A craniofacial statistical shape model for the virtual reconstruction of bilateral maxillary defects.

Zhou, Kevin Xiang; Patel, M.; Shimizu, M.; Thang, T.

doi:10.1016/j.ijom.2022.03.027

Cited by 2 publications

(2 citation statements)

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“…To determine the accuracy, the 95th percentile Hausdorff distance, volumetric dice coefficient, anterior nasal spine deviation and midfacial projection deviation were used. Although the results are promising in terms of virtually reconstructing the missing maxilla, the overall dice coefficient only reached a maximum of 68% (74). In another previous study, a deep-learning-based automatic facial bone segmentation model using a CNN (named U-Net) was applied, and its accuracy was also measured using the dice coefficient.…”

Section: Discussionmentioning

confidence: 98%

Applications and challenges of neural networks in otolaryngology (Review)

Taciuc,

Dumitru,

Vrinceanu

et al. 2024

Biomed Rep

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Artificial Intelligence (AI) has become a topic of interest that is frequently debated in all research fields. The medical field is no exception, where several unanswered questions remain. When and how this field can benefit from AI support in daily routines are the most frequently asked questions. The present review aims to present the types of neural networks (NNs) available for development, discussing their advantages, disadvantages and how they can be applied practically. In addition, the present review summarizes how NNs (combined with various other features) have already been applied in studies in the ear nose throat research field, from assisting diagnosis to treatment management. Although the answer to this question regarding AI remains elusive, understanding the basics and types of applicable NNs can lead to future studies possibly using more than one type of NN. This approach may bypass the actual limitations in accuracy and relevance of information generated by AI. The proposed studies, the majority of which used convolutional NNs, obtained accuracies varying 70-98%, with a number of studies having the AI trained on a limited number of cases (<100 patients). The lack of standardization in AI protocols for research negatively affects data homogeneity and transparency of databases.

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Section: Discussionmentioning

confidence: 98%

Applications and challenges of neural networks in otolaryngology (Review)

Taciuc,

Dumitru,

Vrinceanu

et al. 2024

Biomed Rep

View full text Add to dashboard Cite

show abstract

“…These include point-to-point distances, bone segment lengths, and angles, whose inaccuracies can lead to misalignment in the reconstructed mandible (7,10). The Hausdorff distance offers a nuanced three-dimensional assessment that captures these small deviations, providing more reliable and precise results (30)(31)(32). Similarly, the Dice coefficient provides a robust, scalable metric that is resilient to shape and size variations (33,34).…”

Section: Discussionmentioning

confidence: 99%

Realizing in-house algorithm-driven free fibula flap set up within 24 hours: a pilot study evaluating accuracy with open-source tools

Vollmer,

Saravi,

Breitenbuecher

et al. 2023

Front. Surg.

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ObjectiveThis study aims to critically evaluate the effectiveness and accuracy of a time safing and cost-efficient open-source algorithm for in-house planning of mandibular reconstructions using the free osteocutaneous fibula graft. The evaluation focuses on quantifying anatomical accuracy and assessing the impact on ischemia time.MethodsA pilot study was conducted, including patients who underwent in-house planned computer-aided design and manufacturing (CAD/CAM) of free fibula flaps between 2021 and 2023. Out of all patient cases, we included all with postoperative 3D imaging in the study. The study utilized open-source software tools for the planning step, and three-dimensional (3D) printing techniques. The Hausdorff distance and Dice coefficient metrics were used to evaluate the accuracy of the planning procedure.ResultsThe study assessed eight patients (five males and three females, mean age 61.75 ± 3.69 years) with different diagnoses such as osteoradionecrosis and oral squamous cell carcinoma. The average ischemia time was 68.38 ± 27.95 min. For the evaluation of preoperative planning vs. the postoperative outcome, the mean Hausdorff Distance was 1.22 ± 0.40. The Dice Coefficients yielded a mean of 0.77 ± 0.07, suggesting a satisfactory concordance between the planned and postoperative states. Dice Coefficient and Hausdorff Distance revealed significant correlations with ischemia time (Spearman's rho = −0.810, p = 0.015 and Spearman's rho = 0.762, p = 0.028, respectively). Linear regression models adjusting for disease type further substantiated these findings.ConclusionsThe in-house planning algorithm not only achieved high anatomical accuracy, as reflected by the Dice Coefficients and Hausdorff Distance metrics, but this accuracy also exhibited a significant correlation with reduced ischemia time. This underlines the critical role of meticulous planning in surgical outcomes. Additionally, the algorithm's open-source nature renders it cost-efficient, easy to learn, and broadly applicable, offering promising avenues for enhancing both healthcare affordability and accessibility.

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A craniofacial statistical shape model for the virtual reconstruction of bilateral maxillary defects.

Cited by 2 publications

References 0 publications

Applications and challenges of neural networks in otolaryngology (Review)

Applications and challenges of neural networks in otolaryngology (Review)

Realizing in-house algorithm-driven free fibula flap set up within 24 hours: a pilot study evaluating accuracy with open-source tools

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