language regarding UTD in radiology reports leads to significant difficulty in both clinical management and research. We seek to develop a unified multi-task/multi-class model that can effectively extract UTD components and classifications from early postnatal ultrasound (US) reports. Methods: Radiology records from our institution were reviewed to identify infants aged 0-90 days undergoing early ultrasound for antenatal UTD. The report and images were reviewed by the study team to create the ground truth of UTD classification and components (primary outcome). Bio_ClinicalBERT, a variant of the Bidirectional Encoder Representations from Transformers (BERT) model, was used as the embedding layers of the classification model. The model was fine-tuned with 11 linear classification layers. All but the last BERT layer were frozen during the fine-tuning process. The model performance was evaluated with five-fold cross-validation with an 80:20 train-test ratio. Results: 2460 early (0-90 days) US reports were included. The five-fold cross-validated model performance is satisfactory (Weighted F1 > 0.9 for all UTD components). We report the weighted F1 scores, accuracies, and standard deviations for all 11 tasks and their average performance. Conclusions: By applying deep state-of-the-art NLP neural networks, we developed a high-performing, efficient, and scalable solution to extract UTD components from unstructured ultrasound reports using one single multi-task model. This can potentially help standardize and facilitate large-scale computer vision research for pediatric hydronephrosis. Key Words: machine learning, efficiency, ambulatory care, forecasting

The authors have declared no competing interest.

This study did not receive any funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of Boston Children's Hospital gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All data produced in the present study are available upon reasonable request to the authors, pending the necessary institutional reviews and approvals.