A review on patient-specific facial and cranial implant design using Artificial Intelligence (AI) techniques

Memon, Afaque Rafique; Li, Jianning; Egger, Jan; Chen, Xiaojun

doi:10.1080/17434440.2021.1969914

“…However, they require a long iterative process and tend to end up in local optima. With the development of data-driven methods, MIC based on AI, like deep learning, can achieve faster and more accurate image analysis, thus it has become an important method in head & neck surgery (Memon et al 2021, Wang and Li 2021c.…”

mentioning

confidence: 99%

A review on AI-based medical image computing in head and neck surgery

Xu¹,

Zeng²,

Egger³

et al. 2022

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Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical Image Computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, Artificial Intelligence (AI), especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and 8 references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.

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“…Due to its high precision and consistency, the developed automated landmarking method has the potential to be applied in various fields. Possible applications include objective follow-up and analysis of soft-tissue facial deformities, growth evaluation, facial asymmetry assessment, and integration in virtual planning software for 3D backward planning 23 , 24 . Considering that the proposed DiffusionNet-based approach only uses spatial features, it could be applied on 3D meshes of facial soft-tissue that are derived from imaging modalities lacking texture, such as CT, CBCT, or MRI.…”

Section: Discussionmentioning

confidence: 99%

Fully automated landmarking and facial segmentation on 3D photographs

Berends,

Bielevelt,

Schreurs

et al. 2024

Sci Rep

2

0

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Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

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“…Due to its high precision and consistency, the developed automated landmarking method has the potential to be applied in various elds. Possible applications include objective follow-up and analysis of softtissue facial deformities, growth evaluation, facial asymmetry assessment, and integration in virtual planning software for 3D backward planning 20,21 . Considering that the proposed DiffusionNet-based approach only uses spatial features, it could be applied on 3D meshes of facial soft tissue that are derived from imaging modalities lacking texture, such as CT, CBCT, or MRI.…”

Section: Discussionmentioning

confidence: 99%

Fully automated landmarking and facial segmentation on 3D photographs

Berends,

Bielevelt,

Schreurs

et al. 2023

Preprint

0

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Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.

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A review on patient-specific facial and cranial implant design using Artificial Intelligence (AI) techniques

Cited by 17 publications

References 54 publications

A review on AI-based medical image computing in head and neck surgery

A review on AI-based medical image computing in head and neck surgery

Fully automated landmarking and facial segmentation on 3D photographs

Fully automated landmarking and facial segmentation on 3D photographs

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