The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay
Background: This study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children. Methods: The panoramic films matching the inclusion criteria were collected for the AI model training to es...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Journal of Personalized Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4426/12/7/1158 |
| _version_ | 1797445905602838528 |
|---|---|
| author | Te-Ju Wu Chia-Ling Tsai Quan-Ze Gao Yueh-Peng Chen Chang-Fu Kuo Ying-Hua Huang |
| author_facet | Te-Ju Wu Chia-Ling Tsai Quan-Ze Gao Yueh-Peng Chen Chang-Fu Kuo Ying-Hua Huang |
| author_sort | Te-Ju Wu |
| collection | DOAJ |
| description | Background: This study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children. Methods: The panoramic films matching the inclusion criteria were collected for the AI model training to establish the population-based DA standard. Subsequently, the DA of the validation dataset of the healthy children and the images of the GD children were assessed by both the conventional methods and the AI-assisted standards. The efficacy of all the studied modalities was compared by the paired sample <i>t</i>-test. Results: The AI-assisted standards can provide much more accurate chronological age (CA) predictions with mean errors of less than 0.05 years, while the traditional methods presented overestimated results in both genders. For the GD children, the convolutional neural network (CNN) revealed the delayed DA in GD children of both genders, while the machine learning models presented so only in the GD boys. Conclusion: The AI-assisted DA assessments help overcome the long-standing populational limitation observed in traditional methods. The image feature extraction of the CNN models provided the best efficacy to reveal the nature of delayed DA in GD children of both genders. |
| first_indexed | 2024-03-09T13:33:33Z |
| format | Article |
| id | doaj.art-cf4e4b84a13946d09af5486f45817ea1 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T13:33:33Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-cf4e4b84a13946d09af5486f45817ea12023-11-30T21:15:40ZengMDPI AGJournal of Personalized Medicine2075-44262022-07-01127115810.3390/jpm12071158The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth DelayTe-Ju Wu0Chia-Ling Tsai1Quan-Ze Gao2Yueh-Peng Chen3Chang-Fu Kuo4Ying-Hua Huang5Department of Craniofacial Orthodontics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833253, TaiwanDepartment of Pedodontics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833253, TaiwanCenter for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333423, TaiwanCenter for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan 333423, TaiwanDivision of Rheumatology, Allergy and Immunology, Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 333423, TaiwanDepartment of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833253, TaiwanBackground: This study aimed to reveal the efficacy of the artificial intelligence (AI)-assisted dental age (DA) assessment in identifying the characteristics of growth delay (GD) in children. Methods: The panoramic films matching the inclusion criteria were collected for the AI model training to establish the population-based DA standard. Subsequently, the DA of the validation dataset of the healthy children and the images of the GD children were assessed by both the conventional methods and the AI-assisted standards. The efficacy of all the studied modalities was compared by the paired sample <i>t</i>-test. Results: The AI-assisted standards can provide much more accurate chronological age (CA) predictions with mean errors of less than 0.05 years, while the traditional methods presented overestimated results in both genders. For the GD children, the convolutional neural network (CNN) revealed the delayed DA in GD children of both genders, while the machine learning models presented so only in the GD boys. Conclusion: The AI-assisted DA assessments help overcome the long-standing populational limitation observed in traditional methods. The image feature extraction of the CNN models provided the best efficacy to reveal the nature of delayed DA in GD children of both genders.https://www.mdpi.com/2075-4426/12/7/1158artificial intelligencechronological ageconvolutional neural networkdental ageDemirjian’s methodmachine learning |
| spellingShingle | Te-Ju Wu Chia-Ling Tsai Quan-Ze Gao Yueh-Peng Chen Chang-Fu Kuo Ying-Hua Huang The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay Journal of Personalized Medicine artificial intelligence chronological age convolutional neural network dental age Demirjian’s method machine learning |
| title | The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay |
| title_full | The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay |
| title_fullStr | The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay |
| title_full_unstemmed | The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay |
| title_short | The Application of Artificial-Intelligence-Assisted Dental Age Assessment in Children with Growth Delay |
| title_sort | application of artificial intelligence assisted dental age assessment in children with growth delay |
| topic | artificial intelligence chronological age convolutional neural network dental age Demirjian’s method machine learning |
| url | https://www.mdpi.com/2075-4426/12/7/1158 |
| work_keys_str_mv | AT tejuwu theapplicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT chialingtsai theapplicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT quanzegao theapplicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT yuehpengchen theapplicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT changfukuo theapplicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT yinghuahuang theapplicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT tejuwu applicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT chialingtsai applicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT quanzegao applicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT yuehpengchen applicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT changfukuo applicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay AT yinghuahuang applicationofartificialintelligenceassisteddentalageassessmentinchildrenwithgrowthdelay |