Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer-aided surgical simulation for orthognathic surgery

Deng, Hannah H.; Liu, Q; Chen, A; Kuang, Tianshu; Yuan, Peng; Gateño, Jaime; Kim, D; Barber, Joshua C.; Xiong, K G; Yu, P; Gu, K J; Xu, Xintao; Yan, Pingkun; Shen, Dinggang; Xia, James J.

doi:10.1016/j.ijom.2022.10.010

Cited by 12 publications

(10 citation statements)

References 15 publications

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“… Liu et al (2021) introduced a coarse-to-fine 3D U-Net-based framework, named SkullEngine, which was designed for high-resolution segmentation and large-scale landmark detection in the skull. Deng et al (2023) studied 61 patients from the digital archive of the Department of Oral and Maxillofacial Surgery at Houston Methodist Hospital between January 2021 and December 2021 and independently validated the segmentation accuracy of SkullEngine for teeth (Dice score for upper jaw teeth: 0.97; Dice score for lower jaw teeth: 0.96).…”

Section: Resultsmentioning

confidence: 99%

“…nnU-Net generates three U-Net configurations: a 2D U-Net, a full-resolution 3D U-Net, and a cascaded 3D U-Net ( Isensee et al, 2021 ). In comparison with those of Deng et al (2023) , the patients included in their study by Dot et al (2022) presented diverse anatomical deformities and had undergone orthognathic surgery. Dot et al (2022) demonstrated that nnU-Net performs well on CBCT images of such patients (Dice score for upper jaw teeth: 0.95; Dice score for lower jaw teeth: 0.94).…”

Section: Resultsmentioning

confidence: 99%

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Semi or fully automatic tooth segmentation in CBCT images: a review

Zheng,

Gao,

Zhou

et al. 2024

PeerJ Computer Science

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Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Semi or fully automatic tooth segmentation in CBCT images: a review

Zheng,

Gao,

Zhou

et al. 2024

PeerJ Computer Science

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“…After excluding 379 duplicates, 321 articles were selected for title and abstract review, resulting in 17 articles for full-text review ( Figure 1 ). Of the 17 articles, 4 studies were excluded, because their sample was less than 100 [ 21 , 22 , 23 , 24 ], and 5 studies were excluded, because their objectives did not include the facial diagnosis applied for orthognathic surgery using artificial intelligence, ultimately including 8 studies for descriptive and methodological analysis.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

Ravelo,

Acero,

Fuentes-Zambrano

et al. 2024

JPM

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AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to evaluate the capacity and training of models for diagnosis of dentofacial deformities in class II and class III patients using artificial intelligence and the potential use for indicating orthognathic surgery. The search strategy is from 1943 to April 2024 in PubMed, Embase, Scopus, Lilacs, and Web of Science. Studies that used imaging to assess anatomical structures, airway volume, and craniofacial positions using the AI algorithm in the human population were included. The methodological quality of the studies was assessed using the Effective Public Health Practice Project instrument. The systematic search identified 697 articles. Eight studies were obtained for descriptive analysis after exclusion according to our inclusion and exclusion criteria. All studies were retrospective in design. A total of 5552 subjects with an age range between 14.7 and 56 years were obtained; 2474 (44.56%) subjects were male, and 3078 (55.43%) were female. Six studies were analyzed using 2D imaging and obtained highly accurate results in diagnosing skeletal features and determining the need for orthognathic surgery, and two studies used 3D imaging for measurement and diagnosis. Limitations of the studies such as age, diagnosis in facial deformity, and the included variables were observed. Concerning the overall analysis bias, six studies were at moderate risk due to weak study designs, while two were at high risk of bias. We can conclude that, with the few articles included, using AI-based software allows for some craniometric recognition and measurements to determine the diagnosis of facial deformities using mainly 2D analysis. However, it is necessary to perform studies based on three-dimensional images, increase the sample size, and train models in different populations to ensure accuracy of AI applications in this field. After that, the models can be trained for dentofacial diagnosis

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“…Additionally, an article published in 2021 by Mascarehas et al reports on the cost-effective nature of in-house CASP and surgical splint printing for orthognathic surgery. Based on their analysis of 35 consecutive patients undergoing single jaw surgery, the total time needed from initial scanning to splint manufacturing took 5-9 h and with substantially reduced direct costs [64]. The authors remark, however, that the initial investment can be quite steep, as an institution would be required to purchase a high-power computer, intra-oral or model scanner, a 3D printer, and printing material.…”

Section: In-house Computer-assisted Surgical Planningmentioning

confidence: 99%

Current Trends in Orthognathic Surgery

Zammit,

Ettinger,

Sanati-Mehrizy

et al. 2023

Medicina

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Orthognathic surgery has evolved significantly over the past century. Osteotomies of the midface and mandible are contemporaneously used to perform independent or coordinated movements to address functional and aesthetic problems. Specific advances in the past twenty years include increasing fidelity with computer-assisted planning, the use of patient-specific fixation, expanding indications for management of upper airway obstruction, and shifts in orthodontic-surgical paradigms. This review article serves to highlight the contemporary practice of orthognathic surgery.

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Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer-aided surgical simulation for orthognathic surgery

Cited by 12 publications

References 15 publications

Semi or fully automatic tooth segmentation in CBCT images: a review

Semi or fully automatic tooth segmentation in CBCT images: a review

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

Current Trends in Orthognathic Surgery

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