Heller, Marie Theres and Maderbacher, Guenther and Schuster, Marie Farina and Forchhammer, Lina and Scharf, Markus and Renkawitz, Tobias and Pagano, Stefano (2025) Comparison of an AI-driven planning tool and manual radiographic measurements in total knee arthroplasty. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 28. pp. 148-155. ISSN 2001-0370
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Background: Accurate preoperative planning in total knee arthroplasty (TKA) is essential. Traditional manual radiographic planning can be time-consuming and potentially prone to inaccuracies. This study investigates the performance of an AI-based radiographic planning tool in comparison with manual measurements in patients undergoing total knee arthroplasty, using a retrospective observational design to assess reliability and efficiency. Methods: We retrospectively compared the Autoplan tool integrated within the mediCAD software (mediCAD Hectec GmbH, Altdorf, Germany), routinely implemented in our institutional workflow, to manual measurements performed by two orthopedic specialists on pre- and postoperative radiographs of 100 patients who underwent elective TKA. The following parameters were measured: leg length, mechanical axis deviation (MAD), mechanical lateral proximal femoral angle (mLPFA), anatomical mechanical angle (AMA), mechanical lateral distal femoral angle (mLDFA), joint line convergence angle (JLCA), mechanical medial proximal tibial angle (mMPTA), and mechanical tibiofemoral angle (mTFA). Intraclass correlation coefficients (ICCs) were calculated to assess measurement reliability, and the time required for each method was recorded. Results: The Autoplan tool demonstrated high reliability (ICC > 0.90) compared with manual measurements for linear parameters (e.g., leg length and MAD). However, the angular measurements of mLPFA, JLCA, and AMA exhibited poor reliability (ICC < 0.50) among all raters. The Autoplan tool significantly reduced the time required for measurements compared to manual measurements, with a mean time saving of 44.3 seconds per case (95 % CI: 43.5-45.1 seconds, p < 0.001). Conclusion: AI-assisted tools like the Autoplan tool in mediCAD offer substantial time savings and demonstrate reliable measurements for certain linear parameters in preoperative TKA planning. However, the observed low reliability in some measurements, even amongst experienced human raters, suggests inherent challenges in the radiographic assessment of angular parameters. Further development is needed to improve the accuracy of automated angular measurements, and to address the inherent variability in their assessment.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | RELIABILITY; Total Knee Arthroplasty; Artificial Intelligence; Automated Planning; Radiographic Measurement; Intraclass Correlation Coefficient; Measurement Efficiency |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Orthopädie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 06 May 2026 07:47 |
| Last Modified: | 06 May 2026 07:47 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66601 |
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