The potential of Real-Time volume-rendered 3D Imaging in immersive virtual-reality (VR) for surgical planning in infants with congenital thoracic malformation (CTM)

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Background: CTM are difficult to evaluate preoperatively due to the limitations of low-dose computed tomography imaging in infants. Digital twin technologies, such as immersive VR, offer interactive, patient-specific simulations that replicate anatomical and pathological features in three dimensions. This study investigates the role of VR-based 3D imaging as a digital twin for precision surgical planning in infants with CTM. Methods: Fourteen infants who underwent surgical resection for suspected CTM between 09/2020 and 02/2023 were retrospectively analyzed. Pseudonymized CT scans were reconstructed into interactive VR models, serving as digital anatomical twins. Eight surgeons assessed each case using both conventional 2D CT and VR-based reconstructions, separated by a three-month washout period. Outcomes included diagnostic accuracy, and evaluation times. Results: Both VR and conventional imaging achieved 100 % sensitivity in pathology detection. VR enhanced spatial understanding, identifying multilobar involvement (29 % vs. 21 %) and mediastinal shift (36 % vs. 29 %) more frequently. Surgical planning differed significantly, with VR prompting more lobectomy recommendations (57 % vs. 50 %, p = 0.046). VR required longer evaluation times (203.2 ± 117.4 s vs. 124.1 ± 69.7 s, p < 0.001) but demonstrated higher prediction accuracy for the surgical procedure (95 % vs. 84 %). Conclusion: Patient-specific VR reconstructions act as digital twins, enhancing surgical planning precision and providing a personalized anatomical roadmap for CTM management. While time demands remain a limitation, VR as a digital twin aligns with precision medicine and smart hospital paradigms. It also supports less experienced surgeons and fosters interdisciplinary collaboration. Larger multicenter studies should explore its integration into perioperative decision-making and training.