Quality assurance of 3D-printed patient specific anatomical models: a systematic review
Abstract Background The responsible use of 3D-printing in medicine includes a context-based quality assurance. Considerable literature has been published in this field, yet the quality of assessment varies widely. The limited discriminatory power of some assessment methods challenges the comparison...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-03-01
|
| Series: | 3D Printing in Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41205-024-00210-5 |
| _version_ | 1797233723765161984 |
|---|---|
| author | Martin Schulze Lukas Juergensen Robert Rischen Max Toennemann Gregor Reischle Jan Puetzler Georg Gosheger Julian Hasselmann |
| author_facet | Martin Schulze Lukas Juergensen Robert Rischen Max Toennemann Gregor Reischle Jan Puetzler Georg Gosheger Julian Hasselmann |
| author_sort | Martin Schulze |
| collection | DOAJ |
| description | Abstract Background The responsible use of 3D-printing in medicine includes a context-based quality assurance. Considerable literature has been published in this field, yet the quality of assessment varies widely. The limited discriminatory power of some assessment methods challenges the comparison of results. The total error for patient specific anatomical models comprises relevant partial errors of the production process: segmentation error (SegE), digital editing error (DEE), printing error (PrE). The present review provides an overview to improve the general understanding of the process specific errors, quantitative analysis, and standardized terminology. Methods This review focuses on literature on quality assurance of patient-specific anatomical models in terms of geometric accuracy published before December 4th, 2022 (n = 139). In an attempt to organize the literature, the publications are assigned to comparable categories and the absolute values of the maximum mean deviation (AMMD) per publication are determined therein. Results The three major examined types of original structures are teeth or jaw (n = 52), skull bones without jaw (n = 17) and heart with coronary arteries (n = 16). VPP (vat photopolymerization) is the most frequently employed basic 3D-printing technology (n = 112 experiments). The median values of AMMD (AMMD: The metric AMMD is defined as the largest linear deviation, based on an average value from at least two individual measurements.) are 0.8 mm for the SegE, 0.26 mm for the PrE and 0.825 mm for the total error. No average values are found for the DEE. Conclusion The total error is not significantly higher than the partial errors which may compensate each other. Consequently SegE, DEE and PrE should be analyzed individually to describe the result quality as their sum according to rules of error propagation. Current methods for quality assurance of the segmentation are often either realistic and accurate or resource efficient. Future research should focus on implementing models for cost effective evaluations with high accuracy and realism. Our system of categorization may be enhancing the understanding of the overall process and a valuable contribution to the structural design and reporting of future experiments. It can be used to educate specialists for risk assessment and process validation within the additive manufacturing industry. Graphical Abstract Context of the figures in this review. Center: Fig. 5+ 7; top (blue): Fig. 8; right (green): Fig. 9; bottom (yellow): Fig. 10; left (red): Fig. 11. A version in high resolution can be found online in the supplementary material. |
| first_indexed | 2024-04-24T16:20:42Z |
| format | Article |
| id | doaj.art-236e191c943c424584d82ad62f49b062 |
| institution | Directory Open Access Journal |
| issn | 2365-6271 |
| language | English |
| last_indexed | 2024-04-24T16:20:42Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | 3D Printing in Medicine |
| spelling | doaj.art-236e191c943c424584d82ad62f49b0622024-03-31T11:10:54ZengBMC3D Printing in Medicine2365-62712024-03-0110112910.1186/s41205-024-00210-5Quality assurance of 3D-printed patient specific anatomical models: a systematic reviewMartin Schulze0Lukas Juergensen1Robert Rischen2Max Toennemann3Gregor Reischle4Jan Puetzler5Georg Gosheger6Julian Hasselmann7Department of General Orthopedics and Tumor Orthopedics, University Hospital MuensterDepartment of General Orthopedics and Tumor Orthopedics, University Hospital MuensterClinic for Radiology, University Hospital MuensterDepartment of General Orthopedics and Tumor Orthopedics, University Hospital MuensterQualified AM GmbHDepartment of General Orthopedics and Tumor Orthopedics, University Hospital MuensterDepartment of General Orthopedics and Tumor Orthopedics, University