Academic Journal
Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images.
| Title: | Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images. |
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| Authors: | Türkün, Argun Ege, Kalender, Müslim Ege, Kurt, Murat, Doğan, Servet |
| Source: | Diagnostics (2075-4418); May2026, Vol. 16 Issue 10, p1464, 19p |
| Subject Terms: | Convolutional neural networks, Orthodontics, Deep learning, Annotations, Statistical accuracy, Statistical reliability |
| Abstract: | Background/Objectives: The aim of this study was to evaluate and compare the accuracy, reliability, and time efficiency of a convolutional neural network (CNN)-based deep learning model with manual annotation in the identification of soft tissue landmarks on two-dimensional (2D) facial images for orthodontic applications. Materials and Methods: Three-dimensional (3D) facial scans were obtained from 100 participants (50 females, 50 males) aged 18–25 years using the Revopoint Pop2 3D Scanner. Frontal and profile 2D images were extracted from the 3D models. Manual landmark identification was performed by a single investigator using LabelMe software, marking 22 landmarks on frontal images and 15 landmarks on profile images. A novel CNN model was developed and trained on these manually annotated images. The model's automatic landmark identifications were compared with manual annotations in terms of positional error, identification time, and reproducibility. Results: The CNN model achieved a mean localization accuracy of 96.07%. The mean prediction error ranged from 2.3% to 4.5% across various anatomical points. Trichion, Menton, and Gonion points exhibited relatively higher error rates. The model significantly reduced the annotation time compared to manual identification (manual method: 237 s per image). Intra-observer reliability analysis demonstrated excellent agreement for manual landmarking (ICC: 0.85–0.95). The AI model provided consistent predictions for identical inputs. Conclusions: The deep learning-based model demonstrated comparable accuracy to manual landmark identification while significantly improving the annotation speed and reproducibility. These results suggest that CNN-based systems offer a promising alternative for clinical orthodontic analysis and digital workflow integration. [ABSTRACT FROM AUTHOR] |
| Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20754418&ISBN=&volume=16&issue=10&date=20260515&spage=1464&pages=1464-1482&title=Diagnostics (2075-4418)&atitle=Clinical%20Equivalence%20of%20a%20CNN-Based%20Automated%20Soft%20Tissue%20Landmark%20Detection%20System%20on%202D%20Facial%20Images.&aulast=T%C3%BCrk%C3%BCn%2C%20Argun%20Ege&id=DOI:10.3390/diagnostics16101464 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Türkün%2C+Argun+Ege%22">Türkün, Argun Ege</searchLink><br /><searchLink fieldCode="AR" term="%22Kalender%2C+Müslim+Ege%22">Kalender, Müslim Ege</searchLink><br /><searchLink fieldCode="AR" term="%22Kurt%2C+Murat%22">Kurt, Murat</searchLink><br /><searchLink fieldCode="AR" term="%22Doğan%2C+Servet%22">Doğan, Servet</searchLink> – Name: TitleSource Label: Source Group: Src Data: Diagnostics (2075-4418); May2026, Vol. 16 Issue 10, p1464, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Orthodontics%22">Orthodontics</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Annotations%22">Annotations</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background/Objectives: The aim of this study was to evaluate and compare the accuracy, reliability, and time efficiency of a convolutional neural network (CNN)-based deep learning model with manual annotation in the identification of soft tissue landmarks on two-dimensional (2D) facial images for orthodontic applications. Materials and Methods: Three-dimensional (3D) facial scans were obtained from 100 participants (50 females, 50 males) aged 18–25 years using the Revopoint Pop2 3D Scanner. Frontal and profile 2D images were extracted from the 3D models. Manual landmark identification was performed by a single investigator using LabelMe software, marking 22 landmarks on frontal images and 15 landmarks on profile images. A novel CNN model was developed and trained on these manually annotated images. The model's automatic landmark identifications were compared with manual annotations in terms of positional error, identification time, and reproducibility. Results: The CNN model achieved a mean localization accuracy of 96.07%. The mean prediction error ranged from 2.3% to 4.5% across various anatomical points. Trichion, Menton, and Gonion points exhibited relatively higher error rates. The model significantly reduced the annotation time compared to manual identification (manual method: 237 s per image). Intra-observer reliability analysis demonstrated excellent agreement for manual landmarking (ICC: 0.85–0.95). The AI model provided consistent predictions for identical inputs. Conclusions: The deep learning-based model demonstrated comparable accuracy to manual landmark identification while significantly improving the annotation speed and reproducibility. These results suggest that CNN-based systems offer a promising alternative for clinical orthodontic analysis and digital workflow integration. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/diagnostics16101464 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1464 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Orthodontics Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Annotations Type: general – SubjectFull: Statistical accuracy Type: general – SubjectFull: Statistical reliability Type: general Titles: – TitleFull: Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Türkün, Argun Ege – PersonEntity: Name: NameFull: Kalender, Müslim Ege – PersonEntity: Name: NameFull: Kurt, Murat – PersonEntity: Name: NameFull: Doğan, Servet IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20754418 Numbering: – Type: volume Value: 16 – Type: issue Value: 10 Titles: – TitleFull: Diagnostics (2075-4418) Type: main |
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