Beyond Needles: Pioneering Pediatric Care with Virtual Reality (VR) for TIVAD Access in Oncology.

INTRODUCTION: Pediatric oncology patients use totally implantable venous access devices (TIVADs) to enable central venous access. Anxiety, pain and/or discomfort are common despite anesthesia. Virtual reality (VR) is a non-pharmacological approach that may reduce pain and anxiety in these patients. We aimed to assess the use of VR for reducing anxiety/pain in patients with TIVADs while facilitating the task of healthcare providers when accessing a TIVAD. METHODS: patients 4-18 years old with a TIVAD were prospectively randomized to an intervention group (IG) or a control group (CG). In the IG, VR goggles (Oculus Quest 2, Meta Platforms®, Menlo Park, CA, USA) were used displaying a relaxing video in the Raja Ampat environment (Ecosphere app from Phoria®) while the TIVAD was accessed. The CG was managed as per standard of care. Satisfaction and pain were measured by FPS-R and STAIC scales, respectively. RESULTS: this is the report of a prospective, randomized (60 per group)-unblinded-, single institution study of 120 pediatric oncology patients enrolled from January to April 2022. Median ages for IG and CG were 9.22 and 10.52 years, respectively. Satisfaction was higher in the IG (4.80) compared to the CG (3.92), p ≤ 0.0001. Regarding pain, mean FPS-R scores were 1.79 for the CG and 0.83 for the IG. Significantly different scores were found in the 12 to 18 years group, p ≤ 0.05. The healthcare professionals index of satisfaction was high (4.50 mean Likert score) for the IG compared to accessing the TIVAD without VR (3.73 mean Likert score). CONCLUSION: The use of VR helped reduce pain and/or discomfort in pediatric oncology patients, mainly in the older age group as they can better interact with VR. Healthcare providers were satisfied with the help of VR for TIVAD management.

Analysis of the implementation of a circuit for intra-operative superposition and comparison of the surgical outcomes using ICBCT in maxillofacial surgery.

PURPOSE: This paper describes a novel circuit for intraoperative analysis with ICBCT in maxillofacial surgery. The aim is to establish guidelines, define indications, and conduct an analysis of the implementation of the circuit for intraoperative comparison of surgical outcomes in relation to 3D virtual planning in maxillofacial surgery. METHODS: The study included 150 maxillofacial surgical procedures. Intraoperative actions involved fluoroscopy localization, intraoperative CBCT acquisition, segmentation, and superimposition, among other steps. Surgical times due to intraoperative superposition were measured, including time required for ICBCT positioning and acquisition, image segmentation, and comparison of 3D surfaces from the surgical planning. RESULTS: Successful intraoperative comparison was achieved in all 150 cases, enabling surgeons to detect and address modifications before concluding the surgery. Out of the total, 26 patients (17.33%) required intraoperative revisions, with 11 cases (7.33%) needing major surgical revisions. On average, the additional surgical time with this circuit implementation was 10.66 ± 3.03 min (n = 22). CONCLUSION: The results of our research demonstrate the potential for performing intraoperative surgical revision, allowing for immediate evaluation, enhancing surgical outcomes, and reducing the need for re-interventions.

A state-of-the-art guide about the effects of sterilization processes on 3D-printed materials for surgical planning and medical applications: A comparative study.

Surgeons use different medical devices in the surgery, such as patient-specific anatomical models, cutting and positioning guides, or implants. These devices must be sterilized before being used in the operation room. There are many sterilization processes available, with autoclave, hydrogen peroxide, and ethylene oxide being the most common in hospital settings. Each method has both advantages and disadvantages in terms of mechanics, chemical interaction, and post-treatment accuracy. The aim of the present study is to evaluate the dimensional and mechanical effect of the most commonly used sterilization techniques available in clinical settings, i.e., Autoclave 121, Autoclave 134, and hydrogen peroxide (HPO), on 11 of the most used 3D-printed materials fabricated using additive manufacturing technologies. The results showed that the temperature (depending on the sterilization method) and the exposure time to that temperature influence not only the mechanical behavior but also the original dimensioning planned on the 3D model. Therefore, HPO is a better overall option for most of the materials evaluated. Finally, based on the results of the study, a recommendation guide on sterilization methods per material, technology, and clinical application is presented.

Exploring the Potential of Three-Dimensional Imaging, Printing, and Modeling in Pediatric Surgical Oncology: A New Era of Precision Surgery.

