1
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Noel L, Fat SC, Causey JL, Dong W, Stubblefield J, Szymanski K, Chang JH, Wang PZ, Moore JH, Ray E, Huang X. Sex classification of 3D skull images using deep neural networks. Sci Rep 2024; 14:13707. [PMID: 38877045 PMCID: PMC11178899 DOI: 10.1038/s41598-024-61879-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 05/10/2024] [Indexed: 06/16/2024] Open
Abstract
Determining the fundamental characteristics that define a face as "feminine" or "masculine" has long fascinated anatomists and plastic surgeons, particularly those involved in aesthetic and gender-affirming surgery. Previous studies in this area have relied on manual measurements, comparative anatomy, and heuristic landmark-based feature extraction. In this study, we collected retrospectively at Cedars Sinai Medical Center (CSMC) a dataset of 98 skull samples, which is the first dataset of this kind of 3D medical imaging. We then evaluated the accuracy of multiple deep learning neural network architectures on sex classification with this dataset. Specifically, we evaluated methods representing three different 3D data modeling approaches: Resnet3D, PointNet++, and MeshNet. Despite the limited number of imaging samples, our testing results show that all three approaches achieve AUC scores above 0.9 after convergence. PointNet++ exhibits the highest accuracy, while MeshNet has the lowest. Our findings suggest that accuracy is not solely dependent on the sparsity of data representation but also on the architecture design, with MeshNet's lower accuracy likely due to the lack of a hierarchical structure for progressive data abstraction. Furthermore, we studied a problem related to sex determination, which is the analysis of the various morphological features that affect sex classification. We proposed and developed a new method based on morphological gradients to visualize features that influence model decision making. The method based on morphological gradients is an alternative to the standard saliency map, and the new method provides better visualization of feature importance. Our study is the first to develop and evaluate deep learning models for analyzing 3D facial skull images to identify imaging feature differences between individuals assigned male or female at birth. These findings may be useful for planning and evaluating craniofacial surgery, particularly gender-affirming procedures, such as facial feminization surgery.
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Affiliation(s)
- Lake Noel
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Shelby Chun Fat
- Department of Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Jason L Causey
- Center for No-Boundary Thinking (CNBT), Arkansas State University, Jonesboro, AR, USA
- Department of Computer Science, Arkansas State University, Jonesboro, AR, USA
| | - Wei Dong
- Ann Arbor Algorithms, Ann Arbor, MI, USA
| | - Jonathan Stubblefield
- Center for No-Boundary Thinking (CNBT), Arkansas State University, Jonesboro, AR, USA
- Department of Computer Science, Arkansas State University, Jonesboro, AR, USA
| | | | - Jui-Hsuan Chang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Zhiping Wang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA.
| | - Edward Ray
- Department of Surgery, Cedars Sinai Medical Center, Los Angeles, CA, USA.
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA.
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2
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Li J, Ellis DG, Pepe A, Gsaxner C, Aizenberg MR, Kleesiek J, Egger J. Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model. J Med Syst 2024; 48:55. [PMID: 38780820 PMCID: PMC11116219 DOI: 10.1007/s10916-024-02066-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 04/11/2024] [Indexed: 05/25/2024]
Abstract
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .
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Affiliation(s)
- Jianning Li
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany.
| | - David G Ellis
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria
| | - Michele R Aizenberg
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Jens Kleesiek
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany
| | - Jan Egger
- Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital, Girardetstraße 2, 45131, Essen, Germany.
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3
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Kelly SS, Suarez CA, Mirsky NA, Slavin BV, Brochu B, Vivekanand Nayak V, El Shatanofy M, Witek L, Thaller SR, Coelho PG. Application of 3D Printing in Cleft Lip and Palate Repair. J Craniofac Surg 2024:00001665-990000000-01572. [PMID: 38738906 DOI: 10.1097/scs.0000000000010294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 04/03/2024] [Indexed: 05/14/2024] Open
Abstract
This manuscript reviews the transformative impact of 3-dimensional (3D) printing technologies in the treatment and management of cleft lip and palate (CLP), highlighting its application across presurgical planning, surgical training, implantable scaffolds, and postoperative care. By integrating patient-specific data through computer-aided design and manufacturing, 3D printing offers tailored solutions that improve surgical outcomes, reduce operation times, and enhance patient care. The review synthesizes current research findings, technical advancements, and clinical applications, illustrating the potential of 3D printing to revolutionize CLP treatment. Further, it discusses the future directions of combining 3D printing with other innovative technologies like artificial intelligence, 4D printing, and in situ bioprinting for more comprehensive care strategies. This paper underscores the necessity for multidisciplinary collaboration and further research to overcome existing challenges and fully utilize the capabilities of 3D printing in CLP repair.
