Convex and Concave Model 3D Printing for Designing Right-side Bronchial Blocker for Infants

It is technically challenging for pediatric anesthesiologists to use bronchial blocker (BB) to isolate the lungs of infants during thoracoscopic surgery. Further, BB currently sold in the market cannot match the anatomical characteristics of the infants, especially on the right main bronchus. It may easily cause poor exhaustion of the right upper lobe, which leads to interference with the thoracoscopic surgical field. The two dimensional reconstruction data of 124 normal infants’ airways were extracted from the medical image database of Beijing Children’s Hospital for statistical analysis. After using linear fitting and goodness-of-fit test, a good linear relationship was detected between infant age and various parameters related to aid in designing a new BB for infants (R2 =0.502). According to the growth and development rate of infants, the DICOM files of airway CT scan of 7 infants aged 30, 60, 90, 120, 180, 270, and 360 days were selected to print non-transparent convex and transparent concave 3D models. The non-transparent convex model was precisely measured to obtain the important parameters for BB design infants only, to complete the design of BB, to generate the sample, and to verify the blocking effect of produced sample in transparent concave three-dimensional (3D) model.
1. Guvener O, Eyidogan A, Oto C, et al., 2021, Novel Additive Manufacturing Applications for Communicable Disease Prevention and Control: Focus on Recent COVID-19 Pandemic. Emergent Mater, 4:351–61. https://doi.org/10.1007/s42247-021-00172-y
2. Fillat-Goma F, Coderch-Navarro S, Martinez-Carreres L, et al., 2020, Integrated 3D Printing Solution to Mitigate Shortages of Airway Consumables and Personal Protective Equipment During the COVID-19 Pandemic. BMC Health Serv Res, 20:1035. https://doi.org/10.1186/s12913-020-05891-2
3. Chen J, Chen X, Lv S, et al., 2019, Application of 3D Printing in the Construction of Burr Hole Ring for Deep Brain Stimulation Implants. J Vis Exp, 7:151. https://doi.org/10.3791/59560
4. Rengier F, Mehndiratta A, von Tengg-Kobligk H, et al., 2010, 3D Printing Based on Imaging Data: Review of Medical Applications. Int J Comput Assist Radiol Surg, 5:335–41. https://doi.org/10.1007/s11548-010-0476-x
5. Yoshida H, Hasegawa Y, Matsushima M, et al., 2021, Miniaturization of Respiratory Measurement System in Artificial Ventilator for Small Animal Experiments to Reduce Dead Space and its Application to Lung Elasticity Evaluation. Sensors (Basel), 21:5123. https://doi.org/10.3390/s21155123
6. Yan J, Rufang Z, Rong W, et al., 2020, Extraluminal Placement of the Bronchial Blocker in Infants Undergoing Thoracoscopic Surgery: A Randomized Controlled Study. J Cardiothorac Vasc Anesth, 34:2435–9. https://doi.org/10.1053/j.jvca.2020.02.006
7. Templeton TW, Downard MG, Simpson CR, et al., 2016, Bending the Rules: A Novel Approach to Placement and Retrospective Experience with the 5 French Arndt Endobronchial Blocker in Children <2 Years. Paediatr Anaesth, 26:512–20. https://doi.org/10.1111/pan.12978
8. Abdel-Bary M, Abdel-Naser M, Okasha A, et al., 2020, Clinical and Surgical Aspects of Congenital Lobar Over-inflation: A Single Center Retrospective Study. J Cardiothorac, 15:102. https://doi.org/10.1186/s13019-020-01145-8
9. Luscan R, Leboulanger N, Fayoux P, et al., 2020, Developmental Changes of Upper Airway Dimensions in Children. Paediatr Anaesth, 30:435–45. https://doi.org/10.1111/pan.13832
10. Heydarian M, Noseworthy MD, Kamath MV, et al., 2014, A Morphological Algorithm for Measuring Angle of Airway Branches in Lung CT Images. Crit Rev Biomed Eng, 42:369–81. https://doi.org/10.1615/critrevbiomedeng.2014012135
11. Tanabe N, Oguma T, Sato S, et al., 2018, Quantitative measurement of airway dimensions using ultra-high resolution computed tomography. Respir Investig, 56:489–96. https://doi.org/10.1016/j.resinv.2018.07.008
12. Aboudara C, Nielsen I, Huang JC, et al., 2009, Comparison of Airway Space with Conventional Lateral Headfilms and 3-dimensional Reconstruction from Conebeam Computed Tomography. Am J Orthod Dentofacial Orthop, 135:468–79. https://doi.org/10.1016/j.ajodo.2007.04.043
13. Kramek-Romanowska K, Stecka AM, Zielinski K, et al., 2021, Independent Lung Ventilation-Experimental Studies on a 3D Printed Respiratory Tract Model. Materials (Basel), 14:5189. https://doi.org/10.3390/ma14185189
14. Chi QZ, Mu LZ, He Y, et al., 2021, A Brush-Spin-Coating Method for Fabricating In Vitro Patient-Specific Vascular Models by Coupling 3D-Printing. Cardiovasc Eng Technol, 12:200–14. https://doi.org/10.1007/s13239-020-00504-9
15. Zhang J, Wang T, Li R, et al., 2021, Prediction of Risk Factors of Bronchial Mucus Plugs in Children with Mycoplasma pneumoniae Pneumonia. BMC Infect Dis, 21:67. https://doi.org/10.1186/s12879-021-05765-w
16. Hegde SV, Lensing SY, Greenberg SB, 2015, Determining the Normal Aorta Size in Children. Radiology, 274:859–65. https://doi.org/10.1148/radiol.14140500
17. Moran JL, Solomon PJ, 2007, Statistics in Review Part I: Graphics, Data Summary and Linear Models. Crit Care Resusc, 9:81–90.
