Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning
Efficient and reliable diagnosis of craniofacial patterns is critical to orthodontic treatment. Although machine learning (ML) is time-saving and high-precision, prior knowledge should validate its reliability. This study proposed a craniofacial ML diagnostic workflow base on a cephalometric geometric model through clinical verification. A cephalometric geometric model was established to determine the landmark location by analyzing 408 X-ray lateral cephalograms. Through geometric information and feature engineering, nine supervised ML algorithms were conducted for sagittal and vertical skeleton patterns. After dimension reduction, plane decision boundary and landmark contribution contours were depicted to demonstrate the diagnostic consistency and the consistency with clinical norms. As a result, multi-layer perceptron achieved 97.56% accuracy for sagittal, while linear support vector machine reached 90.24% for the vertical. Sagittal diagnoses showed average superiority (91.60 ± 5.43)% over the vertical (82.25 ± 6.37)%, where discriminative algorithms exhibited more steady performance (93.20 ± 3.29)% than the generative (85.98 ± 9.48)%. Further, the Kruskal-Wallis H test was carried out to explore statistical differences in diagnoses. Though sagittal patterns had no statistical difference in diagnostic accuracy, the vertical showed significance. All aspects of the tests indicated that the proposed craniofacial ML workflow was highly consistent with clinical norms and could supplement practical diagnosis. Graphical Abstract.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:61 |
|---|---|
| Enthalten in: |
Medical & biological engineering & computing - 61(2023), 12 vom: 06. Sept., Seite 3345-3361 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zhou, Yuqing [VerfasserIn] |
|---|
| Links: |
Volltext [lizenzpflichtig] |
|---|
| Themen: |
Cephalometric geometry |
|---|
| Anmerkungen: |
© International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
|---|
| doi: |
10.1007/s11517-023-02919-7 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
SPR054164818 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR054164818 | ||
| 003 | DE-627 | ||
| 005 | 20231223064631.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231223s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11517-023-02919-7 |2 doi | |
| 035 | |a (DE-627)SPR054164818 | ||
| 035 | |a (SPR)s11517-023-02919-7-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zhou, Yuqing |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a Efficient and reliable diagnosis of craniofacial patterns is critical to orthodontic treatment. Although machine learning (ML) is time-saving and high-precision, prior knowledge should validate its reliability. This study proposed a craniofacial ML diagnostic workflow base on a cephalometric geometric model through clinical verification. A cephalometric geometric model was established to determine the landmark location by analyzing 408 X-ray lateral cephalograms. Through geometric information and feature engineering, nine supervised ML algorithms were conducted for sagittal and vertical skeleton patterns. After dimension reduction, plane decision boundary and landmark contribution contours were depicted to demonstrate the diagnostic consistency and the consistency with clinical norms. As a result, multi-layer perceptron achieved 97.56% accuracy for sagittal, while linear support vector machine reached 90.24% for the vertical. Sagittal diagnoses showed average superiority (91.60 ± 5.43)% over the vertical (82.25 ± 6.37)%, where discriminative algorithms exhibited more steady performance (93.20 ± 3.29)% than the generative (85.98 ± 9.48)%. Further, the Kruskal-Wallis H test was carried out to explore statistical differences in diagnoses. Though sagittal patterns had no statistical difference in diagnostic accuracy, the vertical showed significance. All aspects of the tests indicated that the proposed craniofacial ML workflow was highly consistent with clinical norms and could supplement practical diagnosis. Graphical Abstract | ||
| 650 | 4 | |a Craniofacial pattern diagnosis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Orthodontic lateral landmark |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cephalometric geometry |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Diagnostic accuracy |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mao, Bochun |4 aut | |
| 700 | 1 | |a Zhang, Jiwu |4 aut | |
| 700 | 1 | |a Zhou, Yanheng |4 aut | |
| 700 | 1 | |a Li, Jing |4 aut | |
| 700 | 1 | |a Rong, Qiguo |0 (orcid)0000-0002-9209-730X |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Medical & biological engineering & computing |d Cham : Springer Nature, 1963 |g 61(2023), 12 vom: 06. Sept., Seite 3345-3361 |w (DE-627)SPR020494920 |w (DE-600)2052667-2 |x 1741-0444 |7 nnns |
| 773 | 1 | 8 | |g volume:61 |g year:2023 |g number:12 |g day:06 |g month:09 |g pages:3345-3361 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s11517-023-02919-7 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 61 |j 2023 |e 12 |b 06 |c 09 |h 3345-3361 | ||