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Affiliation(s)
- Sophie S Kelly
- Florida Atlantic University Charles E. Schmidt College of Medicine, Boca Raton, FL
| | | | | | | | | | | | - Muhammad El Shatanofy
- Department of Otolaryngology, University of Miami Miller School of Medicine, Miami, FL
| | - Lukasz Witek
- Biomaterials Division, NYU Dentistry
- Hansjörg Wyss Department of Plastic Surgery, NYU Grossman School of Medicine, New York
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, Brooklyn, NY
| | - Seth R Thaller
- DeWitt Daughtry Family, Division of Plastic & Reconstructive Surgery, Department of Surgery, University of Miami Miller School of Medicine, Miami, FL
| | - Paulo G Coelho
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine
- DeWitt Daughtry Family, Division of Plastic & Reconstructive Surgery, Department of Surgery, University of Miami Miller School of Medicine, Miami, FL
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4
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Duteille F, Chavoin JP, Leyx P, Samarut E. Interest of a 3D custom-made implant in the reconstruction of bone defects of the cranial vault. ANN CHIR PLAST ESTH 2024; 69:160-165. [PMID: 37516637 DOI: 10.1016/j.anplas.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 07/31/2023]
Abstract
The authors report a case of a patient managed for severe cranial vault depression following combined neurosurgery and radiotherapy. This situation caused major aesthetic discomfort and was potentially dangerous due to the mechanical weakness of the bone flap. The authors had a CAD (computer aided design) silicone elastomer custom-made implant made to fill perfectly the depression. Beforehand, an expansion was performed to cover the implant after removal of the radiated skin. The surgery and post-operative course raised no concerns. After one year of follow-up, the result is very good and the patient very satisfied, proving that this technique certainly has its place in the therapeutic arsenal when faced with a tissue defect of the cranial vault.
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Affiliation(s)
- F Duteille
- Department of Plastic, Reconstructive and Aesthetic Surgery, Nantes University Hospital, 44093 Nantes, France.
| | - J P Chavoin
- Department of Plastic, Reconstructive and Aesthetic Surgery, Toulouse University Hospital, Toulouse, France
| | - P Leyx
- Anatomik modeling SAS, Engineer, Paris, France
| | - E Samarut
- Neurotraumatology and Neurosurgery Department, Nantes University Hospital, Nantes, France
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5
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Wong KI, Zhong Y, Yu Z, Jiang T, Wei M. Combining Patient-specific Implant With Malar Reduction to Repair Mid-facial Asymmetry Caused by Craniofacial Fractures in Asians. J Craniofac Surg 2024; 35:241-242. [PMID: 37643059 DOI: 10.1097/scs.0000000000009661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 06/04/2023] [Indexed: 08/31/2023] Open
Abstract
Mid-facial asymmetry caused by bone defect or deformation resulted from craniofacial fracture was a common secondary complication needed to repair. Patient-specific implant (PSI) designed with the unaffected side as a template is a good choice to repair this kind of facial asymmetry. However, in Asians, the broad and prominent zygomatic bone in unaffected side is not an optimal template, because the oval facial shape was considered as a more attractive appearance in Asian esthetic concept. To repair the mid-facial asymmetry and to improve the facial contour, the authors combined PSI implantation with malar reduction in one-stage surgery. The authors referred the facial proportion index (the optimal ratio of mid and lower face was 1.27) as a basis for preoperative precise design to determine the ideal facial shape of unaffected side, and used mirror image overlay technique with the ideal shape of unaffected side as a template to design the PSI. With this surgical strategy, patients not only can repair facial asymmetry but also can get a more attractive appearance.
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Affiliation(s)
- Ka Ioi Wong
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai
| | - Yehong Zhong
- Department of Maxillofacial Surgery and Digital Plastic Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China
| | - Zheyuan Yu
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai
| | - Taoran Jiang
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai
| | - Min Wei
- Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai
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6
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Slavin BV, Ehlen QT, Costello JP, Nayak VV, Bonfante EA, Benalcázar Jalkh EB, Runyan CM, Witek L, Coelho PG. 3D Printing Applications for Craniomaxillofacial Reconstruction: A Sweeping Review. ACS Biomater Sci Eng 2023; 9:6586-6609. [PMID: 37982644 PMCID: PMC11229092 DOI: 10.1021/acsbiomaterials.3c01171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
The field of craniomaxillofacial (CMF) surgery is rich in pathological diversity and broad in the ages that it treats. Moreover, the CMF skeleton is a complex confluence of sensory organs and hard and soft tissue with load-bearing demands that can change within millimeters. Computer-aided design (CAD) and additive manufacturing (AM) create extraordinary opportunities to repair the infinite array of craniomaxillofacial defects that exist because of the aforementioned circumstances. 3D printed scaffolds have the potential to serve as a comparable if not superior alternative to the "gold standard" autologous graft. In vitro and in vivo studies continue to investigate the optimal 3D printed scaffold design and composition to foster bone regeneration that is suited to the unique biological and mechanical environment of each CMF defect. Furthermore, 3D printed fixation devices serve as a patient-specific alternative to those that are available off-the-shelf with an opportunity to reduce operative time and optimize fit. Similar benefits have been found to apply to 3D printed anatomical models and surgical guides for preoperative or intraoperative use. Creation and implementation of these devices requires extensive preclinical and clinical research, novel manufacturing capabilities, and strict regulatory oversight. Researchers, manufacturers, CMF surgeons, and the United States Food and Drug Administration (FDA) are working in tandem to further the development of such technology within their respective domains, all with a mutual goal to deliver safe, effective, cost-efficient, and patient-specific CMF care. This manuscript reviews FDA regulatory status, 3D printing techniques, biomaterials, and sterilization procedures suitable for 3D printed devices of the craniomaxillofacial skeleton. It also seeks to discuss recent clinical applications, economic feasibility, and future directions of this novel technology. By reviewing the current state of 3D printing in CMF surgery, we hope to gain a better understanding of its impact and in turn identify opportunities to further the development of patient-specific surgical care.