Hospital MuensterDepartment of General Orthopedics and Tumor Orthopedics, University Hospital MuensterAbstract Background The responsible use of 3D-printing in medicine includes a context-based quality assurance. Considerable literature has been published in this field, yet the quality of assessment varies widely. The limited discriminatory power of some assessment methods challenges the comparison of results. The total error for patient specific anatomical models comprises relevant partial errors of the production process: segmentation error (SegE), digital editing error (DEE), printing error (PrE). The present review provides an overview to improve the general understanding of the process specific errors, quantitative analysis, and standardized terminology. Methods This review focuses on literature on quality assurance of patient-specific anatomical models in terms of geometric accuracy published before December 4th, 2022 (n = 139). In an attempt to organize the literature, the publications are assigned to comparable categories and the absolute values of the maximum mean deviation (AMMD) per publication are determined therein. Results The three major examined types of original structures are teeth or jaw (n = 52), skull bones without jaw (n = 17) and heart with coronary arteries (n = 16). VPP (vat photopolymerization) is the most frequently employed basic 3D-printing technology (n = 112 experiments). The median values of AMMD (AMMD: The metric AMMD is defined as the largest linear deviation, based on an average value from at least two individual measurements.) are 0.8 mm for the SegE, 0.26 mm for the PrE and 0.825 mm for the total error. No average values are found for the DEE. Conclusion The total error is not significantly higher than the partial errors which may compensate each other. Consequently SegE, DEE and PrE should be analyzed individually to describe the result quality as their sum according to rules of error propagation. Current methods for quality assurance of the segmentation are often either realistic and accurate or resource efficient. Future research should focus on implementing models for cost effective evaluations with high accuracy and realism. Our system of categorization may be enhancing the understanding of the overall process and a valuable contribution to the structural design and reporting of future experiments. It can be used to educate specialists for risk assessment and process validation within the additive manufacturing industry. Graphical Abstract Context of the figures in this review. Center: Fig. 5+ 7; top (blue): Fig. 8; right (green): Fig. 9; bottom (yellow): Fig. 10; left (red): Fig. 11. A version in high resolution can be found online in the supplementary material.https://doi.org/10.1186/s41205-024-00210-5Anatomical modelsQuality assuranceMedical additive manufacturing3D-printingSegmentation errorDigital editing error |
| spellingShingle | Martin Schulze Lukas Juergensen Robert Rischen Max Toennemann Gregor Reischle Jan Puetzler Georg Gosheger Julian Hasselmann Quality assurance of 3D-printed patient specific anatomical models: a systematic review 3D Printing in Medicine Anatomical models Quality assurance Medical additive manufacturing 3D-printing Segmentation error Digital editing error |
| title | Quality assurance of 3D-printed patient specific anatomical models: a systematic review |
| title_full | Quality assurance of 3D-printed patient specific anatomical models: a systematic review |
| title_fullStr | Quality assurance of 3D-printed patient specific anatomical models: a systematic review |
| title_full_unstemmed | Quality assurance of 3D-printed patient specific anatomical models: a systematic review |
| title_short | Quality assurance of 3D-printed patient specific anatomical models: a systematic review |
| title_sort | quality assurance of 3d printed patient specific anatomical models a systematic review |
| topic | Anatomical models Quality assurance Medical additive manufacturing 3D-printing Segmentation error Digital editing error |
| url | https://doi.org/10.1186/s41205-024-00210-5 |
| work_keys_str_mv | AT martinschulze qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview AT lukasjuergensen qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview AT robertrischen qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview AT maxtoennemann qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview AT gregorreischle qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview AT janpuetzler qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview AT georggosheger qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview AT julianhasselmann qualityassuranceof3dprintedpatientspecificanatomicalmodelsasystematicreview |