ArticleScientific data2021
Synthetic skull bone defects for automatic patient-specific craniofacial implant design.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 34 citations in OpenAlex.
- A Mandibular Defect Dataset for Autonomous Reconstruction Planning in Oral and Maxillofacial Surgery.Scientific data · 2025Article
- A Comprehensive AI Framework for Superior Diagnosis, Cranial Reconstruction, and Implant Generation for Diverse Cranial Defects.Bioengineering (Basel, Switzerland) · 2025Article
- Sex classification of 3D skull images using deep neural networks.Scientific reports · 2024Article
- Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.Journal of medical systems · 2024Article
- 3D Printing Applications for Craniomaxillofacial Reconstruction: A Sweeping Review.ACS biomaterials science & engineering · 2023Review
- Bespoke Implants for Cranial Reconstructions: Preoperative to Postoperative Surgery Management System.Bioengineering (Basel, Switzerland) · 2023Article
- The simplified tailor-made workflows for a 3D slicer-based craniofacial implant design.Scientific reports · 2023Article
- Creating high-resolution 3D cranial implant geometry using deep learning techniques.Frontiers in bioengineering and biotechnology · 2023Article
- Functional Hydrogels and Their Applications in Craniomaxillofacial Bone Regeneration.Pharmaceutics · 2022Review
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
- Tissue Engineered Neurovascularization Strategies for Craniofacial Tissue Regeneration.ACS applied bio materials · 2022Review
- Article
- The 3D skull 0-4 years: A validated, generative, statistical shape model.Bone reports · 2021Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Patient-specific craniofacial implants are used to repair skull bone defects after trauma or surgery. Currently, cranial implants are designed and produced by third-party suppliers, which is usually time-consuming and expensive. Recent advances in additive manufacturing made the in-hospital or in-operation-room fabrication of personalized implants feasible. However, the implants are still manufactured by external companies. To facilitate an optimized workflow, fast and automatic implant manufacturing is highly desirable. Data-driven approaches, such as deep learning, show currently great potential towards automatic implant design. However, a considerable amount of data is needed to train such algorithms, which is, especially in the medical domain, often a bottleneck. Therefore, we present CT-imaging data of the craniofacial complex from 24 patients, in which we injected various artificial cranial defects, resulting in 240 data pairs and 240 corresponding implants. Based on this work, automatic implant design and manufacturing processes can be trained. Additionally, the data of this work build a solid base for researchers to work on automatic cranial implant designs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.