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Affiliation(s)
- Blaire V Slavin
- University of Miami Miller School of Medicine, 1011 NW 15th St., Miami, Florida 33136, United States
| | - Quinn T Ehlen
- University of Miami Miller School of Medicine, 1011 NW 15th St., Miami, Florida 33136, United States
| | - Joseph P Costello
- University of Miami Miller School of Medicine, 1011 NW 15th St., Miami, Florida 33136, United States
| | - Vasudev Vivekanand Nayak
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1011 NW 15th St., Miami, Florida 33136, United States
| | - Estavam A Bonfante
- Department of Prosthodontics and Periodontology, University of Sao Paulo, Bauru School of Dentistry, Alameda Dr. Octávio Pinheiro Brisolla, Quadra 9 - Jardim Brasil, Bauru São Paulo 17012-901, Brazil
| | - Ernesto B Benalcázar Jalkh
- Department of Prosthodontics and Periodontology, University of Sao Paulo, Bauru School of Dentistry, Alameda Dr. Octávio Pinheiro Brisolla, Quadra 9 - Jardim Brasil, Bauru São Paulo 17012-901, Brazil
| | - Christopher M Runyan
- Department of Plastic and Reconstructive Surgery, Wake Forest School of Medicine, 475 Vine St, Winston-Salem, North Carolina 27101, United States
| | - Lukasz Witek
- Biomaterials Division, NYU Dentistry, 345 E. 24th St., New York, New York 10010, United States
- Hansjörg Wyss Department of Plastic Surgery, NYU Grossman School of Medicine, New York University, 222 E 41st St., New York, New York 10017, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, 6 MetroTech Center, Brooklyn, New York 11201, United States
| | - Paulo G Coelho
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, 1011 NW 15th St., Miami, Florida 33136, United States
- DeWitt Daughtry Family Department of Surgery, Division of Plastic Surgery, University of Miami Miller School of Medicine, 1120 NW 14th St., Miami, Florida 33136, United States
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7
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Wu CT, Yang YH, Chang YZ. Creating high-resolution 3D cranial implant geometry using deep learning techniques. Front Bioeng Biotechnol 2023; 11:1297933. [PMID: 38149174 PMCID: PMC10750412 DOI: 10.3389/fbioe.2023.1297933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/22/2023] [Indexed: 12/28/2023] Open
Abstract
Creating a personalized implant for cranioplasty can be costly and aesthetically challenging, particularly for comminuted fractures that affect a wide area. Despite significant advances in deep learning techniques for 2D image completion, generating a 3D shape inpainting remains challenging due to the higher dimensionality and computational demands for 3D skull models. Here, we present a practical deep-learning approach to generate implant geometry from defective 3D skull models created from CT scans. Our proposed 3D reconstruction system comprises two neural networks that produce high-quality implant models suitable for clinical use while reducing training time. The first network repairs low-resolution defective models, while the second network enhances the volumetric resolution of the repaired model. We have tested our method in simulations and real-life surgical practices, producing implants that fit naturally and precisely match defect boundaries, particularly for skull defects above the Frankfort horizontal plane.
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Affiliation(s)
- Chieh-Tsai Wu
- Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | | | - Yau-Zen Chang
- Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Mechanical Engineering, Chang Gung University, Taoyuan, Taiwan
- Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
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8
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Li J, Ellis DG, Kodym O, Rauschenbach L, Rieß C, Sure U, Wrede KH, Alvarez CM, Wodzinski M, Daniol M, Hemmerling D, Mahdi H, Clement A, Kim E, Fishman Z, Whyne CM, Mainprize JG, Hardisty MR, Pathak S, Sindhura C, Gorthi RKSS, Kiran DV, Gorthi S, Yang B, Fang K, Li X, Kroviakov A, Yu L, Jin Y, Pepe A, Gsaxner C, Herout A, Alves V, Španěl M, Aizenberg MR, Kleesiek J, Egger J. Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge. Med Image Anal 2023; 88:102865. [PMID: 37331241 DOI: 10.1016/j.media.2023.102865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/23/2023] [Accepted: 06/02/2023] [Indexed: 06/20/2023]
Abstract
Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.
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Affiliation(s)
- Jianning Li
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria.
