The CNN model aided the study of the clinical value hidden in the implant images
© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine..
PURPOSE: This article aims to construct a new method to evaluate radiographic image identification results based on artificial intelligence, which can complement the limited vision of researchers when studying the effect of various factors on clinical implantation outcomes.
METHODS: We constructed a convolutional neural network (CNN) model using the clinical implant radiographic images. Moreover, we used gradient-weighted class activation mapping (Grad-CAM) to obtain thermal maps to present identification differences before performing statistical analyses. Subsequently, to verify whether these differences presented by the Grad-CAM algorithm would be of value to clinical practices, we measured the bone thickness around the identified sites. Finally, we analyzed the influence of the implant type on the implantation according to the measurement results.
RESULTS: The thermal maps showed that the sites with significant differences between Straumann BL and Bicon implants as identified by the CNN model were mainly the thread and neck area. (2) The heights of the mesial, distal, buccal, and lingual bone of the Bicon implant post-op were greater than those of Straumann BL (P < 0.05). (3) Between the first and second stages of surgery, the amount of bone thickness variation at the buccal and lingual sides of the Bicon implant platform was greater than that of the Straumann BL implant (P < 0.05).
CONCLUSION: According to the results of this study, we found that the identified-neck-area of the Bicon implant was placed deeper than the Straumann BL implant, and there was more bone resorption on the buccal and lingual sides at the Bicon implant platform between the first and second stages of surgery. In summary, this study proves that using the CNN classification model can identify differences that complement our limited vision.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
Journal of applied clinical medical physics - 24(2023), 10 vom: 10. Okt., Seite e14141 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Huang, Xinxu [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 11.10.2023 Date Revised 11.10.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1002/acm2.14141 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM361538421 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM361538421 | ||
| 003 | DE-627 | ||
| 005 | 20231226085344.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1002/acm2.14141 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1205.xml |
| 035 | |a (DE-627)NLM361538421 | ||
| 035 | |a (NLM)37656066 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Huang, Xinxu |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The CNN model aided the study of the clinical value hidden in the implant images |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 11.10.2023 | ||
| 500 | |a Date Revised 11.10.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. | ||
| 520 | |a PURPOSE: This article aims to construct a new method to evaluate radiographic image identification results based on artificial intelligence, which can complement the limited vision of researchers when studying the effect of various factors on clinical implantation outcomes | ||
| 520 | |a METHODS: We constructed a convolutional neural network (CNN) model using the clinical implant radiographic images. Moreover, we used gradient-weighted class activation mapping (Grad-CAM) to obtain thermal maps to present identification differences before performing statistical analyses. Subsequently, to verify whether these differences presented by the Grad-CAM algorithm would be of value to clinical practices, we measured the bone thickness around the identified sites. Finally, we analyzed the influence of the implant type on the implantation according to the measurement results | ||
| 520 | |a RESULTS: The thermal maps showed that the sites with significant differences between Straumann BL and Bicon implants as identified by the CNN model were mainly the thread and neck area. (2) The heights of the mesial, distal, buccal, and lingual bone of the Bicon implant post-op were greater than those of Straumann BL (P < 0.05). (3) Between the first and second stages of surgery, the amount of bone thickness variation at the buccal and lingual sides of the Bicon implant platform was greater than that of the Straumann BL implant (P < 0.05) | ||
| 520 | |a CONCLUSION: According to the results of this study, we found that the identified-neck-area of the Bicon implant was placed deeper than the Straumann BL implant, and there was more bone resorption on the buccal and lingual sides at the Bicon implant platform between the first and second stages of surgery. In summary, this study proves that using the CNN classification model can identify differences that complement our limited vision | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a convolutional neural network | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a implantation | |
| 650 | 4 | |a implants | |
| 650 | 4 | |a oral implantology | |
| 650 | 7 | |a Dental Implants |2 NLM | |
| 700 | 1 | |a Chen, Xingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Xinnan |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Taoran |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of applied clinical medical physics |d 2000 |g 24(2023), 10 vom: 10. Okt., Seite e14141 |w (DE-627)NLM114960119 |x 1526-9914 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2023 |g number:10 |g day:10 |g month:10 |g pages:e14141 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1002/acm2.14141 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2023 |e 10 |b 10 |c 10 |h e14141 | ||