Automatic modeling of the knee joint based on artificial intelligence
Objective: To investigate an artificial intelligence (AI) automatic segmentation and modeling method for knee joints, aiming to improve the efficiency of knee joint modeling.
Methods: Knee CT images of 3 volunteers were randomly selected. AI automatic segmentation and manual segmentation of images and modeling were performed in Mimics software. The AI-automated modeling time was recorded. The anatomical landmarks of the distal femur and proximal tibia were selected with reference to previous literature, and the indexes related to the surgical design were calculated. Pearson correlation coefficient ( r) was used to judge the correlation of the modeling results of the two methods; the consistency of the modeling results of the two methods were analyzed by DICE coefficient.
Results: The three-dimensional model of the knee joint was successfully constructed by both automatic modeling and manual modeling. The time required for AI to reconstruct each knee model was 10.45, 9.50, and 10.20 minutes, respectively, which was shorter than the manual modeling [(64.73±17.07) minutes] in the previous literature. Pearson correlation analysis showed that there was a strong correlation between the models generated by manual and automatic segmentation ( r=0.999, P<0.001). The DICE coefficients of the 3 knee models were 0.990, 0.996, and 0.944 for the femur and 0.943, 0.978, and 0.981 for the tibia, respectively, verifying a high degree of consistency between automatic modeling and manual modeling.
Conclusion: The AI segmentation method in Mimics software can be used to quickly reconstruct a valid knee model.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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| Enthalten in: |
Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery - 37(2023), 3 vom: 15. März, Seite 348-352 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Tang, Xiaoyong [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 22.03.2023 Date Revised 23.03.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.7507/1002-1892.202212008 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 500 | |a Date Revised 23.03.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Objective: To investigate an artificial intelligence (AI) automatic segmentation and modeling method for knee joints, aiming to improve the efficiency of knee joint modeling | ||
| 520 | |a Methods: Knee CT images of 3 volunteers were randomly selected. AI automatic segmentation and manual segmentation of images and modeling were performed in Mimics software. The AI-automated modeling time was recorded. The anatomical landmarks of the distal femur and proximal tibia were selected with reference to previous literature, and the indexes related to the surgical design were calculated. Pearson correlation coefficient ( r) was used to judge the correlation of the modeling results of the two methods; the consistency of the modeling results of the two methods were analyzed by DICE coefficient | ||
| 520 | |a Results: The three-dimensional model of the knee joint was successfully constructed by both automatic modeling and manual modeling. The time required for AI to reconstruct each knee model was 10.45, 9.50, and 10.20 minutes, respectively, which was shorter than the manual modeling [(64.73±17.07) minutes] in the previous literature. Pearson correlation analysis showed that there was a strong correlation between the models generated by manual and automatic segmentation ( r=0.999, P<0.001). The DICE coefficients of the 3 knee models were 0.990, 0.996, and 0.944 for the femur and 0.943, 0.978, and 0.981 for the tibia, respectively, verifying a high degree of consistency between automatic modeling and manual modeling | ||
| 520 | |a Conclusion: The AI segmentation method in Mimics software can be used to quickly reconstruct a valid knee model | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Automatic segmentation | |
| 650 | 4 | |a DICE coefficient | |
| 650 | 4 | |a Pearson coefficient | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a knee joint | |
| 700 | 1 | |a Li, Xiaohu |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Xuelian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yuxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Anchen |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yutian |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, Yurong |e verfasserin |4 aut | |
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