| | - David G Ellis
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Oldřich Kodym
- Graph@FIT, Brno University of Technology, Brno, Czech Republic
| | - Laurèl Rauschenbach
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Christoph Rieß
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Ulrich Sure
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Karsten H Wrede
- Department of Neurosurgery and Spine Surgery, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Carlos M Alvarez
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Marek Wodzinski
- AGH University of Science and Technology, Department of Measurement and Electronics, Krakow, Poland; University of Applied Sciences Western Switzerland (HES-SO Valais), Information Systems Institute, Sierre, Switzerland
| | - Mateusz Daniol
- AGH University of Science and Technology, Department of Measurement and Electronics, Krakow, Poland
| | - Daria Hemmerling
- AGH University of Science and Technology, Department of Measurement and Electronics, Krakow, Poland
| | - Hamza Mahdi
- Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Evan Kim
- Sunnybrook Research Institute, Toronto, ON, Canada
| | | | - Cari M Whyne
- Sunnybrook Research Institute, Toronto, ON, Canada; Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, M5T 1P5, Canada
| | - James G Mainprize
- Sunnybrook Research Institute, Toronto, ON, Canada; Calavera Surgical Design Inc., Toronto, ON, Canada
| | - Michael R Hardisty
- Sunnybrook Research Institute, Toronto, ON, Canada; Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, M5T 1P5, Canada
| | - Shashwat Pathak
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | - Chitimireddy Sindhura
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | | | - Degala Venkata Kiran
- Department of Mechanical Engineering, Indian Institute of Technology, Tirupati, India
| | - Subrahmanyam Gorthi
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | - Bokai Yang
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Ke Fang
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Xingyu Li
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Artem Kroviakov
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
| | - Lei Yu
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
| | - Yuan Jin
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Antonio Pepe
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Christina Gsaxner
- Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria
| | - Adam Herout
- Graph@FIT, Brno University of Technology, Brno, Czech Republic
| | - Victor Alves
- ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal
| | | | - Michele R Aizenberg
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany
| | - Jan Egger
- Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria; Computer Algorithms for Medicine Laboratory, Graz, Austria.
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9
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Ulmeanu ME, Mateș IM, Doicin CV, Mitrică M, Chirteș VA, Ciobotaru G, Semenescu A. Bespoke Implants for Cranial Reconstructions: Preoperative to Postoperative Surgery Management System. Bioengineering (Basel) 2023; 10:bioengineering10050544. [PMID: 37237614 DOI: 10.3390/bioengineering10050544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/28/2023] Open
Abstract
Traumatic brain injury is a leading cause of death and disability worldwide, with nearly 90% of the deaths coming from low- and middle-income countries. Severe cases of brain injury often require a craniectomy, succeeded by cranioplasty surgery to restore the integrity of the skull for both cerebral protection and cosmetic purposes. The current paper proposes a study on developing and implementing an integrative surgery management system for cranial reconstructions using bespoke implants as an accessible and cost-effective solution. Bespoke cranial implants were designed for three patients and subsequent cranioplasties were performed. Overall dimensional accuracy was evaluated on all three axes and surface roughness was measured with a minimum value of 2.209 μm for Ra on the convex and concave surfaces of the 3D-printed prototype implants. Improvements in patient compliance and quality of life were reported in postoperative evaluations of all patients involved in the study. No complications were registered from both short-term and long-term monitoring. Material and processing costs were lower compared to a metal 3D-printed implants through the usage of readily available tools and materials, such as standardized and regulated bone cement materials, for the manufacturing of the final bespoke cranial implants. Intraoperative times were reduced through the pre-planning management stages, leading to a better implant fit and overall patient satisfaction.
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Affiliation(s)
- Mihaela-Elena Ulmeanu
- Faculty of Industrial Engineering and Robotics, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania
| | - Ileana Mariana Mateș
- Central Military Emergency University Hospital "Dr. Carol Davila", 010825 Bucharest, Romania
| | - Cristian-Vasile Doicin
- Faculty of Industrial Engineering and Robotics, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania
| | - Marian Mitrică
- Central Military Emergency University Hospital "Dr. Carol Davila", 010825 Bucharest, Romania
| | - Vasile Alin Chirteș
- Central Military Emergency University Hospital "Dr. Carol Davila", 010825 Bucharest, Romania
| | - Georgian Ciobotaru
- Central Military Emergency University Hospital "Dr. Carol Davila", 010825 Bucharest, Romania
| | - Augustin Semenescu
- Faculty of Materials Science and Engineering, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania
- Academy of Romanian Scientists, 3 Ilfov St., 050044 Bucharest, Romania
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10
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Nadian MH, Farmani S, Ghazizadeh A. A novel methodology for exact targeting of human and non-human primate brain structures and skull implants using atlas-based 3D reconstruction. J Neurosci Methods 2023; 391:109851. [PMID: 37028519 DOI: 10.1016/j.jneumeth.2023.109851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/18/2023] [Accepted: 04/03/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Accurate targeting of brain areas for stimulation and/or electrophysiological recording is key in many therapeutic applications and basic neuroscience research. Nevertheless, there are currently no end-to-end packages that accommodate all steps required for exact localization, visualization and targeting regions of interest (ROIs) using standard atlases and for designing skull implants. NEW METHOD We have implemented a new processing pipeline that addresses this issue in macaques and humans including various preprocessing, registration, warping procedures and 3D reconstructions and provide a noncommercial open-source graphical software which we refer to as the MATLAB-based reconstruction for recording and stimulation (MATres). RESULTS The results of skull stripping were shown to work seamlessly in humans and monkeys. Linear and nonlinear warping of the standard atlas to the native space outperformed state-of-the-art using AFNI with improvements being more prominent in humans which had a more complex gyration geometry. The skull surface extracted by MATres using MRI images had more than 90% match with CT ground truth and could be used to design skull implants that conformed well to the skull's local curvature. COMPARISON WITH EXISTING METHOD(S) The accuracy of the various steps including skull stripping, standard atlas registration and skull reconstruction in MATres was compared with and shown to outperform the AFNI. The localization accuracy of the recording chambers designed with MATres and implanted in two macaque monkeys was further confirmed using MRI imaging. CONCLUSIONS Precise localization of ROIs offered by MATres can be used for planning electrode penetrations for recording and for shallow or deep brain stimulation (DBS).
