Zusammenfassung
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 ...
Zusammenfassung
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.
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