# Use of Patient-Specific 3D Models in Paediatric Surgery: Effect on Communication and Surgical Management

## TL;DR

3D models help doctors explain complex pediatric conditions to families and improve surgical planning, despite being time-consuming to create.

## Contribution

The study introduces an efficient workflow for 3D model creation and highlights their impact on family communication and clinical decisions.

## Key findings

- Families showed improved understanding after viewing 3D models (mean score increased from 3.94 to 4.67).
- Physicians rated the 3D models positively for their clinical utility.
- An efficient post-processing workflow was established for model creation.

## Abstract

Children with rare tumours and malformations may benefit from innovative imaging, including patient-specific 3D models that can enhance communication and surgical planning. The primary aim was to evaluate the impact of patient-specific 3D models on communication with families. The secondary aims were to assess their influence on medical management and to establish an efficient post-processing workflow. From 2021 to 2024, we prospectively included patients aged 3 months to 18 years with rare tumours or malformations. Families completed questionnaires before and after the presentation of a 3D model generated from MRI sequences, including peripheral nerve tractography. Treating physicians completed a separate questionnaire before surgical planning. Analyses were performed in R. Among 21 patients, diagnoses included 11 tumours, 8 malformations, 1 trauma, and 1 pancreatic pseudo-cyst. Likert scale responses showed improved family understanding after viewing the 3D model (mean score 3.94 to 4.67) and a high overall evaluation (mean 4.61). Physicians also rated the models positively. An efficient image post-processing workflow was defined. Although manual 3D reconstruction remains time-consuming, these preliminary results show that colourful, patient-specific 3D models substantially improve family communication and support clinical decision-making. They also highlight the need for supporting the development of MRI-based automated segmentation softwares using deep neural networks, which are clinically approved and usable in routine practice.