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Affiliation(s)
- Mohammad Hossein Nadian
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Sepideh Farmani
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Ali Ghazizadeh
- Bio-intelligence Research Unit, Sharif Brain Center, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran.
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11
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Sivakumar NK, Palaniyappan S, Sekar V, Alodhayb A, Braim M. An optimization approach for studying the effect of lattice unit cell's design-based factors on additively manufactured poly methyl methacrylate cranio-implant. J Mech Behav Biomed Mater 2023; 141:105791. [PMID: 37004304 DOI: 10.1016/j.jmbbm.2023.105791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/15/2023] [Accepted: 03/19/2023] [Indexed: 03/28/2023]
Abstract
In craniomaxillofacial surgery the inclusion of lattice structure on the Cranio-implants for the surgical procedure of cranial defects is difficult. Additive manufacturing open ups a huge space for the development of intricate profiles for complex surgical practices. Designing lattice structures with various design topologies has gained more interest in the medical community for reducing the weight of the implants in the cranial region. This research proposes the mimicking of cranial defective portion concerning bone-like porous structure by means of Poly methyl methacrylate (PMMA) material via 3D printing technology. The experiments were optimized by incorporating square-type porous lattice structure in the development of cranial implants. The design-based factors of the unit cell were enhanced with the aid of the Design of experiments (DOE) technique. L9 orthogonal array is developed by incorporating various design-based factors of the lattice unit cell like unit cell size (mm), skewing angle (°), wall thickness (mm), and unit cell orientation (°). The experiments are optimized with respect to obtaining better compressive strength and compressive strength/density of the prepared lattice structure incorporated polymeric samples. The result shows that for obtaining the maximum compressive strength in the porous square lattice-structured PMMA compression samples will be a lower cell size of 2 mm, a higher skewing angle of 30°, a higher wall thickness of 1 mm, and a unit cell orientation of 90°. The experimental optimized condition results of the design-based factors achieve the maximum compressive strength and compressive strength/density of 83.37 MPa and 189.73 MPa/g mm-3. The lattice structure orientated with 90° has a significant contribution towards reducing the development of structural deviations of incorporating square lattice structure on the PMMA polymeric material. Therefore, the topologically modified square lattice structure incorporated 3D printed PMMA material has a potential scope for the replacement of conventional maxillofacial cranial implants.
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12
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Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning. Clin Oral Investig 2023; 27:897-906. [PMID: 36323803 DOI: 10.1007/s00784-022-04706-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/29/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The aim of this review was to investigate the application of artificial intelligence (AI) in maxillofacial computer-assisted surgical planning (CASP) workflows with the discussion of limitations and possible future directions. MATERIALS AND METHODS An in-depth search of the literature was undertaken to review articles concerned with the application of AI for segmentation, multimodal image registration, virtual surgical planning (VSP), and three-dimensional (3D) printing steps of the maxillofacial CASP workflows. RESULTS The existing AI models were trained to address individual steps of CASP, and no single intelligent workflow was found encompassing all steps of the planning process. Segmentation of dentomaxillofacial tissue from computed tomography (CT)/cone-beam CT imaging was the most commonly explored area which could be applicable in a clinical setting. Nevertheless, a lack of generalizability was the main issue, as the majority of models were trained with the data derived from a single device and imaging protocol which might not offer similar performance when considering other devices. In relation to registration, VSP and 3D printing, the presence of inadequate heterogeneous data limits the automatization of these tasks. CONCLUSION The synergy between AI and CASP workflows has the potential to improve the planning precision and efficacy. However, there is a need for future studies with big data before the emergent technology finds application in a real clinical setting. CLINICAL RELEVANCE The implementation of AI models in maxillofacial CASP workflows could minimize a surgeon's workload and increase efficiency and consistency of the planning process, meanwhile enhancing the patient-specific predictability.
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Tantisatirapong S, Khunakornpattanakarn S, Suesatsakul T, Boonpratatong A, Benjamin I, Tongmeesee S, Kangkorn T, Chanwimalueang T. The simplified tailor-made workflows for a 3D slicer-based craniofacial implant design. Sci Rep 2023; 13:2850. [PMID: 36801943 PMCID: PMC9938178 DOI: 10.1038/s41598-023-30117-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 02/15/2023] [Indexed: 02/19/2023] Open
Abstract
A specific design of craniofacial implant model is vital and urgent for patients with traumatic head injury. The mirror technique is commonly used for modeling these implants, but it requires the presence of a healthy skull region opposite to the defect. To address this limitation, we propose three processing workflows for modeling craniofacial implants: the mirror method, the baffle planner, and the baffle-based mirror guideline. These workflows are based on extension modules on the 3D Slicer platform and were developed to simplify the modeling process for a variety of craniofacial scenarios. To evaluate the effectiveness of these proposed workflows, we investigated craniofacial CT datasets collected from four accidental cases. The designed implant models were created using the three proposed workflows and compared to reference models created by an experienced neurosurgeon. The spatial properties of the models were evaluated using performance metrics. Our results show that the mirror method is suitable for cases where a healthy skull region can be completely reflected to the defect region. The baffle planner module offers a flexible prototype model that can be fit independently to any defect location, but it requires customized refinement of contour and thickness to fill the missing region seamlessly and relies on the user's experience and expertise. The proposed baffle-based mirror guideline method strengthens the baffle planner method by tracing the mirrored surface. Overall, our study suggests that the three proposed workflows for craniofacial implant modeling simplify the process and can be practically applied to a variety of craniofacial scenarios. These findings have the potential to improve the care of patients with traumatic head injuries and could be used by neurosurgeons and other medical professionals.
