ReviewDento maxillo facial radiology2022
A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery.
Review in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06967090 (Accuracy of Guided Implant Placement Using Artificial Intelligence Segmentation Versus Conventional Technique), which is not on this map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Accuracy of Guided Implant Placement Using Artificial Intelligence Segmentation Versus Conventional Technique: A Randomized Clinical Trial
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in the diagnosis of maxillary sinus diseases: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Artificial intelligence in refractive surgery: progress, challenges, and future directions.Frontiers in cell and developmental biology · 2026Review
- Efficiency of Artificial Intelligence in Three-Dimensional Reconstruction of Medical Imaging.Cureus · 2025Review
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Automatic Segmentation of the Infraorbital Canal in CBCT Images: Anatomical Structure Recognition Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Tissue-specified reconstruction modeling of the head and neck structure and its application in simulating airway obstruction.Heliyon · 2025Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Error detection using a multi-channel hybrid network with a low-resolution detector in patient-specific quality assurance.Journal of applied clinical medical physics · 2024Article
- Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation.Journal of personalized medicine · 2024Article
- Skeletal Fracture Detection with Deep Learning: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- [Pediatric oral maxillofacial management and artificial intelligence].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2023Review
- ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model.International journal of oral science · 2023Review
- Training End-to-End Unrolled Iterative Neural Networks for SPECT Image Reconstruction.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning.Frontiers in bioengineering and biotechnology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxillofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and promising research avenues are discussed.
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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.