A 3D reconstruction of the human jaw from a single image

Abdelrahim, Aly S.; Shalaby, Ahmed; Elhabian, Shireen; Graham, James H.; Farag, Aly A.

doi:10.1109/icip.2013.6738747

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…11 The healthcare sector in general has also seen many advancements in AI implementation 12 such as early detection of disease as well as medical image processing. 13 In dentistry, AI has been used to assist in restoration design, [14][15][16][17][18] interpretation of radiographs, 19 implant type detection, 20 root fracture detection, 21 and periodontal status diagnosis. 22,23 Treatment planning and decisionmaking are one of the most critical steps in dental treatment.…”

Section: Introductionmentioning

confidence: 99%

The use of artificial intelligence and game‐based learning in removable partial denture design: A comparative study

Mahrous

Botsko²,

Elgreatly

et al. 2023

Journal of Dental Education

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Purpose The purpose of this study was to compare student performance in removable partial denture (RPD) design during a pre‐clinical RPD course with and without using a recently developed computer software named AiDental. Additionally, student perceptions associated with the use of this software were assessed. Methods The AiDental software consists of a learning environment containing an RPD design system that automatically designs RPDs based on the user's input. The software also contains an RPD game component that compares the user's RPD Design to an automatically generated RPD ideal design. The study was conducted in two phases. In phase one, pre‐clinical second‐year dental students who participated in the study were randomly divided into two groups: The AiDental group with AiDental software access (n = 36), and the conventional group without software access (n = 37). Both groups received conventional RPD instruction and practice, however, the AiDental group had additional access to the AiDental software. After 2 weeks, both groups took a mock practical test, which was collected and graded by the principal investigator (PI). The PI was blinded from group assignment and no identifying information was used in the mock practical. In phase two, all students were granted access to the AiDental software for the remainder of the pre‐clinical course duration. At the conclusion of the course, all students were given a survey to evaluate their perceptions of the AiDental software. Descriptive statistics were calculated and analyzed. Variables related to perceptions of both the AiDental designer and game were assessed using Spearman's rank correlation test, the chi‐square test, Fisher's exact test, and the non‐parametric Wilcoxon rank‐sum test as appropriate. In addition, a thematic analysis of the responses to the optional comments section was conducted using the Braun and Clarke method. Results Phase one results showed that subjects in the AiDental group were more likely than subjects in the conventional group to receive a final grade of A or B. Phase two results showed generally favorable student perceptions towards the software, and additionally, the results showed that age was significantly negatively correlated with ease of use of the software, improving decision‐making, and critical thinking relative to RPD design choices. However, no correlation between age and using the software as a reference were noted. Conclusions The use of AiDental's automated feedback and gamification techniques in RPD education had a positive effect on student grades and it was well‐liked by students. Thus, the results suggest that AiDental has the potential to be a useful adjunct to pre‐clinical teaching.

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Section: Introductionmentioning

confidence: 99%

The use of artificial intelligence and game‐based learning in removable partial denture design: A comparative study

Mahrous

Botsko²,

Elgreatly

et al. 2023

Journal of Dental Education

View full text Add to dashboard Cite

show abstract

“…Using 3D CAD models has great advantages over conventional physical solid models, as solid models require big physical space for storage, adding financial and logistic burdens which is not the case of CAD models that are stored electronically [82]. This chapter proposes to collect resources for the jaw project in a common repository for dissemination among researchers elsewhere.…”

Section: A Introductionmentioning

confidence: 99%