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Affiliation(s)
- Suchada Tantisatirapong
- Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand
| | | | - Thanyakarn Suesatsakul
- Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand
| | - Amaraporn Boonpratatong
- Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand
| | - Itsara Benjamin
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Chonburi Hospital, Chonburi, 20000, Thailand
| | - Somprasong Tongmeesee
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Chonburi Hospital, Chonburi, 20000, Thailand
| | - Tanasit Kangkorn
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Chonburi Hospital, Chonburi, 20000, Thailand
| | - Theerasak Chanwimalueang
- Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand.
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14
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Portnoy Y, Koren J, Khoury A, Factor S, Dadia S, Ran Y, Benady A. Three-dimensional technologies in presurgical planning of bone surgeries: current evidence and future perspectives. Int J Surg 2023; 109:3-10. [PMID: 36799780 PMCID: PMC10389328 DOI: 10.1097/js9.0000000000000201] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/20/2022] [Indexed: 02/18/2023]
Abstract
BACKGROUND The recent development of three-dimensional (3D) technologies introduces a novel set of opportunities to the medical field in general, and specifically to surgery. The preoperative phase has proven to be a critical factor in surgical success. Utilization of 3D technologies has the potential to improve preoperative planning and overall surgical outcomes. In this narrative review article, the authors describe existing clinical data pertaining to the current use of 3D printing, virtual reality, and augmented reality in the preoperative phase of bone surgery. METHODS The methodology included keyword-based literature search in PubMed and Google Scholar for original articles published between 2014 and 2022. After excluding studies performed in nonbone surgery disciplines, data from 61 studies of five different surgical disciplines were processed to be included in this narrative review. RESULTS Among the mentioned technologies, 3D printing is currently the most advanced in terms of clinical use, predominantly creating anatomical models and patient-specific instruments that provide high-quality operative preparation. Virtual reality allows to set a surgical plan and to further simulate the procedure via a 2D screen or head mounted display. Augmented reality is found to be useful for surgical simulation upon 3D printed anatomical models or virtual phantoms. CONCLUSIONS Overall, 3D technologies are gradually becoming an integral part of a surgeon's preoperative toolbox, allowing for increased surgical accuracy and reduction of operation time, mainly in complex and unique surgical cases. This may eventually lead to improved surgical outcomes, thereby optimizing the personalized surgical approach.
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Affiliation(s)
- Yotam Portnoy
- First Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Jonathan Koren
- First Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Amal Khoury
- Sackler School of Medicine, Tel Aviv University
- Division of Orthopaedic Surgery
| | - Shai Factor
- Sackler School of Medicine, Tel Aviv University
- Division of Orthopaedic Surgery
| | - Solomon Dadia
- Sackler School of Medicine, Tel Aviv University
- Levin Center of 3D Printing and Surgical Innovation
- National Unit of Orthopedic Oncology
| | - Yuval Ran
- Sackler School of Medicine, Tel Aviv University
- Office of the Deputy Medical Manager, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Amit Benady
- Sackler School of Medicine, Tel Aviv University
- Division of Orthopaedic Surgery
- Levin Center of 3D Printing and Surgical Innovation
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15
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Yu Y, Yu T, Wang X, Liu D. Functional Hydrogels and Their Applications in Craniomaxillofacial Bone Regeneration. Pharmaceutics 2022; 15:pharmaceutics15010150. [PMID: 36678779 PMCID: PMC9864650 DOI: 10.3390/pharmaceutics15010150] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/26/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
Craniomaxillofacial bone defects are characterized by an irregular shape, bacterial and inflammatory environment, aesthetic requirements, and the need for the functional recovery of oral-maxillofacial areas. Conventional clinical treatments are currently unable to achieve high-quality craniomaxillofacial bone regeneration. Hydrogels are a class of multifunctional platforms made of polymers cross-linked with high water content, good biocompatibility, and adjustable physicochemical properties for the intelligent delivery of goods. These characteristics make hydrogel systems a bright prospect for clinical applications in craniomaxillofacial bone. In this review, we briefly demonstrate the properties of hydrogel systems that can come into effect in the field of bone regeneration. In addition, we summarize the hydrogel systems that have been developed for craniomaxillofacial bone regeneration in recent years. Finally, we also discuss the prospects in the field of craniomaxillofacial bone tissue engineering; these discussions can serve as an inspiration for future hydrogel design.
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Affiliation(s)
- Yi Yu
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing 100081, China
- Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - Tingting Yu
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing 100081, China
- Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | - Xing Wang
- Beijing National Laboratory for Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
- Correspondence: (X.W.); (D.L.)
| | - Dawei Liu
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing 100081, China
- Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
- Correspondence: (X.W.); (D.L.)