Pediatric surgical oncology is a technically challenging field that relies on CT and MRI as the primary imaging tools for surgical planning. However, recent advances in 3D reconstructions, including Cinematic Rendering, Volume Rendering, 3D modeling, Virtual Reality, Augmented Reality, and 3D printing, are increasingly being used to plan complex cases bringing new insights into pediatric tumors to guide therapeutic decisions and prognosis in different pediatric surgical oncology areas and locations including thoracic, brain, urology, and abdominal surgery. Despite this, challenges to their adoption remain, especially in soft tissue-based specialties such as pediatric surgical oncology. This work explores the main innovative imaging reconstruction techniques, 3D modeling technologies (CAD, VR, AR), and 3D printing applications through the analysis of three real cases of the most common and surgically challenging pediatric tumors: abdominal neuroblastoma, thoracic inlet neuroblastoma, and a bilateral Wilms tumor candidate for nephron-sparing surgery. The results demonstrate that these new imaging and modeling techniques offer a promising alternative for planning complex pediatric oncological cases. A comprehensive analysis of the advantages and limitations of each technique has been carried out to assist in choosing the optimal approach.

Patient-Specific 3D Printed Soft Models for Liver Surgical Planning and Hands-On Training.

Background: Pre-surgical simulation-based training with three-dimensional (3D) models has been intensively developed in complex surgeries in recent years. This is also the case in liver surgery, although with fewer reported examples. The simulation-based training with 3D models represents an alternative to current surgical simulation methods based on animal or ex vivo models or virtual reality (VR), showing reported advantages, which makes the development of realistic 3D-printed models an option. This work presents an innovative, low-cost approach for producing patient-specific 3D anatomical models for hands-on simulation and training. Methods: The article reports three paediatric cases presenting complex liver tumours that were transferred to a major paediatric referral centre for treatment: hepatoblastoma, hepatic hamartoma and biliary tract rhabdomyosarcoma. The complete process of the additively manufactured liver tumour simulators is described, and the different steps for the correct development of each case are explained: (1) medical image acquisition; (2) segmentation; (3) 3D printing; (4) quality control/validation; and (5) cost. A digital workflow for liver cancer surgical planning is proposed. Results: Three hepatic surgeries were planned, with 3D simulators built using 3D printing and silicone moulding techniques. The 3D physical models showed highly accurate replications of the actual condition. Additionally, they proved to be more cost-effective in comparison with other models. Conclusions: It is demonstrated that it is possible to manufacture accurate and cost-effective 3D-printed soft surgical planning simulators for treating liver cancer. The 3D models allowed for proper pre-surgical planning and simulation training in the three cases reported, making it a valuable aid for surgeons.

Advanced Strategies for the Fabrication of Multi-Material Anatomical Models of Complex Pediatric Oncologic Cases.

The printing and manufacturing of anatomical 3D models has gained popularity in complex surgical cases for surgical planning, simulation and training, the evaluation of anatomical relations, medical device testing and patient-professional communication. 3D models provide the haptic feedback that Virtual or Augmented Reality (VR/AR) cannot provide. However, there are many technologies and strategies for the production of 3D models. Therefore, the aim of the present study is to show and compare eight different strategies for the manufacture of surgical planning and training prototypes. The eight strategies for creating complex abdominal oncological anatomical models, based on eight common pediatric oncological cases, were developed using four common technologies (stereolithography (SLA), selectie laser sinterning (SLS), fused filament fabrication (FFF) and material jetting (MJ)) along with indirect and hybrid 3D printing methods. Nine materials were selected for their properties, with the final models assessed for application suitability, production time, viscoelastic mechanical properties (shore hardness and elastic modulus) and cost. The manufacturing and post-processing of each strategy is assessed, with times ranging from 12 h (FFF) to 61 h (hybridization of FFF and SLS), as labor times differ significantly. Cost per model variation is also significant, ranging from EUR 80 (FFF) to EUR 600 (MJ). The main limitation is the mimicry of physiological properties. Viscoelastic properties and the combination of materials, colors and textures are also substantially different according to the strategy and the intended use. It was concluded that MJ is the best overall option, although its use in hospitals is limited due to its cost. Consequently, indirect 3D printing could be a solid and cheaper alternative.

Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery.

Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative complications and clinical deterioration either due to their underlying pathology or due to the cardiac surgery, contributing significantly to mortality, morbidity, hospital and family costs, and poor quality of life. In current clinical practice, clinical deterioration is detected, in most of the cases, when it has already occurred. Several early warning scores (EWS) have been proposed to assess children at risk of clinical deterioration using vital signs and risk indicators, in order to intervene in a timely manner to reduce the impact of deterioration and risk of death among children. However, EWS are based on measurements performed at a single time point without incorporating trends nor providing information about patient's risk trajectory. Moreover, some of these measurements rely on subjective assessment making them susceptible to different interpretations. All these limitations could explain why the implementation of EWS in high-resource settings failed to show a significant decrease in hospital mortality. By means of machine learning (ML) based algorithms we could integrate heterogeneous and complex data to predict patient's risk of deterioration. In this perspective article, we provide a brief overview of the potential of ML technologies to improve the identification of pediatric CHD patients at high-risk for clinical deterioration after cardiac surgery, and present the CORTEX traffic light, a ML-based predictive system that Sant Joan de Déu Barcelona Children's Hospital is implementing, as an illustration of the application of an ML-based risk stratification system in a relevant hospital setting.