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Superior performance of artificial intelligence-assisted preoperative planning for total knee arthroplasty in patients under 60 years: a retrospective cross-sectional comparative study
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Ke ZENG1, Shangwei YU1, Liming LIU2, Lin GUO1
Journal of Army Medical University | 2026, 48(6) : 822 - 830
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Journal of Army Medical University | 2026, 48(6): 822-830
• Intelligent Medicine and Prediction Model •
Superior performance of artificial intelligence-assisted preoperative planning for total knee arthroplasty in patients under 60 years: a retrospective cross-sectional comparative study
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Ke ZENG1, Shangwei YU1, Liming LIU2, Lin GUO1
Affiliations
  • 1Sports Medicine Center, First Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing
  • 2Department of Orthopedics, Jiangbei Branch of First Affiliated Hospital (No. 958 Hospital of Chinese PLA), Army Medical University (Third Military Medical University), Chongqing, China
Published: 2026-03-30 doi: 10.16016/j.2097-0927.202512155
Outline
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Objective

To investigate whether the accuracy of artificial intelligence (AI)-assisted preoperative planning for total knee arthroplasty (TKA) is non-inferior to computer-assisted manual preoperative planning completed collaboratively by 3D engineers and orthopedic surgeons, and to evaluate the impact of age on the accuracy of AI-based planning.

Methods

This study adopted a retrospective, cross-sectional comparative design. Data were retrospectively collected from 127 patients who underwent TKA at the First Affiliated Hospital of Army Medical University between December 2022 and September 2023. They were divided into a ≤60 years group (40 to 60 years old, n=22) and an >60 years group (61 to 80 years old, n=105) based on age. All patients underwent both AI-assisted preoperative planning (AI planning) and computer-assisted manual preoperative planning (conventional planning). Hip-knee-shaft angle (HKS), femoral resection difference, tibial resection difference, femoral rotation angle, and prosthesis size data from AI planning, conventional planning, and actual intraoperative measurements, as well as time, manpower, and economic costs of AI planning and conventional planning were compared between the 2 planning methods. The non-inferiority margin was set as resection angle difference ≤1°, resection thickness difference ≤1 mm, and prosthesis size difference ≤1.

Results

Compared with the conventional planning, the AI planning showed a mean increase of 0.8°±1.2° in HKS (Cohen’s d=0.4, 95%CI: 0.2 to 0.7), a mean increase of 1.1±1.8 mm in femoral resection difference (Cohen’s d=0.5, 95%CI: 0.3 to 0.8), and a mean increase of 0.6±1.7 mm in tibial resection difference (Cohen’s d=0.3, 95%CI: 0.0 to 0.5). The lower limits of 95%CI for these above standardized mean differences were all greater than their corresponding negative non-inferiority margins. In comparison with the conventional planning, the AI planning in the ≤60 years group showed no significant differences in HKS, tibial resection difference, and femoral rotation angle (P>0.05), whereas in the >60 years group, only femoral resection difference of AI planning exceeded the non-inferiority margin (with a mean increase of 1.1 mm, P<0.05). For prosthesis size prediction, the AI planning achieved 73.2% femoral prosthesis and 87.4% tibial prosthesis predictions with size differences not exceeding 1, while conventional planning achieved 81.9% femoral prosthesis and 90.6% tibial prosthesis predictions with size differences not exceeding 1, with no significant difference between the 2 planning methods (P>0.05). The time consumed for AI planning was significantly shorter than that for conventional planning (27.4±4.1 vs 45.3±8.5 min, P<0.05). Moreover, AI planning required no additional labor and equipment costs, with no additional manpower or equipment costs (0 vs 3 000 Yuan/case).

Conclusion

AI-assisted preoperative planning for TKA s demonstrates non-inferior accuracy compared with conventional planning, with superior performance particularly in patients under 60 years, and significant advantages in time and economic costs.

total knee arthroplasty  /  artificial intelligence  /  preoperative planning  /  accuracy  /  lower limb alignment
曾科, 余尚委, 刘力铭, 郭林. 人工智能辅助全膝关节置换术前规划在60岁以下患者中的优势表现:一项回顾性横断面对比研究. 陆军军医大学学报, 2026 , 48 (6) : 822 -830 . DOI: 10.16016/j.2097-0927.202512155
Ke ZENG, Shangwei YU, Liming LIU, Lin GUO. Superior performance of artificial intelligence-assisted preoperative planning for total knee arthroplasty in patients under 60 years: a retrospective cross-sectional comparative study[J]. Journal of Army Medical University, 2026 , 48 (6) : 822 -830 . DOI: 10.16016/j.2097-0927.202512155
Year 2026 volume 48 Issue 6
70
30
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Article Info
doi: 10.16016/j.2097-0927.202512155
  • Receive Date:2025-12-29
  • Online Date:2026-10-23
  • Published:2026-03-30
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History
  • Received:2025-12-29
  • Revised:2026-02-01
Affiliations
    1Sports Medicine Center, First Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing
    2Department of Orthopedics, Jiangbei Branch of First Affiliated Hospital (No. 958 Hospital of Chinese PLA), Army Medical University (Third Military Medical University), Chongqing, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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