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16
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Thimukonda Jegadeesan J, Baldia M, Basu B. Next-generation personalized cranioplasty treatment. Acta Biomater 2022; 154:63-82. [PMID: 36272686 DOI: 10.1016/j.actbio.2022.10.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 12/14/2022]
Abstract
Decompressive craniectomy (DC) is a surgical procedure, that is followed by cranioplasty surgery. DC is usually performed to treat patients with traumatic brain injury, intracranial hemorrhage, cerebral infarction, brain edema, skull fractures, etc. In many published clinical case studies and systematic reviews, cranioplasty surgery is reported to restore cranial symmetry with good cosmetic outcomes and neurophysiologically relevant functional outcomes in hundreds of patients. In this review article, we present a number of key issues related to the manufacturing of patient-specific implants, clinical complications, cosmetic outcomes, and newer alternative therapies. While discussing alternative therapeutic treatments for cranioplasty, biomolecules and cellular-based approaches have been emphasized. The current clinical practices in the restoration of cranial defects involve 3D printing to produce patient-specific prefabricated cranial implants, that provide better cosmetic outcomes. Regardless of the advancements in image processing and 3D printing, the complete clinical procedure is time-consuming and requires significant costs. To reduce manual intervention and to address unmet clinical demands, it has been highlighted that automated implant fabrication by data-driven methods can accelerate the design and manufacturing of patient-specific cranial implants. The data-driven approaches, encompassing artificial intelligence (machine learning/deep learning) and E-platforms, such as publicly accessible clinical databases will lead to the development of the next generation of patient-specific cranial implants, which can provide predictable clinical outcomes. STATEMENT OF SIGNIFICANCE: Cranioplasty is performed to reconstruct cranial defects of patients who have undergone decompressive craniectomy. Cranioplasty surgery improves the aesthetic and functional outcomes of those patients. To meet the clinical demands of cranioplasty surgery, accelerated designing and manufacturing of 3D cranial implants are required. This review provides an overview of biomaterial implants and bone flap manufacturing methods for cranioplasty surgery. In addition, tissue engineering and regenerative medicine-based approaches to reduce clinical complications are also highlighted. The potential use of data-driven computer applications and data-driven artificial intelligence-based approaches are emphasized to accelerate the clinical protocols of cranioplasty treatment with less manual intervention and shorter intraoperative time.
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Affiliation(s)
| | - Manish Baldia
- Department of Neurosurgery, Jaslok Hospital and Research Centre, Mumbai, Maharashtra 400026, India
| | - Bikramjit Basu
- Materials Research Centre, Indian Institute of Science, CV Raman Road, Bangalore, Karnataka 560012, India; Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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17
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Busnatu Ș, Niculescu AG, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med 2022; 11:jcm11082265. [PMID: 35456357 PMCID: PMC9031863 DOI: 10.3390/jcm11082265] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.
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Affiliation(s)
- Ștefan Busnatu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Adelina-Gabriela Niculescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
| | - Alexandra Bolocan
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - George E. D. Petrescu
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Dan Nicolae Păduraru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Iulian Năstasă
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Mircea Lupușoru
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Marius Geantă
- Centre for Innovation in Medicine, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania;
| | - Octavian Andronic
- “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (Ș.B.); (A.B.); (G.E.D.P.); (D.N.P.); (I.N.); (M.L.); (O.A.)
| | - Alexandru Mihai Grumezescu
- Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, Politehnica University of Bucharest, 011061 Bucharest, Romania;
- Research Institute of the University of Bucharest—ICUB, University of Bucharest, 050657 Bucharest, Romania
- Academy of Romanian Scientists, Ilfov No. 3, 50044 Bucharest, Romania
- Correspondence:
| | - Henrique Martins
- Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilha, Portugal;
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18
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Li Y, Fraser D, Mereness J, Van Hove A, Basu S, Newman M, Benoit DSW. Tissue Engineered Neurovascularization Strategies for Craniofacial Tissue Regeneration. ACS APPLIED BIO MATERIALS 2022; 5:20-39. [PMID: 35014834 PMCID: PMC9016342 DOI: 10.1021/acsabm.1c00979] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Craniofacial tissue injuries, diseases, and defects, including those within bone, dental, and periodontal tissues and salivary glands, impact an estimated 1 billion patients globally. Craniofacial tissue dysfunction significantly reduces quality of life, and successful repair of damaged tissues remains a significant challenge. Blood vessels and nerves are colocalized within craniofacial tissues and act synergistically during tissue regeneration. Therefore, the success of craniofacial regenerative approaches is predicated on successful recruitment, regeneration, or integration of both vascularization and innervation. Tissue engineering strategies have been widely used to encourage vascularization and, more recently, to improve innervation through host tissue recruitment or prevascularization/innervation of engineered tissues. However, current scaffold designs and cell or growth factor delivery approaches often fail to synergistically coordinate both vascularization and innervation to orchestrate successful tissue regeneration. Additionally, tissue engineering approaches are typically investigated separately for vascularization and innervation. Since both tissues act in concert to improve craniofacial tissue regeneration outcomes, a revised approach for development of engineered materials is required. This review aims to provide an overview of neurovascularization in craniofacial tissues and strategies to target either process thus far. Finally, key design principles are described for engineering approaches that will support both vascularization and innervation for successful craniofacial tissue regeneration.