18. Tam MD, Laycock SD, Jayne D, et al., 2013, 3-D Printouts of the Tracheobronchial Tree Generated from CT Images as an Aid to Management in a Case of Tracheobronchial Chondromalacia Caused by Relapsing Polychondritis. J Radiol Case Rep, 7:34–43. https://doi.org/10.3941/jrcr.v7i8.1390
19. Salas AA, Carlo WA, Do BT, et al., 2021, Growth Rates of Infants Randomized to Continuous Positive Airway Pressure or Intubation After Extremely Preterm Birth. J Pediatr, 237:148–53.e3.
20. Hann SY, Cui H, Esworthy T, et al., 2021, Dual 3D Printing for Vascularized Bone Tissue Regeneration. Acta Biomater, 123:263–74. https://doi.org/10.1016/j.actbio.2021.01.012
21. L’Alzit FR, Cade R, Naveau A, et al., 2022, Accuracy of Commercial 3D Printers for the Fabrication of Surgical Guides in Dental Implantology. J Dent, 117:103909. https://doi.org/10.1016/j.jdent.2021.103909
22. Di Cicco M, Kantar A, Masini B, et al., 2021, Structural and Functional Development in Airways throughout Childhood: Children are not Small Adults. Pediatr Pulmonol, 56:240–51. https://doi.org/10.1002/ppul.25169
23. Lahiff TJ, Sotutu V, Sarachandran S, et al., 2021, An Infrequent Cause of Neonatal Upper Airway Obstruction: Congenital Nasal Pyriform Aperture Stenosis Presenting to a Remote Facility. Pediatr Investig, 5:244–6. https://doi.org/10.1002/ped4.12269
24. Piras FF, Ferruzzi F, Ferrairo BM, et al., 2021, Correlation between 2D and 3D Measurements of Cement Space in CAD-CAM Crowns. J Prosthet Dent, In press. https://doi.org/10.1016/j.prosdent.2020.08.051
25. van de Bunt F, Pearl ML, van Noort A, 2020, Humeral Retroversion (Complexity of Assigning Reference Axes in 3D and Its Influence on Measurement): A Technical Note. Strategies Trauma Limb Reconstr, 15:69–73. https://doi.org/10.5005/jp-journals-10080-1463
26. Dukov N, Bliznakova K, Okkalidis N, et al., 2022, Thermoplastic 3D Printing Technology Using a Single Filament for Producing Realistic Patient-derived Breast Models. Phys Med Biol, 67:ac4c30. https://doi.org/10.1088/1361-6560/ac4c30
27. Lebowitz C, Massaglia J, Hoffman C, et al., 2021, The Accuracy of 3D Printed Carpal Bones Generated from Cadaveric Specimens. Arch Bone Joint Surg, 9:432–8.
28. Pravdivtseva MS, Peschke E, Lindner T, et al., 2021, 3D-printed, Patient-Specific Intracranial Aneurysm Models: From Clinical Data to Flow Experiments with Endovascular Devices. Med Phys, 48:1469–84. https://doi.org/10.1002/mp.14714
29. Kirby B, Kenkel JM, Zhang AY, et al., 2021, Three-dimensional (3D) Synthetic Printing for the Manufacture of Nonbiodegradable Models, Tools and Implants Used in Surgery: A Review of Current Methods. J Med Eng Technol, 45:14–21. https://doi.org/10.1080/03091902.2020.1838643
30. Wu Y, Jiang X, Wang S, et al., 2015, Grid Multi-category Response Logistic Models. BMC Med Inform Decis Mak, 15:10. https://doi.org/10.1186/s12911-015-0133-y
31. Schorgendorfer A, Branscum AJ, Hanson TE, 2013, A Bayesian Goodness of Fit Test and Semiparametric Generalization of Logistic Regression with Measurement Data. Biometrics, 69:508–19. https://doi.org/10.1111/biom.12007
32. Solari A, le Cessie S, Goeman JJ, 2012, Testing Goodness of fit in Regression: A General Approach for Specified Alternatives. Stat Med, 31:3656–66. https://doi.org/10.1002/sim.5417
33. Masters IB, Zimmerman PV, Chang AB, 2007, Longitudinal Quantification of Growth and Changes in Primary Tracheobronchomalacia Sites in Children. Pediatr Pulmonol, 42:906–13. https://doi.org/10.1002/ppul.20681
34. Senkoylu A, Cetinkaya M, Daldal I, et al., 2020, Personalized Three-Dimensional Printing Pedicle Screw Guide Innovation for the Surgical Management of Patients with Adolescent Idiopathic Scoliosis. World Neurosurg, 144:e513–22. https://doi.org/10.1016/j.wneu.2020.08.212
35. Sugahara K, Katsumi Y, Koyachi M, et al., 2018, Novel Condylar Repositioning Method for 3D-Printed Models. Maxillofac Plast Reconstr Surg, 40:4. https://doi.org/10.1186/s40902-018-0143-7
36. Ross PA, Hammer J, Khemani R, et al., 2010, Pressure-rate Product and Phase Angle as Measures of Acute Inspiratory Upper Airway Obstruction in Rhesus Monkeys. Pediatr Pulmonol, 45:639–44. https://doi.org/10.1002/ppul.21212
37. Lin T, Hu J, Zhang L, et al., 2022, Promoting Enteral Tube Feeding Safety and Performance in Preterm Infants: A Systematic Review. Int J Nurs Stud, 128:104188. https://doi.org/10.1016/j.ijnurstu.2022.104188