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Affiliation(s)
- Yiming Li
- Department of Biomedical Engineering, University of Rochester, Rochester, New York 14627, United States.,Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York 14642, United States
| | - David Fraser
- Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York 14642, United States.,Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, New York 14620, United States.,Translational Biomedical Sciences Program, University of Rochester Medical Center, Rochester, New York 14642, United States
| | - Jared Mereness
- Department of Biomedical Engineering, University of Rochester, Rochester, New York 14627, United States.,Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York 14642, United States.,Department of Environmental Medicine, University of Rochester Medical Center, Rochester, New York 14642, United States
| | - Amy Van Hove
- Department of Biomedical Engineering, University of Rochester, Rochester, New York 14627, United States.,Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York 14642, United States
| | - Sayantani Basu
- Department of Biomedical Engineering, University of Rochester, Rochester, New York 14627, United States.,Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York 14642, United States
| | - Maureen Newman
- Department of Biomedical Engineering, University of Rochester, Rochester, New York 14627, United States.,Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York 14642, United States
| | - Danielle S W Benoit
- Department of Biomedical Engineering, University of Rochester, Rochester, New York 14627, United States.,Department of Orthopaedics and Center for Musculoskeletal Research, University of Rochester Medical Center, Rochester, New York 14642, United States.,Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, New York 14620, United States.,Translational Biomedical Sciences Program, University of Rochester Medical Center, Rochester, New York 14642, United States.,Department of Environmental Medicine, University of Rochester Medical Center, Rochester, New York 14642, United States.,Materials Science Program, University of Rochester, Rochester, New York 14627, United States.,Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States.,Department of Biomedical Genetics and Center for Oral Biology, University of Rochester Medical Center, Rochester, New York 14642, United States
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19
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O' Sullivan E, van de Lande LS, Oosting AJC, Papaioannou A, Jeelani NO, Koudstaal MJ, Khonsari RH, Dunaway DJ, Zafeiriou S, Schievano S. The 3D skull 0-4 years: A validated, generative, statistical shape model. Bone Rep 2021; 15:101154. [PMID: 34917697 PMCID: PMC8645852 DOI: 10.1016/j.bonr.2021.101154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 11/16/2022] Open
Abstract
Background This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0-4 years old) and construct a generative statistical shape model. Methods The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population. Results The 3DMM of the paediatric skull 0-4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases. Conclusion This study presents a reliable statistical shape model of the paediatric skull 0-4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field.
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Affiliation(s)
- Eimear O' Sullivan
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Department of Computing, Imperial College London, London, UK
| | - Lara S. van de Lande
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Anne-Jet C. Oosting
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Department of Oral and Maxillofacial Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Athanasios Papaioannou
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Department of Computing, Imperial College London, London, UK
| | - N. Owase Jeelani
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | - Maarten J. Koudstaal
- Department of Oral and Maxillofacial Surgery, Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Roman H. Khonsari
- Oral and Maxillofacial Surgery Department, Hospital Necker, Enfants Malades, Paris, France
| | - David J. Dunaway
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
| | | | - Silvia Schievano
- Great Ormond Street Institute of Child Health, University College London & Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK
- Corresponding author at: The Zayad Centre for Research, 20 Guilford St, London WC1N 1DZ, UK.
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Li J, Krall M, Trummer F, Memon AR, Pepe A, Gsaxner C, Jin Y, Chen X, Deutschmann H, Zefferer U, Schäfer U, Campe GV, Egger J. MUG500+: Database of 500 high-resolution healthy human skulls and 29 craniotomy skulls and implants. Data Brief 2021; 39:107524. [PMID: 34815988 PMCID: PMC8591340 DOI: 10.1016/j.dib.2021.107524] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 10/25/2021] [Indexed: 11/19/2022] Open
Abstract
In this article, we present a skull database containing 500 healthy skulls segmented from high-resolution head computed-tomography (CT) scans and 29 defective skulls segmented from craniotomy head CTs. Each healthy skull contains the complete anatomical structures of human skulls, including the cranial bones, facial bones and other subtle structures. For each craniotomy skull, a part of the cranial bone is missing, leaving a defect on the skull. The defects have various sizes, shapes and positions, depending on the specific pathological conditions of each patient. Along with each craniotomy skull, a cranial implant, which is designed manually by an expert and can fit with the defect, is provided. Considering the large volume of the healthy skull collection, the dataset can be used to study the geometry/shape variabilities of human skulls and create a robust statistical model of the shape of human skulls, which can be used for various tasks such as cranial implant design. The craniotomy collection can serve as an evaluation set for automatic cranial implant design algorithms.
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Affiliation(s)
- Jianning Li
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
- Corresponding authors.
| | - Marcell Krall
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Florian Trummer
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Afaque Rafique Memon
- Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Antonio Pepe
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
| | - Christina Gsaxner
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Yuan Jin
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Research Center for Connected Healthcare Big Data, ZhejiangLab, Hangzhou, Zhejiang, China
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | | | - Ulrike Zefferer
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Ute Schäfer
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Gord von Campe
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
| | - Jan Egger
- Graz University of Technology (TU Graz), Graz, Styria, Austria
- Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria
- Medical University of Graz (MedUni Graz), Graz, Styria, Austria
- Corresponding authors.
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